Author: "Olivier, Colliot" / Topic: business.industry - Searchworks@Jio Institute Digital Library Search Results

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117 results on '"Olivier, Colliot"'

1. Learning the spatiotemporal variability in longitudinal shape data sets

Author: Stanley Durrleman, Alexandre Bône, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This work has been partly funded by the European Research Council with grant 678304, European Union’s Horizon 2020 research and innovation program with grant 666992, and the program Investissements d’avenir ANR-10-IAIHU-06.Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bio-engineering, and through generous contributions from the following: AbbVie, Alzheimers Association, Alzheimers Drug Discovery Foundation, Araclon Biotech, BioClinica, Inc., Biogen, Bristol-Myers Squibb Company, CereSpir, Inc., Cogstate, Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, EuroImmun, F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc., Fujirebio, GE Healthcare, IXICO Ltd., Janssen Alzheimer Immunotherapy Research & Development, LLC., Johnson & Johnson Pharmaceutical Research & Development LLC., Lumosity, Lundbeck, Merck & Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimers Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California., Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-05-PADD-0003,TRANS,Transformations de l'élevage et dynamiques des espaces(2005), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Bône, Alexandre, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Programme fédérateur Agriculture et Développement Durable - Transformations de l'élevage et dynamiques des espaces - - TRANS2005 - ANR-05-PADD-0003 - ADD - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Disease progression modeling, Large deformation diffeomorphic metric mapping, Computer science, Statistical shape analysis, 02 engineering and technology, Stochastic approximation, Computational morphometry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Set (psychology), [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS], Longitudinal data, business.industry, Disease progression modelin, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Statistical model, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS], [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], Pattern recognition (psychology), Trajectory, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 020201 artificial intelligence & image processing, Medical imaging, Computer Vision and Pattern Recognition, Noise (video), Artificial intelligence, [MATH.MATH-DG] Mathematics [math]/Differential Geometry [math.DG], business, Software
Abstract: International audience; In this paper, we propose a generative statistical model to learn the spatiotemporal variability in longitudinal shape data sets, which contain repeated observations of a set of objects or individuals over time. From all the short-term sequences of individual data, the method estimates a long-term normative scenario of shape changes and a tubular coordinate system around this trajectory. Each individual data sequence is therefore (i) mapped onto a specific portion of the trajectory accounting for differences in pace of progression across individuals, and (ii) shifted in the shape space to account for intrinsic shape differences across individuals that are independent of the progression of the observed process. The parameters of the model are estimated using a stochastic approximation of the expectation–maximization algorithm. The proposed approach is validated on a simulated data set, illustrated on the analysis of facial expression in video sequences, and applied to the modeling of the progressive atrophy of the hippocampus in Alzheimer’s disease patients. These experiments show that one can use the method to reconstruct data at the precision of the noise, to highlight significant factors that may modulate the progression, and to simulate entirely synthetic longitudinal data sets reproducing the variability of the observed process.
Published: 2020
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2. Axial multi-layer perceptron architecture for automatic segmentation of choroid plexus in multiple sclerosis

Author: Mariem Hamzaoui, Arya Yazdan Panah, Vito A. G. Ricigliano, Annalisa Colombi, Marius Schmidt-Mengin, Emanuele Morena, Olivier Colliot, Benedetta Bodini, and Bruno Stankoff
Subjects: business.industry, Computer science, Multiple sclerosis, Deep learning, Pattern recognition, medicine.disease, Perceptron, Convolutional neural network, Cerebrospinal fluid, Multilayer perceptron, medicine, Choroid plexus, Segmentation, Artificial intelligence, business
Abstract: Choroid plexuses (CP) are structures of the brain ventricles which produce most of the cerebrospinal fluid (CSF). Several postmortem and in vivo studies have pointed towards their role in the inflammatory processes in multiple sclerosis (MS). Automatic segmentation of CP from MRI thus has high value for studying their characteristics in large cohorts of patients. To the best of our knowledge, the only freely available tool for CP segmentation is FreeSurfer but its accuracy for this specific structure is poor. In this paper, we propose to automatically segment CP from non-contrast enhanced T1-weighted MRI. To that end, we introduce a new model called “Axial-MLP” based on an assembly of Axial multi-layer perceptrons (MLPs). This is inspired by recent works which showed that the self-attention layers of Transformers can be replaced with MLPs. This approach is systematically compared with a standard 3D U-Net, nnU-Net, Freesurfer and FastSurfer. For our experiments, we make use of a dataset of 141 subjects (44 controls and 97 patients with MS). We show that all the tested deep learning (DL) methods outperform FreeSurfer (Dice around 0.7 for DL vs 0.33 for FreeSurfer). Axial-MLP is competitive with U-Nets even though it is slightly less accurate. The conclusions of our paper are two-fold: 1) the studied deep learning methods could be useful tools to study CP in large cohorts of MS patients; 2) Axial-MLP is a potentially viable alternative to convolutional neural networks for such tasks, although it could benefit from further improvements.
Published: 2021
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3. SLITRK2, an X-linked modifier of the age at onset in C9orf72 frontotemporal lobar degeneration

Author: Barbier, Mathieu, Camuzat, Agnès, Hachimi, Khalid El, Guegan, Justine, Rinaldi, Daisy, Lattante, Serena, Houot, Marion, Sánchez-Valle, Raquel, Sabatelli, Mario, Antonell, Anna, Molina-Porcel, Laura, Clot, Fabienne, Couratier, Philippe, van der Ende, Emma, van der Zee, Julie, Manzoni, Claudia, Camu, William, Cazeneuve, Cécile, Sellal, François, Didic, Mira, Golfier, Véronique, Pasquier, Florence, Duyckaerts, Charles, Rossi, Giacomina, Bruni, Amalia C, Alvarez, Victoria, Gómez-Tortosa, Estrella, de Mendonça, Alexandre, Graff, Caroline, Masellis, Mario, Nacmias, Benedetta, Oumoussa, Badreddine Mohand, Jornea, Ludmila, Forlani, Sylvie, Van Deerlin, Viviana, Rohrer, Jonathan D, Gelpi, Ellen, Rademakers, Rosa, Van Swieten, John, Le Guern, Eric, Van Broeckhoven, Christine, Ferrari, Raffaele, Génin, Emmanuelle, Brice, Alexis, Ber, Le, Isabelle Alexis Brice, Sophie, Auriacombe, Serge, Belliard, Anne, Bertrand, Anne, Bissery, Fre ́ de, ́ ric Blanc, Marie-Paule, Boncoeur, Ste, ́ phanie Bombois, Claire Boutoleau-Bretonnie` re, Agne`, s Camuzat, Mathieu, Ceccaldi, Marie, Chupin, Philippe, Couratier, Olivier, Colliot, Vincent, Deramecourt, Mira, Didic, Bruno, Dubois, Charles, Duyckaerts, Fre ́ de, ́ rique Etcharry-Bouyx, Aure, ́ lie Guignebert-Funkiewiez, Maı ̈te, ́ Formaglio, ́ ronique Golfier, Ve, Marie-Odile, Habert, Didier, Hannequin, Lucette, Lacomblez, Julien, Lagarde, ́ raldine Lautrette, Ge, Isabelle Le Ber, Benjamin Le Toullec, Richard, Levy, Marie-Anne, Mackowiak, Bernard-Franc ̧ois Michel, Florence, Pasquier, Thibaud, Lebouvier, Carole Roue, ́ -Jagot, Christel Thauvin- Robinet, Catherine, Thomas-Anterion, Je ́ re, ́ mie Pariente, Franc ̧ois Salachas, Sabrina, Sayah, Franc ̧ois Sellal, Assi-Herve, ́ Oya, Daisy, Rinaldi, Adeline, Rollin-Sillaire, Martine, Vercelletto, David, Wallon, Armelle, Rametti-Lacroux, Raffaele, Ferrari, Hernandez, Dena G., Nalls, Michael A., Rohrer, Jonathan D., Adaikalavan, Ramasamy, Kwok, John B. J., Carol Dobson- Stone, Brooks, William S., Schofield, Peter R., Halliday, Glenda M., Hodges, John R., Olivier, Piguet, Lauren, Bartley, Elizabeth, Thompson, Isabel Herna, ́ ndez, Agustı ́n Ruiz, Merce`, Boada, Barbara, Borroni, Alessandro, Padovani, Carlos, Cruchaga, Cairns, Nigel J., Luisa, Benussi, Giuliano, Binetti, Roberta, Ghidoni, Gianluigi, Forloni, Diego, Albani, Daniela, Galimberti, Chiara, Fenoglio, Maria, Serpente, Elio, Scarpini, ́ n, Jordi Clarimo, Alberto Lleo, ́, Rafael, Blesa, Maria Landqvist Waldo, ̈, Karin, Nilsson, Christer, Nilsson, Mackenzie, Ian R. A., Hsiung, Ging-Yuek R., Mann, David M. A., Jordan, Grafman, Morris, Christopher M., Johannes, Attems, Griffiths, Timothy D., Mckeith, Ian G., Thomas, Alan J., Pietro, Pietrini, Edward, Uey, Wassermann, Eric M., Atik, Baborie, Evelyn, Jaros, Tierney, Michael C., Pau, Pastor, Cristina, Razquin, Sara, Ortega-Cubero, Elena, Alonso, Robert, Perneczky, Janine, Diehl-Schmid, Panagiotis, Alexopoulos, Alexander, Kurz, Rainero, Innocenzo, Rubino, Elisa, Pinessi, Lorenzo, Ekaterina, Rogaeva, Peter St George-Hyslop, Giacomina, Rossi, Fabrizio, Tagliavini, Giorgio, Giaccone, Rowe, James B., Schlachetzki, Johannes C. M., James, Uphill, John, Collinge, Simon, Mead, Adrian, Danek, Van Deerlin, Vivianna M., Murray, Grossman, Trojanowski, John Q., Julie van der Zee, Christine Van Broeckhoven, Cappa, Stefano F., Isabelle, Leber, Alexis, Brice, Benedetta, Nacmias, Sandro, Sorbi, Silvia, Bagnoli, Irene, Piaceri, Nielsen, Jørgen E., Hjermind, Lena E., Matthias, Riemenschneider, Manuel, Mayhaus, Bernd, Ibach, Gilles, Gasparoni, Sabrina, Pichler, Wei, Gu, Rossor, Martin N., Fox, Nick C., Warren, Jason D., Maria Grazia Spillantini, Morris, Huw R., Patrizia, Rizzu, Peter, Heutink, Snowden, Julie S., Sara, Rollinson, Anna, Richardson, Alexander, Gerhard, Bruni, Amalia C., Raffaele, Maletta, Francesca, Frangipane, Chiara, Cupidi, Livia, Bernardi, Maria, Anfossi, Maura, Gallo, Maria Elena Conidi, Nicoletta, Smirne, Rosa, Rademakers, Matt, Baker, Dickson, Dennis W., Graff-Radford, Neill R., Petersen, Ronald C., David, Knopman, Josephs, Keith A., Boeve, Bradley F., Parisi, Joseph E., Seeley, William W., Miller, Bruce L., Karydas, Anna M., Howard, Rosen, van Swieten, John C., Dopper, Elise G. P., Harro, Seelaar, Pijnenburg, Yolande A. L., Philip, Scheltens, Giancarlo, Logroscino, Rosa, Capozzo, Valeria, Novelli, Puca, Annibale A., Massimo, Franceschi, Alfredo, Postiglione, Graziella, Milan, Paolo, Sorrentino, Mark, Kristiansen, Huei-Hsin, Chiang, Caroline, Graff, Adeline, Rollin, Dimitrios, Kapogiannis, Luigi, Ferrucci, Stuart, Pickering-Brown, Singleton, Andrew B., John, Hardy, Parastoo, Momeni., Neurology, Amsterdam Neuroscience - Neurodegeneration, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Università cattolica del Sacro Cuore = Catholic University of the Sacred Heart [Roma] (Unicatt), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona (UB), Centre d'investigation clinique Paris Est [CHU Pitié Salpêtrière] (CIC Paris-Est), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Hôpital Dupuytren [CHU Limoges], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Center for Molecular Neurology (VIB-UAntwerp), University of Antwerp (UA), University College of London [London] (UCL), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Service de Neurologie [Hôpitaux Civils de Colmar], Hôpitaux Civils Colmar, Mécanismes Centraux et Périphériques de la Neurodégénérescence, Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Neurologie, maladies neuro-musculaires [Hôpital de la Timone - APHM], Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Yves le Foll, Lille Neurosciences & Cognition - U 1172 (LilNCog), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Regional Neurogenetic Centre [Lamezia Terme, Italy] (CRN - ASP Catanzaro), Hospital Central de Asturias, Institute of Health Research of Principado de Asturias (ISPA), Fundación Jiménez Díaz, Fundacion Jimenez Diaz [Madrid] (FJD), Faculdade de Medicina [Lisboa], Universidade de Lisboa = University of Lisbon (ULISBOA), Karolinska University Hospital [Stockholm], Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, Università degli Studi di Firenze = University of Florence (UniFI), Fondazione Don Carlo Gnocchi, Plateforme Post-génomique de la Pitié-Salpêtrière (PASS-P3S), Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Hospital of the University of Pennsylvania (HUP), Perelman School of Medicine, University of Pennsylvania-University of Pennsylvania, Neurodegenerative Brain Diseases group, Department of Molecular Genetics, VIB, Antwerpen, Belgium, Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), EFS-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), The French clinical and genetic Research network on FTLD/FTLD-ALS and PREVDEMALS, The International Frontotemporal Dementia Genomics Consortium, The European Early Onset Dementia (EU -EOD) Consortium, Brainbank Neuro-CEB Neuropathology Network, and Neurological Tissue Bank of the Biobank Hospital Clinic-IDIBAPS
Subjects: Adult, Male, TDP-43, C9orf72, SLITRK2, amyotrophic lateral sclerosis, frontotemporal dementia, Nerve Tissue Proteins, Settore MED/03 - GENETICA MEDICA, Polymorphism, Single Nucleotide, Cohort Studies, Genes, X-Linked, 80 and over, Medicine, Dementia, Humans, Allele, Age of Onset, Polymorphism, Aged, Aged, 80 and over, biology, C9orf72 Protein, business.industry, Membrane Proteins, MESH: Frontotemporal Lobar Degeneration / epidemiology, Frontotemporal Lobar, Degeneration / genetics, Genes, X-Linked / genetics, Genome-Wide Association Study / methods, Frontotemporal lobar degeneration, Single Nucleotide, Middle Aged, X-Linked, medicine.disease, Amyotrophic lateral sclerosis, Minor allele frequency, Genes, Immunology, Synaptophysin, biology.protein, Female, MESH: Adult, C9orf72 Protein / genetics, Frontotemporal Lobar Degeneration / diagnosis, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Human medicine, Neurology (clinical), MESH: Humans, Membrane Proteins / genetics, Nerve Tissue Proteins / genetics, Polymorphism, Single Nucleotide / genetics, Age of onset, Frontotemporal Lobar Degeneration, business, Frontotemporal dementia, Genome-Wide Association Study
Abstract: The G4C2-repeat expansion in C9orf72 is the most common cause of frontotemporal dementia and of amyotrophic lateral sclerosis. The variability of age at onset and phenotypic presentations is a hallmark of C9orf72 disease. In this study, we aimed to identify modifying factors of disease onset in C9orf72 carriers using a family-based approach, in pairs of C9orf72 carrier relatives with concordant or discordant age at onset. Linkage and association analyses provided converging evidence for a locus on chromosome Xq27.3. The minor allele A of rs1009776 was associated with an earlier onset (P = 1 × 10−5). The association with onset of dementia was replicated in an independent cohort of unrelated C9orf72 patients (P = 0.009). The protective major allele delayed the onset of dementia from 5 to 13 years on average depending on the cohort considered. The same trend was observed in an independent cohort of C9orf72 patients with extreme deviation of the age at onset (P = 0.055). No association of rs1009776 was detected in GRN patients, suggesting that the effect of rs1009776 was restricted to the onset of dementia due to C9orf72. The minor allele A is associated with a higher SLITRK2 expression based on both expression quantitative trait loci (eQTL) databases and in-house expression studies performed on C9orf72 brain tissues. SLITRK2 encodes for a post-synaptic adhesion protein. We further show that synaptic vesicle glycoprotein 2 and synaptophysin, two synaptic vesicle proteins, were decreased in frontal cortex of C9orf72 patients carrying the minor allele. Upregulation of SLITRK2 might be associated with synaptic dysfunctions and drives adverse effects in C9orf72 patients that could be modulated in those carrying the protective allele. How the modulation of SLITRK2 expression affects synaptic functions and influences the disease onset of dementia in C9orf72 carriers will require further investigations. In summary, this study describes an original approach to detect modifier genes in rare diseases and reinforces rising links between C9orf72 and synaptic dysfunctions that might directly influence the occurrence of first symptoms.
Published: 2021
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4. Anomaly detection for the individual analysis of brain PET images

