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1. Loss of glucocorticoid receptor phosphorylation contributes to cognitive and neurocentric damages of the amyloid-β pathway

Author: Yann Dromard, Margarita Arango-Lievano, Amelie Borie, Maheva Dedin, Pierre Fontanaud, Joan Torrent, Michael J. Garabedian, Stephen D. Ginsberg, Freddy Jeanneteau, Institut de Génomique Fonctionnelle (IGF), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), BioCampus Montpellier (BCM), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des Neurosciences de Montpellier (INM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), New York University School of Medicine (NYU Grossman School of Medicine), Nathan Kline Institute, BioCampus (BCM), New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Guerineau, Nathalie C., and freddy, jeanneteau
Subjects: Hydrocortisone, [SDV]Life Sciences [q-bio], Mice, Transgenic, Neuroimaging, Glucocorticoid receptor, Pathology and Forensic Medicine, Cellular and Molecular Neuroscience, Amyloid beta-Protein Precursor, Mice, Cognition, Receptors, Glucocorticoid, Alzheimer Disease, Memory, Animals, Humans, Receptor, trkB, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Phosphorylation, Amyloid beta-Peptides, Brain-Derived Neurotrophic Factor, Spine dynamics, [SDV] Life Sciences [q-bio], Disease Models, Animal, BDNF, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical)
Abstract: International audience; AbstractAberrant cortisol and activation of the glucocorticoid receptor (GR) play an essential role in age-related progression of Alzheimer's disease (AD). However, the GR pathways required for influencing the pathobiology of AD dementia remain unknown. To address this, we studied an early phase of AD-like progression in the well-established APP/PS1 mouse model combined with targeted mutations in the BDNF-dependent GR phosphorylation sites (serines 134/267) using molecular, behavioral and neuroimaging approaches. We found that disrupting GR phosphorylation (S134A/S267A) in mice exacerbated the deleterious effects of the APP/PS1 genotype on mortality, neuroplasticity and cognition, without affecting either amyloid-β deposition or vascular pathology. The dynamics, maturation and retention of task-induced new dendritic spines of cortical excitatory neurons required GR phosphorylation at the BDNF-dependent sites that amyloid-β compromised. Parallel studies in postmortem human prefrontal cortex revealed AD subjects had downregulated BDNF signaling and concomitant upregulated cortisol pathway activation, which correlated with cognitive decline. These results provide key evidence that the loss of neurotrophin-mediated GR phosphorylation pathway promotes the detrimental effects of the brain cortisol response that contributes to the onset and/or progression of AD dementia. These findings have important translational implications as they provide a novel approach to treating AD dementia by identifying drugs that increase GR phosphorylation selectively at the neurotrophic sites to improve memory and cognition.
Published: 2022
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2. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Author: Sook-Lei Liew, Bethany P. Lo, Miranda R. Donnelly, Artemis Zavaliangos-Petropulu, Jessica N. Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P. Simon, Julia M. Juliano, Anisha Suri, Zhizhuo Wang, Aisha Abdullah, Jun Kim, Tyler Ard, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Lei Cao, Jessica M. Cassidy, Valentina Ciullo, Adriana B. Conforto, Steven C. Cramer, Rosalia Dacosta-Aguayo, Ezequiel de la Rosa, Martin Domin, Adrienne N. Dula, Wuwei Feng, Alexandre R. Franco, Fatemeh Geranmayeh, Alexandre Gramfort, Chris M. Gregory, Colleen A. Hanlon, Brenton G. Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Jan S. Kirschke, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, Maria Mataró, Feroze B. Mohamed, Jan E. Nordvik, Gilsoon Park, Amy Pienta, Fabrizio Piras, Shane M. Redman, Kate P. Revill, Mauricio Reyes, Andrew D. Robertson, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Alison Sweet, Maria Telenczuk, Gregory Thielman, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Kristin A. Wong, Chunshui Yu, University of Southern California (USC), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Emory University School of Medicine, Emory University [Atlanta, GA], University of British Columbia [Vancouver], University of Melbourne, Child Mind Institute, Department Biostatistics University of North Carolina, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Universidade de São Paulo = University of São Paulo (USP), University of California (UC), University of Barcelona, Technische Universität München = Technical University of Munich (TUM), Universität Greifswald - University of Greifswald, University of Texas at Austin [Austin], Duke University [Durham], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU Grossman School of Medicine), Imperial College London, Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Medical University of South Carolina [Charleston] (MUSC), Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, University of South Australia [Adelaide], The Florey Institute of Neuroscience and Mental Health, Tianjin University (TJU), University of Toronto, Universitat de Barcelona (UB), Oslo Metropolitan University (OsloMet), University of Michigan [Ann Arbor], University of Michigan System, University of Bern, University of Waterloo [Waterloo], Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Oslo University Hospital [Oslo], Supported by The European Research Council under the European Union’s Horizon 2020 research and Innovation program (ERC StG, Grant 802998)., Liew, Sook-Lei, Lo, Bethany P, Donnelly, Miranda R, Zavaliangos-Petropulu, Artemis, Hordacre, Brenton G, and Winstein, Carolee J
Subjects: accurate image processing, Statistics and Probability, Image Processing, 610 Medicine & health, Neuroimaging, Bioengineering, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Library and Information Sciences, Education, Computer-Assisted, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, stroke rehabilitation, [STAT.AP]Statistics [stat]/Applications [stat.AP], segmented lesion, Neurosciences, Brain, ATLAS, Magnetic Resonance Imaging, Computer Science Applications, Stroke, Networking and Information Technology R&D (NITRD), 570 Life sciences, biology, Statistics, Probability and Uncertainty, Algorithms, Information Systems
Abstract: Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.
Published: 2022
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3. Biological constraints on configural odour mixture perception