Author: Sebastien Ourselin, M. Jorge Cardoso, Marie-Odile Habert, Jorge Samper-González, Ninon Burgos, Stanley Durrleman, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), European Project: 609102,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,PRESTIGE(2014), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Burgos, Ninon, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, PRES Towards International Gain of Excellence - PRESTIGE - - EC:FP7:PEOPLE2014-09-01 - 2019-08-31 - 609102 - VALID, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Positron emission tomography, Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Image registration, Anomaly detection, Brain mapping, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Dementia, Radiology, Nuclear Medicine and imaging, medicine.diagnostic_test, business.industry, Pattern recognition, Image synthesis, medicine.disease, 030220 oncology & carcinogenesis, Artificial intelligence, Abnormality, business, Frontotemporal dementia
Abstract: International audience; Purpose:In clinical practice, positron emission tomography (PET) images are mostly analysed visually, but the sensitivity and specificity of this approach greatly depends on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach: We created subject-specific abnormality maps that summarise the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically-matched PET scans from a control dataset.Results: We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions: The proposed method was able to automatically locate and characterise the areas characteristic of dementia from PET images. The abnormality maps are expected to i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatio-temporal modelling.
Published: 2021
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5. Deep learning for brain disorders: from data processing to disease treatment

Author: Elina Thibeau-Sutre, Ninon Burgos, Olivier Colliot, Simona Bottani, Johann Faouzi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d'avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from the ICM Big Brain Theory Program (project PredictICD), and from the Abeona Foundation (project Brain@Scale)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Colliot, Olivier, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: medicine.medical_specialty, Neurology, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, education, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 030218 nuclear medicine & medical imaging, Environmental data, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, Quality of life (healthcare), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Medical imaging, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Precision Medicine, Molecular Biology, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Data processing, Brain Diseases, Modalities, business.industry, Deep learning, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Genomics, Precision medicine, Data science, 3. Good health, Treatment Outcome, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Disease Progression, Artificial intelligence, Smartphone, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Information Systems
Abstract: In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.
Published: 2021
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6. Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Author: Julia Sidorenko, Kathryn E. Kemper, Jian Yang, Olivier Colliot, Naomi R. Wray, Baptiste Couvy-Duchesne, Futao Zhang, Peter M. Visscher, Institute for Molecular Bioscience, University of Queensland [Brisbane], Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute for Advanced Research (IFAR), Wenzhou Medical University [Wenzhou, China] (WMU), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Couvy-Duchesne, Baptiste, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: General linear model, Mixed model, [STAT.ME] Statistics [stat]/Methodology [stat.ME], business.industry, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Pattern recognition, 02 engineering and technology, Biology, 021001 nanoscience & nanotechnology, 01 natural sciences, 010309 optics, Correlation, Neuroimaging, Sample size determination, 0103 physical sciences, False positive paradox, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], False positive rate, Artificial intelligence, 0210 nano-technology, business, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Genetic association
Abstract: International audience; Mass-univariate association analyses aim at mapping the brain regions associated with a trait/disorder. The correlation between vertices may cause a true association to spread locally (cluster of association) or distally (false positive cluster). We previously showed that controlling for all vertices in the model (using a linear mixed model: LMM), could greatly reduce the probability of false positive and improve mapping precision. Here, we investigated a new LMM called MOMENT which reduces false positive rate in methylome-wide association studies. Compared to LMM, MOMENT had enhanced power and mapping precision but failed at reducing the rates of false positives clusters.The increasing sample sizes from neuroimaging studies should allow detection of image measures are associated with phenotypic traits with smaller effect sizes, which will advance progress in the mapping of the brain regions associated with traits and diseases [1]. The UK Biobank (UKB) is one of the best example of this new generation of samples [2]. Multimodal Brain MRI collection is currently ongoing, with tens of thousands of individuals already imaged out of a target of 100,000 [2]. The large sample size, together with the breadth of phenotyping (incl. self-reports, in lab assessments, prescription and medical history), should allow new insights into the factors contributing to brain differences between older adults.
Published: 2021
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7. A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus from 7T MRI

Author: Alexandre Vignaud, Lucie Hertz-Pannier, Alexis Guyot, Claire Cury, Ana B. Graciano Fouquier, Emilie Gerardin, Claire Boutet, Alain Trouvé, Stanley Durrleman, Joan Alexis Glaunès, Johanne Germain, Marie Chupin, Olivier Colliot, Linda Marrakchi-Kacem, Thomas R. Henry, Pierre-Francois Van de Moortele, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CATI Multicenter Neuroimaging Platform (CATI), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut Supérieur de Biotechnologie de Sidi Thabet (ISBST), Université de la Manouba [Tunisie] (UMA), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of Minnesota Medical School, University of Minnesota System, Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota System-University of Minnesota System, This work was supported by ANR (HM-TC, ANR-09-EMER-006), France Alzheimer (IRMA7), by the CATI project, by Investissements d’avenirANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and ANR-10-IAIHU-06, by the Philippe foundation, and by the NSF/NIH/ANR program 'CollaborativeResearch in Computational Neuroscience' (HIPLAY7, NSF-CRCSN-1607835 and ANR-16-NEUC-0001-01)., ANR-09-EMER-0006,HM-TC,Modélisation des interactions entre l'hippocampe et le cortex dans ' la conscience temporelle', établie à partir de l'imagerie cérébrale multimodale anatomique et fonctionnelle(2009), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-16-NEUC-0001,HIPLAY7,Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks(2016), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Institut Supérieur de Biotechnologies [Sidi Thabet] (ISBST), Empenn, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Centre de Mathématiques et de Leurs Applications (CMLA), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), This work was supported by ANR (project HM-TC, grant number ANR-09-EMER-006), France Alzheimer Association (project IRMA7), by the CATI project (Fondation Plan Alzheimer), by the program Investissements d’avenir (grant number ANR-10-IAIHU-06), from the Philippe foundation, and by the joint NSF/NIH/ANR program 'Collaborative Research in Computational Neuroscience' (project HIPLAY7, grant number ANR-16-NEUC-0001-01)., ANR-09-EMER-0006,HM-TC,Modélisation des interactions entre l'hippocampe et le cortex dans « la conscience temporelle», établie à partir de l'imagerie cérébrale multimodale anatomique et fonctionnelle(2009), Colliot, Olivier, Domaines émergents - Modélisation des interactions entre l'hippocampe et le cortex dans ' la conscience temporelle', établie à partir de l'imagerie cérébrale multimodale anatomique et fonctionnelle - - HM-TC2009 - ANR-09-EMER-0006 - EMER - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks - - HIPLAY72016 - ANR-16-NEUC-0001 - CRCNS - VALID, Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cornu Ammonis, Focus (geometry), Computer science, 0206 medical engineering, Population, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Biomedical Engineering, Hippocampus, 02 engineering and technology, Grey matter, Article, Diffeomorphism, Temporal lobe, Epilepsy, Atrophy, Seizures, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Segmentation, 7-Tesla, education, education.field_of_study, medicine.diagnostic_test, business.industry, Morphometry, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Subiculum, Magnetic resonance imaging, Pattern recognition, Ultra-high-field, medicine.disease, 020601 biomedical engineering, Magnetic Resonance Imaging, medicine.anatomical_structure, Epilepsy, Temporal Lobe, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], nervous system, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, business, Thickness
Abstract: International audience; OBJECTIVE. 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon. METHODS. We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects. RESULTS. We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge. CONCLUSION. This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients. SIGNIFICANCE. As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).
Published: 2021
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8. Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer's disease

Author: Jorge Samper-González, Junhao Wen, Alexandre Routier, Stanley Durrleman, Simona Bottani, Sabrina Fontanella, Anne Bertrand, Olivier Colliot, Ninon Burgos, Thomas Jacquemont, Stéphane Epelbaum, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) ANR-11-IDEX-004 (Agence Nationale de la Recherche-11-Initiative d’Excellence-004, project LearnPETMR number SU-16-R-EMR-16), from the European Union H2020 program (project EuroPOND, grant number 666992, project HBP SGA1 grant number 720270), from the joint NSF/NIH/ANR program 'Collaborative Research in Computational Neuroscience' (project HIPLAY7, grant number ANR-16-NEUC-0001-01), from Agence Nationale de la Recherche (project PREVDEMALS, grant number ANR-14-CE15-0016-07),from the European Research Council (to Dr Durrleman project LEASP, grant number 678304),from the Abeona Foundation (project Brain@Scale), and from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).J.W. receives financial support from China Scholarship Council (CSC). O.C. is supported by a 'Contrat d’Interface Local' from Assistance Publique-Hôpitaux de Paris (AP-HP). N.B. receives funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. PCOFUND-GA-2013-609102, through the PRESTIGE programme coordinated by Campus France., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Colliot, Olivier, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Sorbonne Université (SU)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), and Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Male, Computer Science - Machine Learning, Open-source, Computer science, Diffusion magnetic resonance imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Quantitative Biology - Quantitative Methods, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Statistics - Machine Learning, Preprocessor, Quantitative Methods (q-bio.QM), Aged, 80 and over, Small data, General Neuroscience, Image and Video Processing (eess.IV), 05 social sciences, Brain, Alzheimer's disease, Classification, Reproducibility, 3. Good health, Feature (computer vision), DTI, Female, Smoothing, Information Systems, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Feature extraction, Machine Learning (stat.ML), Feature selection, 050105 experimental psychology, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Alzheimer Disease, Image Interpretation, Computer-Assisted, Fractional anisotropy, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 0501 psychology and cognitive sciences, Aged, business.industry, Pattern recognition, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Electrical Engineering and Systems Science - Image and Video Processing, FOS: Biological sciences, Artificial intelligence, business, 030217 neurology & neurosurgery, Software, Diffusion MRI
Abstract: Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with different approaches is difficult to compare and these studies are also difficult to reproduce. In the present paper, we first extend a previously proposed framework to diffusion MRI data for AD classification. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 0.05 up to 0.40 relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML., 51 pages, 5 figure and 6 tables
Published: 2021
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9. Primary Progressive Aphasia Associated With GRN Mutations: New Insights Into the Nonamyloid Logopenic Variant

Author: Bruno Dubois, Audrey Gabelle, François Sellal, Jérémie Pariente, Marie Sarazin, Agnès Camuzat, Raffaella Migliaccio, Frédérique Etcharry-Bouyx, Amandine Geraudie, Sophie Ferrieux, Simona Bottani, Carole Roué-Jagot, Mira Didic, Florence Pasquier, Leila Sellami, Alexis Brice, Olivier Colliot, Fabienne Clot, Marc Teichmann, Didier Hannequin, Christel Thauvin-Robinet, Claire Boutoleau-Bretonnière, Julien Lagarde, Cinzia Coppola, David Wallon, Richard Levy, Marie Noguès-Lassiaille, Dario Saracino, Aurélie Funkiewiez, Maria Luisa Gorno-Tempini, Olivier Martinaud, Isabelle Le Ber, Marion Houot, Stéphane Epelbaum, Sophie Auriacombe, Alexandre Morin, Vincent Deramecourt, Ftd-Als, Philippe Couratier, Daisy Rinaldi, Saracino, Dario, Ferrieux, Sophie, Noguès-Lassiaille, Marie, Houot, Marion, Funkiewiez, Aurélie, Sellami, Leila, Deramecourt, Vincent, Pasquier, Florence, Couratier, Philippe, Pariente, Jérémie, Géraudie, Amandine, Epelbaum, Stéphane, Wallon, David, Hannequin, Didier, Martinaud, Olivier, Clot, Fabienne, Camuzat, Agnè, Bottani, Simona, Rinaldi, Daisy, Auriacombe, Sophie, Sarazin, Marie, Didic, Mira, Boutoleau-Bretonnière, Claire, Thauvin-Robinet, Christel, Lagarde, Julien, Roué-Jagot, Carole, Sellal, Françoi, Gabelle, Audrey, Etcharry-Bouyx, Frédérique, Morin, Alexandre, Coppola, Cinzia, Levy, Richard, Dubois, Bruno, Brice, Alexi, Colliot, Olivier, Luisa Gorno-Tempini, Maria, Teichmann, Marc, Migliaccio, Raffaella, Le Ber, Isabelle, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Lille, CHU Limoges, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Neuropsychologie et imagerie de la mémoire humaine (NIMH), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Charles Foix [AP-HP], Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Sainte Anne [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre hospitalier universitaire de Nantes (CHU Nantes), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), GHU AP-HP Centre Université de Paris, Hôpitaux Civils de Colmar, Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), University of the Study of Campania Luigi Vanvitelli, University of California [San Francisco] (UC San Francisco), University of California (UC), The research leading to these results received funding from the Investissements davenir ANR-11-INBS-0011. This work was funded by the Programme Hospitalier de Recherche Clinique (PHRC) FTLD-exome (to ILB, promotion by Assistance Publique Hôpitaux de Paris), PHRC Predict-PGRN (to ILB, promotion by Assistance Publique Hôpitaux de Paris)., ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Admin, Oskar, Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Toulouse [Toulouse], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of California [San Francisco] (UCSF), University of California, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE)
Subjects: Oncology, medicine.medical_specialty, Apraxia, 050105 experimental psychology, Primary progressive aphasia, 03 medical and health sciences, Dementia aphasia, 0302 clinical medicine, Atrophy, Gyrus, Aphasia, Agrammatism, Internal medicine, medicine, 0501 psychology and cognitive sciences, Prospective cohort study, business.industry, 05 social sciences, respiratory system, medicine.disease, medicine.anatomical_structure, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Neurology (clinical), medicine.symptom, business, 030217 neurology & neurosurgery, Frontotemporal dementia
Abstract: ObjectiveTo determine relative frequencies and linguistic profiles of primary progressive aphasia (PPA) variants associated with GRN (progranulin) mutations and to study their neuroanatomic correlates.MethodsPatients with PPA carrying GRN mutations (PPA-GRN) were selected among a national prospective research cohort of 1,696 patients with frontotemporal dementia, including 235 patients with PPA. All patients with amyloid-positive CSF biomarkers were excluded. In this cross-sectional study, speech/language and cognitive profiles were characterized with standardized evaluations, and gray matter (GM) atrophy patterns using voxel-based morphometry. Comparisons were performed with controls and patients with sporadic PPA.ResultsAmong the 235 patients with PPA, 45 (19%) carried GRN mutations, and we studied 32 of these. We showed that logopenic PPA (lvPPA) was the most frequent linguistic variant (n = 13, 41%), followed by nonfluent/agrammatic (nfvPPA; n = 9, 28%) and mixed forms (n = 8, 25%). Semantic variant was rather rare (n = 2, 6%). Patients with lvPPA, qualified as nonamyloid lvPPA, presented canonical logopenic deficit. Seven of 13 had a pure form; 6 showed subtle additional linguistic deficits not fitting criteria for mixed PPA and hence were labeled as logopenic-spectrum variant. GM atrophy involved primarily left posterior temporal gyrus, mirroring neuroanatomic changes of amyloid-positive-lvPPA. Patients with nfvPPA presented agrammatism (89%) rather than apraxia of speech (11%).ConclusionsThis study shows that the most frequent PPA variant associated with GRN mutations is nonamyloid lvPPA, preceding nfvPPA and mixed forms, and illustrates that the language network may be affected at different levels. GRN testing is indicated for patients with PPA, whether familial or sporadic. This finding is important for upcoming GRN gene–specific therapies.
Published: 2021
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10. Plasma NfL levels and longitudinal change rates in C9orf72 and GRN-associated diseases: from tailored references to clinical applications

Author: Fabienne Clot, Florence Pasquier, Didier Hannequin, Olivier Colliot, Armelle Rametti-Lacroux, Sophie Auriacombe, Raffaella Migliaccio, Ludmila Jornea, Marion Houot, Agnès Camuzat, Carole Azuar, Alexis Brice, Mathilde Sauvée, Philippe Martin-Hardy, Richard Levy, Philippe Couratier, Isabelle Le Ber, Jérémie Pariente, Claire Boutoleau-Bretonnière, Karim Dorgham, Dario Saracino, Bruno Dubois, Mira Didic, Daisy Rinaldi, Prev-Demals, David Wallon, Predict-PGRN study groups, Ftd-Als, Sylvie Forlani, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Immunologie et de Maladies Infectieuses (CIMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Génétique Cytogénétique et Embryologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Lille 2 - Faculté de Médecine, CHU Limoges, Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Universitaire [Grenoble] (CHU), CIC - Nantes, Université de Nantes (UN)-IFR26-Institut National de la Santé et de la Recherche Médicale (INSERM), Toulouse Neuro Imaging Center (ToNIC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hôpital Purpan [Toulouse], CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse], Institut de Neurosciences des Systèmes (INS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU), Hôpital de la Timone [CHU - APHM] (TIMONE), Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), The research leading to these results received funding from the ’Investissements d’avenir’ ANR-11- INBS-0011. This work was partially funded by the Programme Hospitalier de Recherche Clinique (PHRC) Predict- PGRN (to ILB, promotion by Assistance Publique–Hôpitaux de Paris), the PHRC FTLD- exome (to ILB, promotion by Assistance Publique–Hôpitaux de Paris), by the ANR- PRTS PREV- DEMALS project (to ILB, grant number ANR-14- CE15-0016-07, promotion by Assistance Publique–Hôpitaux de Paris) and the Fondation Vaincre Alzheimer (to ILB, grant number FR-17035)., ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Immunologie et des Maladies Infectieuses (CIMI), Faculté de Médecine Henri Warembourg - Université de Lille, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Saracino, Dario, Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées
Subjects: Oncology, 0303 health sciences, medicine.medical_specialty, business.industry, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Disease progression, Youden's J statistic, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, medicine.disease, Therapeutic trial, 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, C9orf72, Internal medicine, Medicine, Biomarker (medicine), Surgery, Neurology (clinical), Amyotrophic lateral sclerosis, business, Trial registration, 030217 neurology & neurosurgery, 030304 developmental biology, Frontotemporal dementia
Abstract: ObjectiveNeurofilament light chain (NfL) is a promising biomarker in genetic frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). We evaluated plasma neurofilament light chain (pNfL) levels in controls, and their longitudinal trajectories in C9orf72 and GRN cohorts from presymptomatic to clinical stages.MethodsWe analysed pNfL using Single Molecule Array (SiMoA) in 668 samples (352 baseline and 316 follow-up) of C9orf72 and GRN patients, presymptomatic carriers (PS) and controls aged between 21 and 83. They were longitudinally evaluated over a period of >2 years, during which four PS became prodromal/symptomatic. Associations between pNfL and clinical–genetic variables, and longitudinal NfL changes, were investigated using generalised and linear mixed-effects models. Optimal cut-offs were determined using the Youden Index.ResultspNfL levels increased with age in controls, from ~5 to~18 pg/mL (pGRN patients had higher levels than C9orf72 (86.21 vs 39.49 pg/mL, p=0.014), and greater progression rates (ARC:+29.3% vs +24.7%; p=0.016). In C9orf72 patients, levels were associated with the phenotype (ALS: 71.76 pg/mL, FTD: 37.16, psychiatric: 15.3; p=0.003) and remarkably lower in slowly progressive patients (24.11, ARC: +2.5%; p=0.05). Mean ARC was +3.2% in PS and +7.3% in prodromal carriers. We proposed gene-specific cut-offs differentiating patients from controls by decades.ConclusionsThis study highlights the importance of gene-specific and age-specific references for clinical and therapeutic trials in genetic FTD/ALS. It supports the usefulness of repeating pNfL measurements and considering ARC as a prognostic marker of disease progression.Trial registration numbersNCT02590276 and NCT04014673.
Published: 2021
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11. Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data

Author: Olivier Colliot, Pascal Lu, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001(PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationalede la Recherche-10-IA Institut Hospitalo-Universitaire-6) and from the INRIAProject Lab Program (project Neuromarkers)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Colliot, Olivier, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cox Proportional Hazards, Computer science, Imaging genetics, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 02 engineering and technology, computer.software_genre, 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Health Information Management, 0202 electrical engineering, electronic engineering, information engineering, Additive model, ComputingMilieux_MISCELLANEOUS, Multilevel model, Brain, Magnetic Resonance Imaging, 3. Good health, Computer Science Applications, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Biotechnology, Survival model, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Machine learning, 03 medical and health sciences, Neuroimaging, Alzheimer Disease, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Cognitive Dysfunction, Alzheimer’s Disease, Electrical and Electronic Engineering, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Modality (human–computer interaction), Modalities, business.industry, Proportional hazards model, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Group Lasso penalty, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Proximal Gradient Methods, Artificial intelligence, business, computer
Abstract: International audience; This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibitingmild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is incontrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality a a ℓ2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer’s disease (AD) among patients with mild cognitive impairment(MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in theprediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases
Published: 2021
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12. Exploratory analysis of the genetics of impulse control disorders in Parkinson's disease using genetic risk scores

Author: Samir Bekadar, Johann Faouzi, Jean-Christophe Corvol, Olivier Colliot, Baptiste Couvy-Duchesne, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Bioscience, University of Queensland [Brisbane], Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Faouzi, Johann, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Subjects: 0301 basic medicine, Adult, Male, Genome-wide association study, Disease, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Risk Factors, Medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Depression (differential diagnoses), Genetic association, Aged, Genetics, Aged, 80 and over, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], business.industry, Parkinson Disease, Impulse control disorders, Middle Aged, 3. Good health, Disruptive, Impulse Control, and Conduct Disorders, 030104 developmental biology, Bonferroni correction, Neurology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Sample size determination, Parkinson''s disease, Multiple comparisons problem, Cohort, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], symbols, Female, Genetic risk scores, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Geriatrics and Gerontology, business, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: International audience; Objective: To study the association between impulse control disorders (ICDs) in Parkinsons disease (PD) and genetic risk scores (GRS) for 40 known or putative risk factors (e.g. depression, personality traits). Background: In absence of published genome-wide association studies (GWAS), little is known about the genetics of ICDs in PD. GRS of related phenotypes, for which large GWAS are available, may help shed light on the genetic contributors of ICDs in PD. Methods: We searched for GWAS on European ancestry populations with summary statistics publicly available for a broad range of phenotypes, including other psychiatric disorders, personality traits, and simple phenotypes. We separately tested their predictive ability in two of the largest PD cohorts with clinical and genetic available: the Parkinsons Progression Markers Initiative database (N = 368, 33% female, age range = [33 − 84]) and the Drug Interaction With Genes in Parkinsons Disease study (N = 373, 40% female, age range = [29 − 85]). Results: We considered 40 known or putative risk factors for ICDs in PD for which large GWAS had been published. After Bonferroni correction for multiple comparisons, no GRS or the combination of the 40 GRS were significantly associated with ICDs from the analyses in each cohort separately and from the meta-analysis. Conclusion: Albeit unsuccessful, our approach will gain power in the coming years with increasing availability of genotypes in clinical cohorts of PD, but also from future increase in GWAS sample sizes of the phenotypes we considered. Our approach may be applied to other complex disorders, for which GWAS are not available or limited.
Published: 2021
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13. Association of plasma YKL-40 with brain amyloid-β levels, memory performance, and sex in subjective memory complainers

Author: Andrea Vergallo, Simone Lista, Pablo Lemercier, Patrizia A. Chiesa, Henrik Zetterberg, Kaj Blennow, Marie-Claude Potier, Marie-Odile Habert, Filippo Baldacci, Enrica Cavedo, Filippo Caraci, Bruno Dubois, Harald Hampel, Hovagim Bakardjian, Habib Benali, Hugo Bertin, Joel Bonheur, Laurie Boukadida, Nadia Boukerrou, Patrizia Chiesa, Olivier Colliot, Marion Dubois, Stéphane Epelbaum, Geoffroy Gagliardi, Remy Genthon, Marion Houot, Aurélie Kas, Foudil Lamari, Marcel Levy, Christiane Metzinger, Fanny Mochel, Francis Nyasse, Catherine Poisson, Marie Revillon, Antonio Santos, Katia Santos Andrade, Marine Sole, Mohmed Surtee, Michel Thiebaut de Schotten, Nadjia Younsi, Mohammad Afshar, Lisi Flores Aguilar, Leyla Akman-Anderson, Joaquín Arenas, Jesús Ávila, Claudio Babiloni, Richard Batrla, Norbert Benda, Keith L. Black, Arun L.W. Bokde, Ubaldo Bonuccelli, Karl Broich, Francesco Cacciola, Giuseppe Caruso, Juan Castrillo†, Roberto Ceravolo, Massimo Corbo, Jean-Christophe Corvol, Augusto Claudio Cuello, Jeffrey L. Cummings, Herman Depypere, Andrea Duggento, Enzo Emanuele, Valentina Escott-Price, Howard Federoff, Maria Teresa Ferretti, Massimo Fiandaca, Richard A. Frank, Francesco Garaci, Hugo Geerts, Ezio Giacobini, Filippo S. Giorgi, Edward J. Goetzl, Manuela Graziani, Marion Haberkamp, Britta Hänisch, Karl Herholz, Felix Hernandez, Bruno P. Imbimbo, Dimitrios Kapogiannis, Eric Karran, Steven J. Kiddle, Seung H. Kim, Yosef Koronyo, Maya Koronyo-Hamaoui, Todd Langevin, Stéphane Lehéricy, Francisco Llavero, Jean Lorenceau, Alejandro Lucía, Dalila Mango, Mark Mapstone, Christian Neri, Robert Nisticò, Sid E. O’bryant, Giovanni Palermo, George Perry, Craig Ritchie, Simone Rossi, Amira Saidi, Emiliano Santarnecchi, Lon S. Schneider, Olaf Sporns, Nicola Toschi, Pedro L. Valenzuela, Bruno Vellas, Steven R. Verdooner, Nicolas Villain, Kelly Virecoulon Giudici, Mark Watling, Lindsay A. Welikovitch, Janet Woodcock, Erfan Younesi, José L. Zugaza, Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: musculoskeletal diseases, 0301 basic medicine, Apolipoprotein E, Male, Risk, Aging, medicine.medical_specialty, Amyloid, YKL-40, [SDV]Life Sciences [q-bio], Disease, 03 medical and health sciences, Alzheimer's disease, amyloid, neuroinflammation, Sex, 0302 clinical medicine, Neuroinflammation, Alzheimer Disease, Memory, Internal medicine, medicine, Premovement neuronal activity, Humans, Effects of sleep deprivation on cognitive performance, Chitinase-3-Like Protein 1, Longitudinal Studies, Inflammation, Amyloid beta-Peptides, business.industry, General Neuroscience, Neurodegeneration, Settore FIS/07, Brain, medicine.disease, 030104 developmental biology, Endocrinology, Biomarker (medicine), Female, Neurology (clinical), Geriatrics and Gerontology, business, Alzheimer’s disease, 030217 neurology & neurosurgery, Biomarkers, Developmental Biology
Abstract: Neuroinflammation, a key early pathomechanistic alteration of Alzheimer’s disease, may represent either a detrimental or a compensatory mechanism or both (according to the disease stage). YKL-40, a glycoprotein highly expressed in differentiated glial cells, is a candidate biomarker for in vivo tracking neuroinflammation in humans. We performed a longitudinal study in a monocentric cohort of cognitively healthy individuals at risk for Alzheimer’s disease exploring whether age, sex, and the apolipoprotein E e4 allele affect plasma YKL-40 concentrations. We investigated whether YKL-40 is associated with brain amyloid-β (Aβ) deposition, neuronal activity, and neurodegeneration as assessed via neuroimaging biomarkers. Finally, we investigated whether YKL-40 may predict cognitive performance. We found an age-associated increase of YKL-40 and observed that men display higher concentrations than women, indicating a potential sexual dimorphism. Moreover, YKL-40 was positively associated with memory performance and negatively associated with brain Aβ deposition (but not with metabolic signal). Consistent with translational studies, our results suggest a potentially protective effect of glia on incipient brain Aβ accumulation and neuronal homeostasis.
Published: 2020
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14. Enlightening behavioral disturbances in myotonic dystrophy type 1 through neuropsychology and imaging correlations: Insights from the frontal and temporal lobe functions

Author: Tanja Stojkovic, Bruno Dubois, Bruno Eymard, Alexandre Morin, Carole Azuar, Alexandre Routier, Aurélie Funkiewiez, and Olivier Colliot
Subjects: Epidemiology, Behavioral neurology, business.industry, Health Policy, Neuropsychology, Disease, Prodromal States, Neuropsychiatry, medicine.disease, Myotonic dystrophy, Temporal lobe, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience
Published: 2020
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15. Predicting factors of cognitive progression in preclinical AD: A five‐year follow‐up of the insight‐study

Author: Remy Genthon, Olivier Colliot, Foudil Lamari, Nadjia Younsi, Stéphane Epelbaum, Bruno Dubois, Nicolas Villain, Stéphanie Bombois, Marie-Odile Habert, Marcel Levy, and Marion Houot
Subjects: Pediatrics, medicine.medical_specialty, Epidemiology, business.industry, Health Policy, Five year follow up, Cognition, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Medicine, Neurology (clinical), Geriatrics and Gerontology, business
Published: 2020
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16. Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72 -associated frontotemporal dementia and amyotrophic lateral sclerosis

Author: Noémie Robil, Agnès Camuzat, Vincent Anquetil, Isabelle Le Ber, Ivan Moszer, Florence Pasquier, Philippe Couratier, Emmanuelle Becker, Ludmila Jornea, David Wallon, Olivier Colliot, Dario Saracino, Sylvie Forlani, Thomas Gareau, Daisy Rinaldi, Virgilio Kmetzsch, Pierre de la Grange, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL), Service de Neurologie [CHU Limoges], CHU Limoges, Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), CHU Rouen, Université de Lille, Département de neurologie [Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), GenoSplice [Paris], FRONTLAB: Fonctions et dysfonctions de systèmes frontaux [ICM Paris] (FRONTlab), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-14-CE15-0016,PREV-DEMALS,Prédire pour prévenir les démences frontotemporales (DFT) et la sclérose latérale amyotrophique (SLA)(2014), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), École pratique des hautes études (EPHE), Département de neurologie[Lille], Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), FRONTlab - Systèmes frontaux : fonctions et dysfonctions (FRONTlab), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Kmetzsch, Virgilio, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Appel à projets générique - Prédire pour prévenir les démences frontotemporales (DFT) et la sclérose latérale amyotrophique (SLA) - - PREV-DEMALS2014 - ANR-14-CE15-0016 - Appel à projets générique - VALID, Infrastructures - Institut français de bioinformatique - - IFB (ex Renabi-IFB)2011 - ANR-11-INBS-0013 - INBS - VALID, Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Oncology, medicine.medical_specialty, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Chromosome 9, Disease, medicine.disease_cause, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], C9orf72, Internal medicine, microRNA, medicine, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Amyotrophic lateral sclerosis, Prospective cohort study, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Mutation, business.industry, medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Psychiatry and Mental health, Surgery, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 030217 neurology & neurosurgery, Frontotemporal dementia
Abstract: ObjectiveTo identify potential biomarkers of preclinical and clinical progression in chromosome 9 open reading frame 72 gene (C9orf72)-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72 patients and presymptomatic carriers.MethodsThe PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72 mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing. The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers.ResultsFour miRNAs were differentially expressed between patients and controls: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in patients. MiR-34a-5p was also overexpressed in presymptomatic carriers compared with healthy controls, suggesting that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Moreover, miR-345-5p was also overexpressed in patients compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Together, miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease. Four presymptomatic subjects in transitional/prodromal stage, close to the disease conversion, exhibited a stronger similarity with the expression levels of patients.ConclusionsWe identified a signature of four miRNAs differentially expressed in plasma between clinical conditions that have potential to represent progression biomarkers for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. This study suggests that dysregulation of miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.Trial registration numberNCT02590276.
Published: 2020
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17. Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

Author: Louise-Laure Mariani, Marie Villotte, Alexis Brice, Lydia Yahia-Cherif, Lydia Chougar, Romain Valabregue, Christine Payan, David Grabli, Emma Biondetti, Rahul Gaurav, Olivier Colliot, Johann Faouzi, Nadya Pyatigorskaya, Gwendoline Dupont, Stéphane Lehéricy, Ines Piot, Jean-Christophe Corvol, Florence Cormier, M. Vidailhet, Bertrand Degos, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université, Centre de Neuro-Imagerie de Recherche (CENIR), Université de Paris - Faculté de Médecine Paris Centre (UP Médecine Paris Centre), Université de Paris (UP), Service des Maladies du Système Nerveux [CHU Pitié-Salpêtrière], Service de neurochirurgie [CHU de Dijon], Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), BESPIM, Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Centre interdisciplinaire de recherche en biologie (CIRB), Collège de France (CdF (institution))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hôpital Avicenne [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris] (CENIR), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), Université Paris Cité (UPC), Pôle des Maladies du Système Nerveux [CHU Pitié-Salpêtrière] (Pôle MSN), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), PARK-AI, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), Service de neurochirurgie [CHU Dijon], and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: 0301 basic medicine, medicine.medical_specialty, Parkinson's disease, multiple system atrophy, Progressive supranuclear palsy, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Parkinsonian Disorders, medicine, Humans, multimodal magnetic resonance imaging, Receiver operating characteristic, medicine.diagnostic_test, business.industry, Parkinsonism, Magnetic resonance imaging, progressive supranuclear palsy, medicine.disease, Magnetic Resonance Imaging, 3. Good health, nervous system diseases, machine learning algorithm, 030104 developmental biology, Diffusion Tensor Imaging, Neurology, Categorization, nervous system, Cohort, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Supranuclear Palsy, Progressive, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract: Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA-P), and 23 with MSA of the cerebellar variant (MSA-C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner-dependent effects, we tested two types of normalizations using patient data or healthy control data. Results In the replication cohort, high accuracies were achieved using volumetry in the classification of PD-PSP, PD-MSA-C, PSP-MSA-C, and PD-atypical parkinsonism (balanced accuracies: 0.840-0.983, area under the receiver operating characteristic curves: 0.907-0.995). Performances were lower for the classification of PD-MSA-P, MSA-C-MSA-P (balanced accuracies: 0.765-0.784, area under the receiver operating characteristic curve: 0.839-0.871) and PD-PSP-MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions A machine learning approach based on volumetry enabled accurate classification of subjects with early-stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society.
Published: 2020
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18. Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review

Author: Giulia Bassignana, Johann Faouzi, Manon Ansart, Tiziana Cattai, Stéphane Epelbaum, Alexandre Bône, Adam Wild, Stanley Durrleman, Raphaël Couronné, Junhao Wen, Olivier Colliot, Elina Thibeau-Sutre, Didier Dormont, Ninon Burgos, Maxime Louis, Simona Bottani, Igor Koval, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Sorbonne Université (SU), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), The research leading to these results has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) from the European Union H2020 program (project EuroPOND, grant number 666992, project HBP SGA1 grant number 720270), from the ICM Big Brain Theory Program (project DYNAMO, project PredictICD), from the Inria Project Lab Program (project Neuromarkers), from the European ResearchCouncil (to Dr Durrleman project LEASP, grant number 678304), from the Abeona Foundation (project Brain@Scale). OC is supported by a 'contrat d’interface local' from AP-HP. China Scholarship Council supports J.W’s work on this topic., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Ansart, Manon, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Subjects: Decision support system, Automatic prediction, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Electroencephalography, computer.software_genre, 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Cognition, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0303 health sciences, Radiological and Ultrasound Technology, Progression, medicine.diagnostic_test, Alzheimer's disease, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, Feature (computer vision), Disease Progression, Computer Vision and Pattern Recognition, Psychology, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Mild Cognitive Impairment, Health Informatics, Feature selection, Machine learning, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Alzheimer Disease, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, Dementia, Radiology, Nuclear Medicine and imaging, Cognitive Dysfunction, 030304 developmental biology, Modalities, business.industry, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Quantitative review, Positron-Emission Tomography, Test set, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer’s disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that shortterm predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.
Published: 2020
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19. Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges

Author: Ninon Burgos, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Computer science, MEDLINE, Translational research, Neuroimaging, Machine learning, computer.software_genre, Task (project management), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Generalization (learning), medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Dementia, Humans, Pace, Brain Diseases, business.industry, prediction, medicine.disease, Prognosis, Variety (cybernetics), 030104 developmental biology, Neurology, translational research, classification, Neurology (clinical), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Purpose of review. Machine learning (ML) is an artificial intelligence technique that allows computers to perform a task without being explicitly programmed. ML can be used to assist diagnosis and prognosis of brain disorders. While the earliest papers date from more than ten years ago, research increases at a very fast pace. Recent findings. Recent works using ML for diagnosis have moved from classification of a given disease versus controls to differential diagnosis. Intense research has been devoted to the prediction of the future patient state. While a lot of earlier works focused on neuroimaging as data source, the current trend is on the integration of multimodal. In terms of targeted diseases, dementia remains dominant, but approaches have been developed for a wide variety of neurological and psychiatric diseases. Summary. ML is extremely promising for assisting diagnosis and prognosis in brain disorders. Nevertheless, we argue that key challenges remain to be addressed by the community for bringing these tools in clinical routine: good practices regarding validation and reproducible research need to be more widely adopted; extensive generalization studies are required; interpretable models are needed to overcome the limitations of black-box approaches.
Published: 2020
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20. Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting

Author: Emma Biondetti, Louise-Laure Mariani, Johann Faouzi, Bertrand Degos, Alexis Brice, M. Vidailhet, Jean-Christophe Corvol, Lydia Chougar, Marie Villotte, Olivier Colliot, Gwendoline Dupont, Nadya Pyatigorskaya, Romain Valabregue, David Grabli, Florence Cormier, Stéphane Lehéricy, Christine Payan, Ines Piot, and Rahul Gaurav
Subjects: medicine.medical_specialty, medicine.diagnostic_test, business.industry, Parkinsonism, Magnetic resonance imaging, medicine.disease, Training cohort, nervous system diseases, 030218 nuclear medicine & medical imaging, Parkinsonian syndromes, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, stomatognathic system, nervous system, Categorization, mental disorders, Replication (statistics), Research environment, Cohort, medicine, business, 030217 neurology & neurosurgery
Abstract: BackgroundSeveral studies have shown that machine learning algorithms using MRI data can accurately discriminate parkinsonian syndromes. Validation under clinical conditions is missing.ObjectivesTo evaluate the accuracy for the categorization of parkinsonian syndromes of a machine learning algorithm trained with a research cohort and tested on an independent clinical replication cohort.Methods361 subjects, including 94 healthy controls, 139 patients with PD, 60 with PSP with Richardson’s syndrome, 41 with MSA of the parkinsonian variant (MSA-P) and 27 with MSA of the cerebellar variant (MSA-P), were recruited. They were divided into a training cohort (n=179) scanned in a research environment, and a replication cohort (n=182), scanned in clinical conditions on different MRI systems. Volumes and DTI metrics in 13 brain regions were used as input for a supervised machine learning algorithm.ResultHigh accuracy was achieved using volumetry in the classification of PD versus PSP, PD versus MSA-P, PD versus MSA-C, PD versus atypical parkinsonian syndromes and PSP versus MSA-C in both cohorts, although slightly lower in the replication cohort (balanced accuracy: 0.800 to 0.915 in the training cohort; 0.741 to 0.928 in the replication cohort). Performance was lower in the classification of PSP versus MSA-P and MSA-P versus MSA-C. When adding DTI metrics, the performance tended to increase in the training cohort, but not in the replication cohort.ConclusionsA machine learning approach based on volumetric and DTI data can accurately classify subjects with early-stage parkinsonism, scanned on different MRI systems, in the setting of their clinical workup.
Published: 2020
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21. Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

Author: Stéphane Epelbaum, Bruno Dubois, Alexandre Morin, Harald Hampel, Marcel Levy, Anne Bertrand, Didier Dormont, Jamila Ahdidan, Olivier Colliot, Marc Teichmann, Sébastian Ströer, Jorge Samper-González, Dalila Samri, Pierrick Coupé, Aline Mendes, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP], Instituto Butantan [São Paulo], PICTURA, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Brainreader, Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Fédération de neurologie 4, CHU Pitié-Salpêtrière [APHP]-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Université Pierre et Marie Curie - Paris 6 (UPMC), Department of Psychiatry, Psychosomatic Medicine and Psychotherapy [Goethe Universität], Goethe-Universität Frankfurt am Main, Alzheimer Precision Medicine GRC n°21 (APM), CHU Pitié-Salpêtrière [APHP], FRONTlab - Systèmes frontaux : fonctions et dysfonctions (FRONTlab), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), PNRIA, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 APM), FRONTLAB: Fonctions et dysfonctions de systèmes frontaux [ICM Paris] (FRONTlab), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Groupe de recherche clinique Alzheimer Precision Medicine (GRC 21 - APM)
Subjects: 0301 basic medicine, Signal Processing (eess.SP), FOS: Computer and information sciences, Male, Support Vector Machine, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer Vision and Pattern Recognition (cs.CV), [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods, 0302 clinical medicine, Assessment of cognitive disorders/dementia, All Cognitive Disorders/Dementia, Quantitative Methods (q-bio.QM), Volumetric MRI, medicine.diagnostic_test, General Neuroscience, Image and Video Processing (eess.IV), Brain, General Medicine, Alzheimer's disease, Middle Aged, Clinical routine, Magnetic Resonance Imaging, 3. Good health, Psychiatry and Mental health, Clinical Psychology, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Cohort, Female, Alzheimer’s disease, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, MRI, Neuroimaging, 03 medical and health sciences, Magnetic resonance imaging, Alzheimer Disease, Image Interpretation, Computer-Assisted, medicine, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Electrical Engineering and Systems Science - Signal Processing, Assessment of cognitive disorders/Dementia, Aged, Retrospective Studies, business.industry, Memory clinic, Univariate, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Reproducibility of Results, Pattern recognition, Electrical Engineering and Systems Science - Image and Video Processing, Support vector machine, Statistical classification, 030104 developmental biology, FOS: Biological sciences, Dementia, Artificial intelligence, Geriatrics and Gerontology, business, Cognition Disorders, Classifier (UML), 030217 neurology & neurosurgery, Software, All cognitive disorders/dementia
Abstract: BACKGROUND: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers has been evaluated mostly in the artificial setting of research datasets. OBJECTIVE: Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic. METHODS: We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria. RESULTS: Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM. CONCLUSION: In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.
Published: 2020
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22. Gaussian Graphical Model Exploration and Selection in High Dimension Low Sample Size Setting

Author: Stanley Durrleman, Stéphanie Allassonnière, Thomas Lartigue, Simona Bottani, Stéphanie Baron, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), 666992, H2020 European Research Council, ANR-10-IAIHU-06, Agence Nationale de la Recherche, PNRIA, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 826421,TVB-Cloud (2018), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Kullback–Leibler divergence, Computer science, Multivariate random variable, Gaussian, Inference, Machine Learning (stat.ML), 02 engineering and technology, Synthetic data, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, Dimension (vector space), Artificial Intelligence, Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0202 electrical engineering, electronic engineering, information engineering, Graphical model, business.industry, Applied Mathematics, Model selection, Computational Theory and Mathematics, Sample size determination, symbols, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithm, Software
Abstract: International audience; Gaussian graphical models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: the nodewise approach and the penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with a nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.
Published: 2020
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23. The White Matter Module-Hub Network of Semantics Revealed by Semantic Dementia

Author: Bruno Dubois, Olivier Colliot, Marc Teichmann, Martina Sundqvist, Alexandre Routier, FRONTLAB: Fonctions et dysfonctions de systèmes frontaux [ICM Paris] (FRONTlab), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Curie [Paris], Université Paris sciences et lettres (PSL), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), This research has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Agence Institut Hospitalo-Universitaire-6). O.C. is supported by a 'Contrat d’Interface Local' from Assistance Publique-Hôpitaux de Paris (AP-HP)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Routier, Alexandre, Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), FRONTlab - Systèmes frontaux : fonctions et dysfonctions (FRONTlab), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées (MIA-Paris), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay
Subjects: Adult, Cognitive Neuroscience, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Semantic dementia, Cognitive neuroscience, 050105 experimental psychology, Diffusion MRI, White matter, 03 medical and health sciences, 0302 clinical medicine, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 0501 psychology and cognitive sciences, Architecture, Cognitive science, business.industry, 05 social sciences, Modular design, medicine.disease, Magnetic Resonance Imaging, White Matter, Temporal Lobe, Semantics, medicine.anatomical_structure, Tractograghy, Frontotemporal Dementia, Nerve Net, business, Psychology, 030217 neurology & neurosurgery
Abstract: Cognitive neuroscience exploring the architecture of semantics has shown that coherent supramodal concepts are computed in the anterior temporal lobes (ATL), but it is unknown how/where modular information implemented by posterior cortices (word/object/face forms) is conveyed to the ATL hub. We investigated the semantic module-hub network in healthy adults (n = 19) and in semantic dementia patients (n = 28) by combining semantic assessments of verbal and nonverbal stimuli and MRI-based fiber tracking using seeds in three module-related cortices implementing (i) written word forms (visual word form area), (ii) abstract lexical representations (posterior–superior temporal cortices), and (iii) face/object representations (face form area). Fiber tracking revealed three key tracts linking the ATL with the three module-related cortices. Correlation analyses between tract parameters and semantic scores indicated that the three tracts subserve semantics, transferring modular verbal or nonverbal object/face information to the left and right ATL, respectively. The module-hub tracts were functionally and microstructurally damaged in semantic dementia, whereas damage to non-module-specific ATL tracts (inferior longitudinal fasciculus, uncinate fasciculus) had more limited impact on semantic failure. These findings identify major components of the white matter module-hub network of semantics, and they corroborate/materialize claims of cognitive models positing direct links between modular and semantic representations. In combination with modular accounts of cognition, they also suggest that the currently prevailing “hub-and-spokes” model of semantics could be extended by incorporating an intermediate module level containing invariant representations, in addition to “spokes,” which subserve the processing of a near-unlimited number of sensorimotor and speech-sound features.
Published: 2020
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24. Multilevel Survival Analysis with Structured Penalties for Imaging Genetics data

Author: Olivier Colliot, Pascal Lu, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), LU, Pascal, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: Computer science, Imaging genetics, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Single-nucleotide polymorphism, Disease, Machine learning, computer.software_genre, Alzheimer's Disease, Task (project management), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Survival analysis, 030304 developmental biology, 0303 health sciences, Modalities, business.industry, Proportional hazards model, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Regression, 3. Good health, Cox model, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Predicting the future occurrence of Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI) is a topic of active research. Many papers have formulated this question as a classification problem: one considers a fixed time of conversion and aims to discriminate between the patients who have converted to AD at that time and those who have not. However, a clinically more relevant question is to predict the date at which a patient to AD. Survival analysis is an adequate statistical framework for such a task. Multimodal data (imaging and genetic) provide complementary information for the prediction. While imaging data provides an estimate of the current patient's state, genetic variants can be associated to the speed of progression to AD. Although they do not provide the same type of information, most papers in classification or regression put imaging and genetic variables on the same level in order to predict the current or future patient's state. In this work, we propose a survival model using multimodal data to estimate the conversion date to AD, by considering joint effects between the imaging and genetic modalities. We introduce an adapted penalty in the survival model, the group lasso penalty, over joint groups of genes and brain regions. The model is evaluated on genetic (single nucleotide polymorphisms) and imaging (anatomical MRI measures) data from the ADNI database, and compared to a standard Cox model.
Published: 2020

25. Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis

Author: Andreas Hartmann, Linda Marrakchi Kacem, Alexandre Routier, Pietro Gori, Nicholas Ayache, Stanley Durrleman, Yulia Worbe, Olivier Colliot, Cyril Poupon, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Gori, Pietro
Subjects: Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Neuroimaging, 02 engineering and technology, Grey matter, Neural circuits, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, Imaging, Three-Dimensional, 0302 clinical medicine, Atlas (anatomy), Diffeomorphisms, Complex, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, medicine, Biological neural network, Humans, Gray Matter, Electrical and Electronic Engineering, Radiological and Ultrasound Technology, business.industry, Structural connectivity, Multi-object, Morphometry, Tourette, Shape, Bayes Theorem, Pattern recognition, Magnetic Resonance Imaging, White Matter, Computer Science Applications, medicine.anatomical_structure, 020201 artificial intelligence & image processing, Atlas, Diffeomorphism, Artificial intelligence, business, Algorithms, Software, Tourette Syndrome, Shape analysis (digital geometry)
Abstract: International audience; The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modelled as 3D surfaces and curves respectively. Neurodevelopmental disorders involve morphological and organizational alterations which can not be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.
Published: 2018
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26. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Author: Sébastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Adam Wild, Aurélien Maire, Simona Bottani, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Bases de données sur la Biodiversité, Ecologie, Environnement et Sociétés (BBEES), Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), The research leading to these results has received funding from the Abeona Foundation (project Brain@Scale), from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM(2010), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), APPRIMAGE, ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Bottani, Simona, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer science, Image quality, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computer Science - Computer Vision and Pattern Recognition, Health Informatics, [INFO] Computer Science [cs], computer.software_genre, Convolutional neural network, Clinical data warehouse, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030218 nuclear medicine & medical imaging, Set (abstract data type), 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Data Warehousing, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, [INFO]Computer Science [cs], Radiology, Nuclear Medicine and imaging, Quality (business), media_common, Protocol (science), Radiological and Ultrasound Technology, business.industry, Deep learning, Image and Video Processing (eess.IV), Medical image computing, Brain, Quality control, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Electrical Engineering and Systems Science - Image and Video Processing, Magnetic Resonance Imaging, Computer Graphics and Computer-Aided Design, Data warehouse, Brain MRI, Neural Networks, Computer, Computer Vision and Pattern Recognition, Data mining, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-H\^opitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score \textgreater 90\%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score \textgreater 80\%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.
Published: 2022
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27. Learning joint shape and appearance representations with metamorphic auto-encoders

Author: Paul Vernhet, Alexandre Bône, Stanley Durrleman, Olivier Colliot, Vernhet, Paul, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Artificial neural network, Metamorphosis, Computer science, business.industry, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, [MATH] Mathematics [math], 010501 environmental sciences, 01 natural sciences, Shape analysis, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Computer Science::Computer Vision and Pattern Recognition, Numerical brain atlas, Diffeomorphism, Artificial intelligence, [MATH]Mathematics [math], business, 0105 earth and related environmental sciences, Shape analysis (digital geometry)
Abstract: International audience; Transformation-based methods for shape analysis offer a consistent framework to model the geometrical content of images. Most often relying on diffeomorphic transforms, they lack however the ability to properly handle texture and differing topological content. Conversely, modern deep learning methods offer a very efficient way to analyze image textures. Building on the theory of metamorphoses, which models images as combined intensity-domain and spatial-domain transforms of a prototype, we introduce the "metamorphic" auto-encoding architecture. This class of neural networks is interpreted as a Bayesian generative and hierarchical model, allowing the joint estimation of the network parameters, a representative prototype of the training images, as well as the relative importance between the geometrical and texture contents. We give arguments for the practical relevance of the learned prototype and Euclidean latent-space metric, achieved thanks to an explicit normalization layer. Finally, the ability of the proposed architecture to learn joint and relevant shape and appearance representations from image collections is illustrated on BraTs 2018 datasets, showing in particular an encouraging step towards personalized numerical simulation of tumors with data-driven models.
Published: 2020

28. Visualization approach to assess the robustness of neural networks for medical image classification

Author: Didier Dormont, Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), THIBEAU-SUTRE, Elina, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neuroradiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), The research leading to these results has received fundingfrom the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d'avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., PNRIA, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], The research leading to these results has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU06 (Agence Nationale de la Recherche-10-IA Agence Institut Hospitalo-Universitaire-6), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: FOS: Computer and information sciences, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Initialization, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, 01 natural sciences, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 010309 optics, Neuroimaging, Robustness (computer science), 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Neural and Evolutionary Computing (cs.NE), ComputingMilieux_MISCELLANEOUS, Interpretability, Artificial neural network, Contextual image classification, business.industry, Deep learning, Image and Video Processing (eess.IV), Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing, 021001 nanoscience & nanotechnology, Visualization, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Artificial intelligence, 0210 nano-technology, business, computer
Abstract: International audience; The use of neural networks for diagnosis classification is becoming more and more prevalent in the medical imaging community. However, deep learning method outputs remain hard to explain. Another difficulty is to choose among the large number of techniques developed to analyze how networks learn, as all present different limitations. In this paper, we extended the framework of Fong and Vedaldi [IEEE International Conference on Computer Vision (ICCV), 2017] to visualize the training of convolutional neural networks (CNNs) on 3D quantitative neuroimaging data. Our application focuses on the detection of Alzheimer’s disease with gray matter probability maps extracted from structural MRI. We first assessed the robustness of the visualization method by studying the coherence of the longitudinal patterns and regions identified by the network. We then studied the stability of the CNN training by computing visualization-based similarity indexes between different re-runs of the CNN. We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer’s disease and that the visualization approach extract coherent longitudinal patterns. We also showed that the CNN training is not stable and that the areas identified mainly depend on the initialization and the training process. This issue may exist in many other medical studies using deep learning methods on datasets in which the number of samples is too small and the data dimension is high. This means that it may not be possible to rely on deep learning to detect stable regions of interest in this field yet.
Published: 2019
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29. Plasma amyloid β 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer's disease

Author: Geoffroy Gagliardi, Roberto Ceravolo, Herman Depypere, Bruno Dubois, Fanny Mochel, Christiane Metzinger, George L. W. Perry, Marine Sole, Massimo S. Fiandaca, Amira Saidi, E. Cavedo, Patrizia Andrea Chiesa, Thiebaud de Schotten M, Juan I. Castrillo, Jean Lorenceau, Jeffrey L. Cummings, Craig W. Ritchie, Ubaldo Bonuccelli, Francesco Cacciola, Arun L.W. Bokde, F. Lamari, Keith L. Black, Marion Haberkamp, Jean-Christophe Corvol, Ann De Vos, Maya Koronyo-Hamaoui, Filippo Sean Giorgi, Todd Langevin, H. Hampel, Richard Frank, Stéphane Epelbaum, Mohmed Surtee, Remy Genthon, Nadjia Younsi, Olaf Sporns, Harald Hampel, Marion Dubois, Filippo Baldacci, Lindsay A. Welikovitch, Karl Broich, Stéphane Lehéricy, Henrik Zetterberg, M.O. Habert, Manuela Graziani, Janet Woodcock, S. Lista, Aurélie Kas, Lon S. Schneider, Katia Andrade, Robert Nisticò, Simone Rossi, Foudil Lamari, Howard J. Federoff, Francis Nyasse, Enrica Cavedo, Lisi Flores Aguilar, Erfan Younesi, M.C. Potier, Seung Hyun Kim, Karl Herholz, Nadia Boukerrou, Lucile Megret, Catherine Poisson, Claudio Babiloni, A.C. Cuello, Dalila Mango, Antonio Melo dos Santos, Norbert Benda, Marcel Levy, A. Vergallo, Patrizia A. Chiesa, Sid E. O'Bryant, Marie Revillon, Nicola Toschi, Hugo Geerts, Maria Teresa Ferretti, Mark Mapstone, Kaj Blennow, Eugeen Vanmechelen, Simone Lista, Marie-Odile Habert, Marie-Claude Potier, Eric Karran, Nicolas Villain, Laurie Boukadida, Emiliano Santarnecchi, Yosef Koronyo, Olivier Colliot, Habib Benali, Hugo Bertin, Valentina Escott-Price, Andrea Duggento, Steven Verdooner, Andrea Vergallo, Christian Neri, Joel Bonheur, Francesco Garaci, Hovagim Bakardjian, Marion Houot, Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Adaptation Biologique et Vieillissement = Biological Adaptation and Ageing (B2A), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, 0301 basic medicine, Apolipoprotein E, Oncology, Epidemiology, [SDV]Life Sciences [q-bio], Simoa immunoassay, Disease, Cohort Studies, Classification and regression trees (CART), Cognition, 0302 clinical medicine, medicine.diagnostic_test, Health Policy, Amyloidosis, Settore FIS/07, Brain, Alzheimer's disease, 3. Good health, Psychiatry and Mental health, Positron emission tomography, Cohort, Female, Amyloid PET, Machine learning, Plasma amyloid β, Subjective memory complainers, medicine.medical_specialty, 03 medical and health sciences, Cellular and Molecular Neuroscience, Developmental Neuroscience, Alzheimer Disease, Memory, Internal medicine, medicine, Humans, Aged, Amyloid beta-Peptides, Receiver operating characteristic, business.industry, Clinical study design, medicine.disease, Peptide Fragments, Clinical trial, Cerebral Amyloid Angiopathy, 030104 developmental biology, Positron-Emission Tomography, Neurology (clinical), Geriatrics and Gerontology, business, Biomarkers, 030217 neurology & neurosurgery
Abstract: Introduction Blood-based biomarkers of pathophysiological brain amyloid β (Aβ) accumulation, particularly for preclinical target and large-scale interventions, are warranted to effectively enrich Alzheimer's disease clinical trials and management. Methods We investigated whether plasma concentrations of the Aβ 1–40 /Aβ 1–42 ratio, assessed using the single-molecule array (Simoa) immunoassay, may predict brain Aβ positron emission tomography status in a large-scale longitudinal monocentric cohort (N = 276) of older individuals with subjective memory complaints. We performed a hypothesis-driven investigation followed by a no-a-priori hypothesis study using machine learning. Results The receiver operating characteristic curve and machine learning showed a balanced accuracy of 76.5% and 81%, respectively, for the plasma Aβ 1–40 /Aβ 1–42 ratio. The accuracy is not affected by the apolipoprotein E ( APOE ) e4 allele, sex, or age. Discussion Our results encourage an independent validation cohort study to confirm the indication that the plasma Aβ 1–40 /Aβ 1–42 ratio, assessed via Simoa, may improve future standard of care and clinical trial design.
Published: 2019
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30. Presymptomatic spinal cord pathology in c9orf72 mutation carriers: a longitudinal neuroimaging study

Author: Olivier Colliot, Dario Saracino, Isabelle Le Ber, Agnès Camuzat, Mélanie Pélégrini-Issac, Véronique Marchand-Pauvert, Menghan Li, Daisy Rinaldi, François Salachas, Peter Bede, Mohamed-Mounir El Mendili, Giorgia Querin, Julien Cohen-Adad, Martin Catala, Pierre-François Pradat, Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Fédération des Maladies du Système Nerveux, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), École Polytechnique de Montréal (EPM), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This study was supported by the Assistance Publique – Hôpitaux de Paris (Clinical Research and Development Department) grant ANR/DGOS PRTS 2015 -2019 PrevDemAls (to I.L.B.), the 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-Investissements-Avenir Institut-Hospitalo-Universitaire-06), the Centre d’Investigation Clinique (CIC 1422), and the Centre pour l’Acquisition et le Traitement des Images (CATI) platform, at IHU-A-ICM, Paris, France. Peter Bede is supported by the Health Research Board (HRB EIA-2017-019), the Andrew Lydon scholarship, the Irish Institute of Clinical Neuroscience IICN − Novartis Ireland Research Grant, and the Iris O'Brien Foundation., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Colliot, Olivier, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Pathology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Longitudinal Studies, Prospective Studies, Amyotrophic lateral sclerosis, medicine.diagnostic_test, Middle Aged, 3. Good health, medicine.anatomical_structure, Neurology, Spinal Cord, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Frontotemporal Dementia, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], medicine.symptom, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Frontotemporal dementia, Adult, medicine.medical_specialty, Heterozygote, Neuroimaging, Asymptomatic, 03 medical and health sciences, Young Adult, Atrophy, Fractional anisotropy, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Aged, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, C9orf72 Protein, business.industry, Amyotrophic Lateral Sclerosis, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Magnetic resonance imaging, medicine.disease, Spinal cord, 030104 developmental biology, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Corticospinal tract, Asymptomatic Diseases, Mutation, Neurology (clinical), business, 030217 neurology & neurosurgery, Follow-Up Studies
Abstract: International audience; OBJECTIVE:C9orf72 hexanucleotide repeats expansions account for almost half of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases. Recent imaging studies in asymptomatic C9orf72 carriers have demonstrated cerebral white (WM) and gray matter (GM) degeneration before the age of 40 years. The objective of this study was to characterize cervical spinal cord (SC) changes in asymptomatic C9orf72 hexanucleotide carriers.METHODS:Seventy-two asymptomatic individuals were enrolled in a prospective study of first-degree relatives of ALS and FTD patients carrying the c9orf72 hexanucleotide expansion. Forty of them carried the pathogenic mutation (C9+ ). Each subject underwent quantitative cervical cord imaging. Structural GM and WM metrics and diffusivity parameters were evaluated at baseline and 18 months later. Data were analyzed in C9+ and C9- subgroups, and C9+ subjects were further stratified by age.RESULTS:At baseline, significant WM atrophy was detected at each cervical vertebral level in C9+ subjects older than 40 years without associated changes in GM and diffusion tensor imaging parameters. At 18-month follow-up, WM atrophy was accompanied by significant corticospinal tract (CST) fractional anisotropy (FA) reductions. Intriguingly, asymptomatic C9+ subjects older than 40 years with family history of ALS (as opposed to FTD) also exhibited significant CST FA reduction at baseline.INTERPRETATION:Cervical SC imaging detects WM atrophy exclusively in C9+ subjects older than 40 years, and progressive CST FA reductions can be identified on 18-month follow-up. Cervical SC magnetic resonance imaging readily captures presymptomatic pathological changes and disease propagation in c9orf72-associated conditions. ANN NEUROL 2019;86:158-167.
Published: 2019
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31. Neurite density is reduced in the presymptomatic phase of C9orf72 disease

Author: Anne Bertrand, Daisy Rinaldi, Olivier Colliot, Hui Zhang, Isabelle Le Ber, Alexandre Routier, Stanley Durrleman, Philippe Couratier, Florence Pasquier, Didier Hannequin, Daniel C. Alexander, Marion Houot, Jiaying Zhang, Junhao Wen, Sorbonne Université (SU), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Neuroépidémiologie Tropicale (NET), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), CHU Rouen, Normandie Université (NU), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de neurologie 1 [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: Pathology, medicine.medical_specialty, Neurite, business.industry, Frontotemporal lobar degeneration, Grey matter, medicine.disease, White matter, [STAT]Statistics [stat], 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, Atrophy, medicine.anatomical_structure, C9orf72, Medicine, Surgery, [INFO]Computer Science [cs], Neurology (clinical), Amyotrophic lateral sclerosis, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract: ObjectiveTo assess the added value of neurite orientation dispersion and density imaging (NODDI) compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to detect changes in presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation.MethodsThe PREV-DEMALS (Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis) study is a prospective, multicentre, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Sixty-seven participants (38 presymptomatic C9orf72 mutation carriers (C9+) and 29 non-carriers (C9−)) were included in the present cross-sectional study. Each participant underwent one single-shell, multishell diffusion MRI and three-dimensional T1-weighted MRI. Volumetric measures, DTI and NODDI metrics were calculated within regions of interest. Differences in white matter integrity, grey matter volume and free water fraction between C9+ and C9− individuals were assessed using linear mixed-effects models.ResultsCompared with C9−, C9+ demonstrated white matter abnormalities in 10 tracts with neurite density index and only 5 tracts with DTI metrics. Effect size was significantly higher for the neurite density index than for DTI metrics in two tracts. No tract had a significantly higher effect size for DTI than for NODDI. For grey matter cortical analysis, free water fraction was increased in 13 regions in C9+, whereas 11 regions displayed volumetric atrophy.ConclusionsNODDI provides higher sensitivity and greater tissue specificity compared with conventional DTI for identifying white matter abnormalities in the presymptomatic C9orf72 carriers. Our results encourage the use of neurite density as a biomarker of the preclinical phase.Trial registration numberNCT02590276.
Published: 2019
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32. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Author: Elina Thibeau-Sutre, Jorge Samper-González, Simona Bottani, Olivier Colliot, Didier Dormont, Alexandre Routier, Stanley Durrleman, Ninon Burgos, Mauricio Diaz-Melo, Junhao Wen, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001 - PRAIRIE IA - Paris Artificial Intelligence Research Institute (2019), PNRIA, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), THIBEAU-SUTRE, Elina, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Sorbonne Université (SU)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science - Machine Learning, Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Machine Learning (stat.ML), Health Informatics, Convolutional neural network, Overfitting, Machine learning, computer.software_genre, Machine Learning (cs.LG), 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Software, Statistics - Machine Learning, Alzheimer Disease, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Preprocessor, Humans, Radiology, Nuclear Medicine and imaging, 10. No inequality, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Radiological and Ultrasound Technology, business.industry, Model selection, Deep learning, Image and Video Processing (eess.IV), Brain, Alzheimer's disease classification, Electrical Engineering and Systems Science - Image and Video Processing, Modular design, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, Reproducibility, Support vector machine, Computer Vision and Pattern Recognition, Artificial intelligence, Neural Networks, Computer, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.
Published: 2019
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33. CATI: A Large Distributed Infrastructure for the Neuroimaging of Cohorts

Author: Urielle Thoprakarn, Jinpeng Li, Clara Fischer, Michel Bottlaender, Bruno Dubois, Lydie Edward, Antoine Grigis, Vincent Frouin, Catherine Champseix, Mélanie Pélégrini-Issac, Denis Rivière, Christine Delmaire, Chabha Azouani, Stéphane Lehéricy, Sullivan Marie, Vincent Bouteloup, Sylvain Poret, Carole Dufouil, Marie-Odile Habert, Grégory Operto, Jean-François Mangin, Marie Chupin, Ali Bouyahia, Leila Makkaoui, Yann Cointepas, Habib Benali, Regine Trebossen, Dimitri Papadopoulos Orfanos, Gaël Chételat, Bénédicte Batrancourt, Mathilde Bouin, Olivier Colliot, Cyril Poupon, Hugo Dary, Ludovic Fillon, Vincent Perlbarg, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Information Analysis and Management project of France Life Imaging, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Services de neuroradiologie [Lille], Hôpital Roger Salengro [Lille]-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut de neurosciences translationnelles de Paris (NeurATRIS - IHU-A-ICM), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service Hospitalier Frédéric Joliot (SHFJ), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidemiologie-Biostatistique [Bordeaux], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux Ségalen [Bordeaux 2], Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Neuropsychologie cognitive et neuroanatomie fonctionnelles de la mémoire humaine, Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris] (CENIR), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Service de médecine nucléaire [CHU Pitié-Salpétrière], Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Unité de recherche Neuroépidémiologie [CHU Pitié-Salpêtrière], Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC), Centre de Neuro-Imagerie de Recherche (CENIR), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Operto, Grégory, Service de Médecine nucléaire [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Inria de Paris, and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)
Subjects: 0301 basic medicine, Service (systems architecture), Large-scale studies, Knowledge management, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Information Dissemination, Neuroimaging, Neuroimaging biomarkers, Plan (drawing), Computer security, computer.software_genre, Multicenter protocols, Workflow, 03 medical and health sciences, 0302 clinical medicine, Service infrastructure, Humans, Multicenter Studies as Topic, Data mining, business.industry, General Neuroscience, Computational Biology, 3. Good health, Data sharing, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, 030104 developmental biology, Business, computer, 030217 neurology & neurosurgery, Software, Information Systems
Abstract: The CATI Consortium; International audience; This paper provides an overview of CATI, a platform dedicated to multicenter neuroimaging. Initiated by the French Alzheimer’s plan (2008–2012), CATI is a research project called on to provide service to other projects like an industrial partner. Its core mission is to support the neuroimaging of large populations, providing concrete solutions to the increasing complexity involved in such projects by bringing together a service infrastructure, the know-how of its expert academic teams and a large-scale, harmonized network of imaging facilities. CATI aims to make data sharing across studies easier and promotes sharing as much as possible. In the last 4 years, CATI has assisted the clinical community by taking charge of 35 projects so far and has emerged as a recognized actor at the national and international levels.
Published: 2016
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34. Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Author: Benedetta Bodini, Nicholas Ayache, Wen Wei, Emilie Poirion, Bruno Stankoff, Olivier Colliot, Stanley Durrleman, Wei, Wen, E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Multiple Sclerosis, Image Processing, FLAIR Synthesis, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Image processing, Fluid-attenuated inversion recovery, Convolutional neural network, 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], White matter, 03 medical and health sciences, 0302 clinical medicine, Nuclear magnetic resonance, Deep Learning, medicine, Medical imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Radiology, Nuclear Medicine and imaging, medicine.diagnostic_test, business.industry, Pulse (signal processing), 3D Fully Convolutional Networks, Magnetic resonance imaging, Pulse sequence, medicine.anatomical_structure, 030220 oncology & carcinogenesis, MR Images, business
Abstract: International audience; Multiple sclerosis (MS) is a white matter (WM) disease characterized by the formation of WM lesions, which can be visualized by magnetic resonance imaging (MRI). The fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is used clinically and in research for the detection of WM lesions. However, in clinical settings, some MRI pulse sequences can be missing because of various constraints. We propose to use 3D fully convolutional neural networks to predict FLAIR pulse sequences from other MRI pulse sequences. In addition, we evaluate the contribution of each input pulse sequence with a pulse-sequence-specific saliency map. Our approach is tested on a real multiple sclerosis image dataset and evaluated by comparing our approach to other methods and by assessing the lesion contrast in the synthetic FLAIR pulse sequence. Both the qualitative and quantitative results show that our method is competitive for FLAIR synthesis.
Published: 2019
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35. Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

Author: Stéphane Epelbaum, Ninon Burgos, Theodoros Evgeniou, Jorge Samper-González, Marie-Odile Habert, Olivier Colliot, Simona Bottani, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut Européen d'administration des Affaires (INSEAD), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Samper-Gonzalez, Jorge
Subjects: Computer science, SVM, Feature extraction, 02 engineering and technology, [INFO] Computer Science [cs], Machine learning, computer.software_genre, 01 natural sciences, 010309 optics, Multimodal data, Neuroimaging, 0103 physical sciences, [INFO]Computer Science [cs], business.industry, Alzheimer's disease, Random forests, 021001 nanoscience & nanotechnology, 3. Good health, Random forest, Support vector machine, Statistical classification, Artificial intelligence, 0210 nano-technology, business, computer, Classifier (UML)
Abstract: International audience; Various machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer's disease (AD) using neuroimaging data. Even though the vast majority of these works use the public dataset ADNI, reproducing their results is complicated because they often do not make available elements that are essential for reproducibility, such as selected participants and input data, image preprocessing and cross-validation procedures. Comparability is also an issue. Specially, the influence of different components like preprocessing, feature extraction or classification algorithms on the performance is difficult to evaluate. Finally, these studies rarely compare their results to models built from clinical data only, a critical aspect to demonstrate the utility of neuroimaging. In our previous work, 1, 2 we presented a framework for reproducible and objective classification experiments in AD, that included automatic conversion of ADNI database into the BIDS community standard, image preprocessing pipelines and machine learning evaluation. We applied this framework to perform unimodal classifications of T1 MRI and FDG-PET images. In the present paper, we extend this work to the combination of multimodal clinical and neuroimaging data. All experiments are based on standard approaches (namely SVM and random forests). In particular, we assess the added value of neuroimaging over using only clinical data. We first demonstrate that using only demographic and clinical data (gender, education level, MMSE, CDR sum of boxes, ADASCog) results in a balanced accuracy of 75% (AUC of 0.84). This performance is higher than that of standard neuroimaging-based classifiers. We then propose a simple trick to improve the performance of neuroimaging-based classifiers: training from AD patients and controls (rather than from MCI patients) improves the performance of FDG-PET classification by 5 percent points, reaching the level of the clinical classifier. Finally, combining clinical and neuroimaging data, prediction results further improved to 80% balanced accuracy and an AUC of 0.88). These prediction accuracies, obtained in a reproducible way, provide a base to develop on top of it and, to compare against, more sophisticated methods. All the code of the framework and the experiments is publicly available at https://github.com/aramis-lab/AD-ML
Published: 2019
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36. Multi-modal brain fingerprinting: a manifold approximation based framework

Author: Laurent Chauvin, Kuldeep Kumar, Olivier Colliot, Matthew Toews, Christian Desrosiers, Ecole de Technologie Supérieure [Montréal] (ETS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Colliot, Olivier
Subjects: 0301 basic medicine, Male, sMRI-dMRI-rfMRI, Hemisphere asymmetry, rfMRI netmats, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Individuality, Twins, Cohort Studies, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Discriminative model, Manifold approximation, 0303 health sciences, Human Connectome Project, Brain, Magnetic Resonance Imaging, Manifold, Neurology, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], MAP, Embedding, Graph (abstract data type), Female, Brain fingerprint, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Adult, Cognitive Neuroscience, Diffusion MRI, 03 medical and health sciences, Young Adult, Connectome, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, HCP Twin data, 030304 developmental biology, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Resting state fMRI, business.industry, Multi-modal data, Functional Neuroimaging, Siblings, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Brain fingerprinting, Structural MRI, 030104 developmental biology, Modal, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Diffusion Magnetic Resonance Imaging, Multi-modal, Artificial intelligence, business, Bag-of-Features, 030217 neurology & neurosurgery
Abstract: This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features, which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold, using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis. In addition, taking inspiration from the computer vision community, the proposed rank retrieval evaluation based on the task of twin/sibling identification and using Mean Average Precision (MAP) can be used for a standardized comparison of future brain fingerprints.
Published: 2018
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37. Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

Author: Joan Alexis Glaunès, Vincent Frouin, Roberto Toro, Gunter Shumann, Marie Chupin, Jean-Baptiste Poline, Claire Cury, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Université Sorbonne Paris Cité (USPC), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), The research leading to these results has received funding from ANR (project HM-TC, grant number ANR-09-EMER-006, and project KaraMetria, grant number ANR-09-BLAN-0332), from the CATI Project (Fondation Plan Alzheimer) and from the program Investissements d'avenir ANR-10-IAIHU-06.IMAGEN was supported by the European Union-funded FP6 (LSHM-CT-2007-037286), the FP7 projects IMAGEMEND (602450) and MATRICS (603016), and the Innovative Medicine Initiative Project EU-AIMS (115300-2), Medical Research Council Programme Grant Developmental pathways into adolescent substance abuse (93558), the NIHR Biomedical Research Centre Mental Health as well as the Swedish funding agency FORMAS. Further support was provided by the Bundesministerium für Bildung und Forschung (eMED SysAlc, AERIAL, 1EV0711)., ANR-09-EMER-0006,HM-TC,Modélisation des interactions entre l'hippocampe et le cortex dans ' la conscience temporelle', établie à partir de l'imagerie cérébrale multimodale anatomique et fonctionnelle(2009), ANR-09-BLAN-0332,KaraMetria,Karametria (Analyse statistique des structures cérébrales)(2009), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 602450,EC:FP7:HEALTH,FP7-HEALTH-2013-INNOVATION-1,IMAGEMEND(2013), European Project: 603016,EC:FP7:HEALTH,FP7-HEALTH-2013-INNOVATION-1,MATRICS(2014), European Project: 115300,EC:FP7:SP1-JTI,IMI-JU-03-2010,EU-AIMS(2012), Cury, Claire, Domaines émergents - Modélisation des interactions entre l'hippocampe et le cortex dans ' la conscience temporelle', établie à partir de l'imagerie cérébrale multimodale anatomique et fonctionnelle - - HM-TC2009 - ANR-09-EMER-0006 - EMER - VALID, Blanc - Karametria (Analyse statistique des structures cérébrales) - - KaraMetria2009 - ANR-09-BLAN-0332 - Blanc - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, IMAging GEnetics for MENtal Disorders - IMAGEMEND - - EC:FP7:HEALTH2013-10-01 - 2017-09-30 - 602450 - VALID, Multidisciplinary Approaches to Translational Research In Conduct Syndromes - MATRICS - - EC:FP7:HEALTH2014-03-01 - 2019-02-28 - 603016 - VALID, European Autism Interventions - A Multicentre Study for Developing New Medications - EU-AIMS - - EC:FP7:SP1-JTI2012-04-01 - 2017-03-31 - 115300 - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), CATI Multicenter Neuroimaging Platform (CATI), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES), ANR-10-IAHU-0006,IHU-A-ICM(2010), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Large deformation diffeomorphic metric mapping, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, IHI, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 02 engineering and technology, Imagen, Statistical shape analysis, Hippocampus, Kernel principal component analysis, 03 medical and health sciences, 0302 clinical medicine, Diffeomorphisms, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Riemannian barycentres, Original Research, business.industry, Atlas (topology), General Neuroscience, Template, Morphometry, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Centroids, Centroid, Pattern recognition, Shape analysis, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Computational anatomy, [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], 020201 artificial intelligence & image processing, Artificial intelligence, Diffeomorphism, Atlas, business, [MATH.MATH-DG] Mathematics [math]/Differential Geometry [math.DG], 030217 neurology & neurosurgery, LDDMM, Neuroscience, Shape analysis (digital geometry), MRI
Abstract: International audience; In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects.
Published: 2018
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38. Sulcal morphology in Alzheimer's disease: an effective marker of diagnosis and cognition

Author: Julien Lagarde, Maxime Bertoux, Lorraine Hamelin, Jean-François Mangin, Marie Sarazin, Marie Chupin, Meredith N. Braskie, Fabian Corlier, Paul M. Thompson, Michel Bottlaender, Olivier Colliot, Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Neurologie de la mémoire et du langage [CHU Ste Anne], Université Paris Descartes - Paris 5 (UPD5), Imagerie Moléculaire in Vivo (IMIV - U1023 - ERL9218), Service Hospitalier Frédéric Joliot (SHFJ), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imaging Genetics Center [Los Angeles, CA, USA] (Keck School of Medicine), University of Southern California (USC)-USC Stevens Neuroimaging and Informatics Institute [Los Angeles, CA, USA], Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), The study was founded by French Agence Nationale de la Recherche (ANR-07-LVIE-002-01), French Fondation Nationale de Gérontologie and MEDIPAR, French Health Ministry (PHRC-0053-N 2010), Institut Roche de Recherche et Médecine Translationnelle and Fondation pour la Recherche Médicale (to LH)., Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Lille, Inserm, Université de Lille, and Bertoux, Maxime
Subjects: 0301 basic medicine, Aging, Pathology, medicine.medical_specialty, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Diagnosis tool, Disease, 03 medical and health sciences, 0302 clinical medicine, Cognition, Alzheimer Disease, sulcal width, Medicine, Clinical significance, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV.MHEP.GEG] Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, data-driven science, business.industry, Surrogate endpoint, General Neuroscience, [SDV.MHEP.GEG]Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, Neuropsychology, Brain, Sulcus, cortical thickness, sulcal morphology, 3. Good health, 030104 developmental biology, medicine.anatomical_structure, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Csf biomarkers, Data-driven science, Sulcal width, Cortical thickness, Sulcal morphology, Alzheimer\u2019s disease, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Geriatrics and Gerontology, business, Alzheimer’s disease, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Developmental Biology
Abstract: Julien Lagarde and Fabian Corlier contributed equally.; International audience; Measuring the morphology of brain sulci has been recently proposed as a novel imaging approach in AD. We aimed to investigate the relevance of such an approach in AD, by exploring its (1) clinical relevance in comparison with traditional imaging methods, (2) relationship with amyloid deposition, (3) association with cognitive functions. Here, 51 patients (n=32 MCI/mild dementia-AD, n=19 moderate/severe dementia-AD) diagnosed according to clinical-biological criteria (CSF biomarkers and amyloid-PET) and 29 controls (with negative amyloid-PET) underwent neuropsychological and 3T-MRI examinations. Mean sulcal width (SW) & mean cortical thickness around the sulcus (CT-S) were automatically measured. We found higher SW and lower CT-S in AD patients than in controls. These differences were more pronounced at later stages of the disease and provided the best diagnostic accuracies among the imaging markers. Correlations were not found between CT-S or SW and amyloid deposition but between specific cognitive functions and regional CT-S/SW in key associated regions. Sulcal morphology is a good supporting diagnosis tool, that reflects the main cognitive impairments in AD. It could be considered as a good surrogate marker to evaluate the efficacy of new drugs.
Published: 2018
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39. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training

Author: Olivier Colliot, Bruno Stankoff, Emilie Poirion, Wen Wei, Stanley Durrleman, Nicholas Ayache, Benedetta Bodini, E-Patient : Images, données & mOdèles pour la médeciNe numériquE ( EPIONE ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Algorithms, models and methods for images and signals of the human brain ( ARAMIS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Inria de Paris, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], White matter, 03 medical and health sciences, Myelin, 0302 clinical medicine, medicine, Demyelinating disease, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Remyelination, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], medicine.diagnostic_test, [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, business.industry, Multiple sclerosis, Magnetic resonance imaging, medicine.disease, medicine.anatomical_structure, Positron emission tomography, Content (measure theory), business, Neuroscience, 030217 neurology & neurosurgery
Abstract: Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding of the physiopathology of MS, tracking progression and assessing treatment efficacy. Positron emission tomography (PET) with $[^{11} \mbox{C}] \mbox{PIB}$ has been proposed as a promising biomarker for measuring myelin content changes in-vivo in MS. However, PET imaging is expensive and invasive due to the injection of a radioactive tracer. On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely available technique, but existing MRI sequences do not provide, to date, a reliable, specific, or direct marker of either demyelination or remyelination. In this work, we therefore propose Sketcher-Refiner Generative Adversarial Networks (GANs) with specifically designed adversarial loss functions to predict the PET-derived myelin content map from a combination of MRI modalities. The prediction problem is solved by a sketch-refinement process in which the sketcher generates the preliminary anatomical and physiological information and the refiner refines and generates images reflecting the tissue myelin content in the human brain. We evaluated the ability of our method to predict myelin content at both global and voxel-wise levels. The evaluation results show that the demyelination in lesion regions and myelin content in normal-appearing white matter (NAWM) can be well predicted by our method. The method has the potential to become a useful tool for clinical management of patients with MS., Comment: Accepted by MICCAI2018
Published: 2018
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40. O3‐12‐04: THE NEURONAL COMPENSATION MODEL OF THE PRECLINICAL STAGE OF ALZHEIMER'S DISEASE: RESULTS FROM THE INSIGHT‐PRE AD STUDY

Author: Harald Hampel, Stéphane Epelbaum, Bruno Dubois, Olivier Colliot, Anne Bertrand, Marie-Odile Habert, Geoffroy Gagliardi, Marion Houot, Jean-François Mangin, Marie-Claude Potier, Francis Nyasse, Foudil Lamari, Hovagim Bakardjian, Federica Cacciamani, and Simone Lista
Subjects: Epidemiology, business.industry, Health Policy, Disease, Compensation (engineering), Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Medicine, Neurology (clinical), Geriatrics and Gerontology, Preclinical stage, business, Neuroscience
Published: 2018
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41. IC‐P‐113: USING DIFFUSION MRI FOR CLASSIFICATION AND PREDICTION OF ALZHEIMER'S DISEASE: A REPRODUCIBLE STUDY

Author: Sabrina Fontanella, Thomas Jacquemont, Stanley Durrleman, Junhao Wen, Jorge Samper-González, Ninon Burgos, Simona Bottani, Alexandre Routier, Anne Bertrand, and Olivier Colliot
Subjects: medicine.medical_specialty, Epidemiology, business.industry, Health Policy, Disease, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Medicine, Neurology (clinical), Radiology, Geriatrics and Gerontology, business, Diffusion MRI
Published: 2018
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42. Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

Author: Alexandre Bône, Stanley Durrleman, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Algorithms, models and methods for images and signals of the human brain ( ARAMIS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Inria de Paris, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Bône, Alexandre, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Mathematics - Differential Geometry, FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 01 natural sciences, Hierarchical database model, Methodology (stat.ME), 010104 statistics & probability, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0101 mathematics, Statistics - Methodology, [STAT.ME] Statistics [stat]/Methodology [stat.ME], [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, Parallel transport, business.industry, Sampling (statistics), [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Statistical model, Manifold, [ MATH.MATH-DG ] Mathematics [math]/Differential Geometry [math.DG], [ STAT.ME ] Statistics [stat]/Methodology [stat.ME], Differential Geometry (math.DG), [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], Trajectory, 020201 artificial intelligence & image processing, Artificial intelligence, [MATH.MATH-DG] Mathematics [math]/Differential Geometry [math.DG], business, Algorithm, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Distribution (differential geometry)
Abstract: International audience; We propose a method to learn a distribution of shape tra-jectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this trajectory both in terms of geometry and time dynamics. First, we formulate a non-linear mixed-effects statistical model as the combination of a generic statistical model for manifold-valued longitudinal data, a deformation model defining shape trajectories via the action of a finite-dimensional set of diffeomorphisms with a man-ifold structure, and an efficient numerical scheme to compute parallel transport on this manifold. Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape sampling, an adaptive scheme for proposal variances , and a log-likelihood tempering strategy to estimate our model. Third, we validate our algorithm on 2D simulated data, and then estimate a scenario of alteration of the shape of the hippocampus 3D brain structure during the course of Alzheimer's disease. The method shows for instance that hippocampal atrophy progresses more quickly in female subjects, and occurs earlier in APOE4 mutation carriers. We finally illustrate the potential of our method for classifying pathological trajectories versus normal ageing.
Published: 2018

43. P1-364: RADIOLOGICAL CLASSIFICATION OF DEMENTIA FROM ANATOMICAL MRI ASSISTED BY MACHINE LEARNING-DERIVED MAPS

Author: Anne Bertrand, Olivier Colliot, Stéphane Epelbaum, Jorge Samper-González, Bruno Dubois, Didier Dormont, Marion Houot, Béatrice Marro, Alexandre Morin, Marc Teichmann, Pierre Chagué, Sarah Fadili, Lionel Arrivé, and Paul Beunon
Subjects: medicine.medical_specialty, Epidemiology, business.industry, Health Policy, medicine.disease, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Radiological weapon, medicine, Dementia, Medical physics, Neurology (clinical), Geriatrics and Gerontology, business
Published: 2019
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44. Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease

Author: Joan Alexis Glaunès, Marie Chupin, Olivier Colliot, Claire Cury, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ), Algorithms, models and methods for images and signals of the human brain ( ARAMIS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Inria de Paris, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Pitié-Salpêtrière [APHP], Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Jacques-Louis Lions ( LJLL ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Service de neuroradiologie diagnostique et fonctionnelle [CHU Pitié-Salpêtrière], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Cury, Claire, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)
Subjects: Large deformation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Biomedical Engineering, Computational Mechanics, Alzheimer's Disease, Hippocampus, Diffeomorphism, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Centroid algorithm, Alzheimer's Disease Neuroimaging Initiative ADNI, Radiology, Nuclear Medicine and imaging, Statistical analysis, In patient, Memory test, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging, Shape Analysis, business.industry, Centroid, Pattern recognition, Control subjects, Computer Science Applications, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Artificial intelligence, business, LDDMM, 030217 neurology & neurosurgery, Shape analysis (digital geometry)
Abstract: International audience; This paper presents a new approach for template-based analysis of anatomical variability in populations,in the framework of Large Deformation Diffeomorphic Metric Mappings and mathematical currents. We propose a fast approach in which the template is computed using an diffeomorphic iterative centroid method. Statistical analysis is then performed on the initial momenta that define the deformations between the centroid and each individual subject. We applied the approach to study the variability of the hippocampus in 134 patients with Alzheimer’s disease (AD) and 160 elderly control subjects. We show that this approach can describe the main modes of variability of the two populations and can predict the performance to a memory test in AD patients.
Published: 2015
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45. Multi-Modal Analysis of Genetically-Related Subjects Using SIFT Descriptors in Brain MRI

Author: Kuldeep Kumar, Christian Desrosiers, Laurent Chauvin, Olivier Colliot, Matthew Toews, Ecole de Technologie Supérieure [Montréal] (ETS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris
Subjects: FOS: Computer and information sciences, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Computer Vision and Pattern Recognition (cs.CV), [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-invariant feature transform, Similarity measure, Quantitative Biology - Quantitative Methods, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Similarity (network science), [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0501 psychology and cognitive sciences, Quantitative Methods (q-bio.QM), Human Connectome Project, Modalities, business.industry, 05 social sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Contrast (statistics), Pattern recognition, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Graph (abstract data type), Neurons and Cognition (q-bio.NC), Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Diffusion MRI
Abstract: International audience; So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstruc-ture. However, due to the lack of a framework for comparing across multiple modalities, studies based on multi-modal data remain elusive. This paper presents a multi-modal analysis of genetically-related subjects to compare and contrast the information provided by various MRI modalities. The proposed framework represents MRI scans as bags of SIFT features, and uses these features in a nearest-neighbor graph to measure subject similarity. Experiments using the T1/T2-weighted MRI and diffusion MRI data of 861 Human Connectome Project subjects demonstrate strong links between the proposed similarity measure and genetic proximity.
Published: 2018
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46. Cognitive and neuroimaging features and brain Î²-amyloidosis in individuals at risk of Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study

Author: Foudil Lamari, Marion Houot, Habib Benali, Fanny Mochel, Christiane Metzinger, Catherine Poisson, Stéphane Epelbaum, Marcel Levy, Valentina La Corte, Claire Letondor, Agnès Michon, Geoffroy Gagliardi, Francis Nyasse, Pauline Glasman, Marie-Claude Potier, Filippo Baldacci, Valérie Simon, Alexis Genin, Olga Uspenskaya, Juliette Ly, Gaëlle Fontaine, Simone Lista, Bruno Dubois, Maya Kilani, Enrica Cavedo, Nadjia Younsi, Caroline Tandetnik, Jean-François Mangin, Alexandra Auffret, Rachel Schindler, Marie-Odile Habert, Katia Santos-Andrade, Marine Sole, Antonio Melo dos Santos, Marion Dubois, Aurélie Kas, Anne Bertrand, Katrine Rojkova, Laurie Boukadida, Marie Revillon, Laure Seux, Stanley Durrleman, Marc Teichmann, Harald Hampel, Olivier Colliot, Stephie Ratovohery, Remy Genthon, Navichka Jungalee, Federica Cacciamani, Michel Thiebaut de Shotten, Ismahane Benakki, Patrizia A. Chiesa, Perrine Roy, Hovagim Bakardjian, Ornella Makiese, Luce Dauphinot, Christelle Audrain, Hugo Bertin, Mark Lowrey, Stéphane Lehéricy, Centre des Maladies Cognitives et Comportementales [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), IQVIA, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Biochimie Métabolique [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, Clinical Dementia Rating, Brain Structure and Function, Neuroimaging, Neuropsychological Tests, 03 medical and health sciences, 0302 clinical medicine, Cognition, Functional neuroimaging, Alzheimer Disease, Risk Factors, Internal medicine, medicine, Humans, Longitudinal Studies, Cognitive decline, Aged, Aged, 80 and over, Amyloid beta-Peptides, business.industry, Amyloidosis, [SCCO.NEUR]Cognitive science/Neuroscience, Neuropsychology, Brain, Actigraphy, medicine.disease, Magnetic Resonance Imaging, 3. Good health, 030104 developmental biology, Positron-Emission Tomography, Female, Neurology (clinical), business, 030217 neurology & neurosurgery, Executive dysfunction
Abstract: For the INSIGHT-preAD study group; International audience; Improved understanding is needed of risk factors and markers of disease progression in preclinical Alzheimer's disease. We assessed associations between brain β-amyloidosis and various cognitive and neuroimaging parameters with progression of cognitive decline in individuals with preclinical Alzheimer's disease.The INSIGHT-preAD is an ongoing single-centre observational study at the Salpêtrière Hospital, Paris, France. Eligible participants were age 70–85 years with subjective memory complaints but unimpaired cognition and memory (Mini-Mental State Examination [MMSE] score ≥27, Clinical Dementia Rating score 0, and Free and Cued Selective Reminding Test [FCSRT] total recall score ≥41). We stratified participants by brain amyloid β deposition on 18F-florbetapir PET (positive or negative) at baseline. All patients underwent baseline assessments of demographic, cognitive, and psychobehavioural, characteristics, APOE ε4 allele carrier status, brain structure and function on MRI, brain glucose-metabolism on 18F-fluorodeoxyglucose (18F-FDG) PET, and event-related potentials on electroencephalograms (EEGs). Actigraphy and CSF investigations were optional. Participants were followed up with clinical, cognitive, and psychobehavioural assessments every 6 months, neuropsychological assessments, EEG, and actigraphy every 12 months, and MRI, and 18F-FDG and 18F-florbetapir PET every 24 months. We assessed associations of amyloid β deposition status with test outcomes at baseline and 24 months, and with clinical status at 30 months. Progression to prodromal Alzheimer's disease was defined as an amnestic syndrome of the hippocampal type From May 25, 2013, to Jan 20, 2015, we enrolled 318 participants with a mean age of 76·0 years (SD 3·5). The mean baseline MMSE score was 28·67 (SD 0·96), and the mean level of education was high (score >6 [SD 2] on a scale of 1–8, where 1=infant school and 8=higher education). 88 (28%) of 318 participants showed amyloid β deposition and the remainder did not. The amyloid β subgroups did not differ for any psychobehavioural, cognitive, actigraphy, and structural and functional neuroimaging results after adjustment for age, sex, and level of education More participants positive for amyloid β deposition had the APOE ε4 allele (33 [38%] vs 29 [13%], p
Published: 2018
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47. White Matter Fiber Segmentation Using Functional Varifolds

Author: Pietro Gori, Stanley Durrleman, Benjamin Charlier, Christian Desrosiers, Olivier Colliot, Kuldeep Kumar, Ecole de Technologie Supérieure [Montréal] (ETS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Colliot, Olivier, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris
Subjects: FOS: Computer and information sciences, Similarity (geometry), Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer Vision and Pattern Recognition (cs.CV), [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Quantitative Biology - Quantitative Methods, Distance measures, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, Fiber, Quantitative Methods (q-bio.QM), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, business.industry, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], FOS: Biological sciences, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Quantitative Biology - Neurons and Cognition, Metric (mathematics), 020201 artificial intelligence & image processing, Neurons and Cognition (q-bio.NC), Artificial intelligence, Focus (optics), business, Neural coding, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Tractography
Abstract: International audience; The extraction of fibers from dMRI data typically produces a large number of fibers, it is common to group fibers into bundles. To this end, many specialized distance measures, such as MCP, have been used for fiber similarity. However, these distance based approaches require point-wise correspondence and focus only on the geometry of the fibers. Recent publications have highlighted that using microstructure measures along fibers improves tractography analysis. Also, many neurodegenerative diseases impacting white matter require the study of microstructure measures as well as the white matter geometry. Motivated by these, we propose to use a novel computational model for fibers, called functional varifolds, characterized by a metric that considers both the geometry and microstructure measure (e.g. GFA) along the fiber pathway. We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
Published: 2017

48. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials

Author: Didier Dormont, Olivier Colliot, Bruno Dubois, Stanley Durrleman, Harald Hampel, Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), AXA Research Fund, Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de Neuroradiologie [CHU Pitié-Salpêtrière], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Ansart, Manon, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Inria de Paris
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], medicine.medical_specialty, Disease stages, Bioinformatics, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Medicine, 0101 mathematics, business.industry, Amyloidosis, Neuropsychology, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], 3. Good health, Random forest, Data set, Clinical trial, Binary classification, Radiology, business, 030217 neurology & neurosurgery
Abstract: International audience; We propose a method for selecting pre-symptomatic subjects likely to have amyloid plaques in the brain, based on the automatic analysis of neuropsychological and MRI data and using a cross-validated binary classifier. By avoiding systematic PET scan for selecting subjects, it reduces the cost of forming cohorts of subjects with amyloid plaques for clinical trials, by scanning fewer subjects but increasing the number of recruitments. We validate our method on three cohorts of subjects at different disease stages, and compare the performance of six classifiers, showing that the random forest yields good results more consistently, and that the method generalizes well when tested on an unseen data set.
Published: 2017

49. Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

Author: Kaleem Siddiqi, Matthew Toews, Christian Desrosiers, Kuldeep Kumar, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Ecole de Technologie Supérieure [Montréal] (ETS), Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Colliot, Olivier, Service de Neuroradiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Service de neuroradiologie diagnostique et fonctionnelle [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: White matter geometry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, Feature vector, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Pooling, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Neuroimaging, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Sparse code pooling, Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer vision, Mathematics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Human Connectome Project, Fiber (mathematics), business.industry, Fingerprint (computing), [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Brain, HCP, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, dMRI, White Matter, Twin data, Diffusion Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Neurology, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Bundle, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Data analysis, Fiber trajectories, Artificial intelligence, Subject fingerprint, business, Neural coding, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract: International audience; White matter characterization studies use the information provided by diffusion magnetic resonance imaging (dMRI) to draw cross-population inferences. However, the structure, function, and white matter geometry vary across individuals. Here, we propose a subject fingerprint, called Fiberprint, to quantify the individual uniqueness in white matter geometry using fiber trajectories. We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary. A subject fingerprint is then generated by applying a pooling function for each bundle, thus providing a vector of bundle-wise features describing a particular subject's white matter geometry. These features encode unique properties of fiber trajectories, such as their density along prominent bundles. An analysis of data from 861 Human Connectome Project subjects reveals that a fingerprint based on approximately 3000 fiber trajectories can uniquely identify exemplars from the same individual. We also use fingerprints for twin/sibling identification, our observations consistent with the twin data studies of white matter integrity. Our results demonstrate that the proposed Fiberprint can effectively capture the variability in white matter fiber geometry across individuals, using a compact feature vector (dimension of 50), making this framework particularly attractive for handling large datasets.
Published: 2017
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50. [P3–364]: ACCELERATED SUBCORTICAL ATROPHY DURING AGING IN PRESYMPTOMATIC CARRIERS OF C9ORF72 MUTATION

Author: Florence Pasquier, Agnès Camuzat, Anne Bertrand, Daisy Rinaldi, Junhao Wen, Alexandre Routier, Sabrina Fontanella, Philippe Couratier, Olivier Colliot, Alexis Brice, Stanley Durrleman, Olivier Martinaud, and Isabelle Le Ber
Subjects: Pathology, medicine.medical_specialty, Epidemiology, business.industry, Health Policy, Subcortical atrophy, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, C9orf72, Mutation (genetic algorithm), medicine, Neurology (clinical), Geriatrics and Gerontology, business
Published: 2017
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