Author: Gérard Coureaud, Thierry Thomas-Danguin, Jean-Christophe Sandoz, Donald A. Wilson, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Dijon, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Bourgogne Franche-Comté [COMUE] (UBFC), Evolution, génomes, comportement et écologie (EGCE), Institut de Recherche pour le Développement (IRD)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Agence Nationale de la Recherche (ANR-2010-JCJC-1410-1, ANR-17-CE20-003) + FEDER + CNRS +National Institutes of Health (R01-AG037693)., ANR-17-CE20-0003,Bee-o-CHOC,Déchiffrer le code olfactif des abeilles : de la communication sexuelle aux comportements sociaux(2017), Coureaud, Gérard, and Déchiffrer le code olfactif des abeilles : de la communication sexuelle aux comportements sociaux - - Bee-o-CHOC2017 - ANR-17-CE20-0003 - AAPG2017 - VALID
Subjects: [SDV.BA] Life Sciences [q-bio]/Animal biology, Physiology, Rodentia, Aquatic Science, Elemental perception, Mice, Species Specificity, Odour-guided behaviour, Animals, Molecular Biology, Ecology, Evolution, Behavior and Systematics, [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.BA]Life Sciences [q-bio]/Animal biology, [SCCO.NEUR] Cognitive science/Neuroscience, Olfactory Perception, Invertebrates, Smell, Comparative olfaction, Animals, Newborn, Insect Science, Odorants, Odour object, Vertebrates, Commentary, Animal Science and Zoology, Rabbits
Abstract: Animals, including humans, detect odours and use this information to behave efficiently in the environment. Frequently, odours consist of complex mixtures of odorants rather than single odorants, and mixtures are often perceived as configural wholes, i.e. as odour objects (e.g. food, partners). The biological rules governing this ‘configural perception’ (as opposed to the elemental perception of mixtures through their components) remain weakly understood. Here, we first review examples of configural mixture processing in diverse species involving species-specific biological signals. Then, we present the original hypothesis that at least certain mixtures can be processed configurally across species. Indeed, experiments conducted in human adults, newborn rabbits and, more recently, in rodents and honeybees show that these species process some mixtures in a remarkably similar fashion. Strikingly, a mixture AB (A, ethyl isobutyrate; B, ethyl maltol) induces configural processing in humans, who perceive a mixture odour quality (pineapple) distinct from the component qualities (A, strawberry; B, caramel). The same mixture is weakly configurally processed in rabbit neonates, which perceive a particular odour for the mixture in addition to the component odours. Mice and honeybees also perceive the AB mixture configurally, as they respond differently to the mixture compared with its components. Based on these results and others, including neurophysiological approaches, we propose that certain mixtures are convergently perceived across various species of vertebrates/invertebrates, possibly as a result of a similar anatomical organization of their olfactory systems and the common necessity to simplify the environment's chemical complexity in order to display adaptive behaviours.
Published: 2022
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4. How to remove or control confounds in predictive models, with applications to brain biomarkers

Author: Darya Chyzhyk, Gaël Varoquaux, Michael Milham, Bertrand Thirion, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données (SODA), Inria Saclay - Ile de France, Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, MIND, ANR-17-CE23-0018,DirtyData,Intégration et nettoyage de données pour l'analyse statistique(2017), and European Project: 826421,TVB-Cloud (2018)
Subjects: Machine Learning, phenotype, statistical testing, confound, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Brain, Humans, subsampling, biomarkers, Health Informatics, deconfounding, predictive models, Computer Science Applications
Abstract: Background With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers. Results Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds. Conclusions Cross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.
Published: 2022
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5. Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging

Author: David C. Van Essen, Caspar M. Schwiedrzik, Takuro Ikeda, Shaomin Zhang, Marcello G. P. Rosa, Ning Liu, Aidan Murphy, Li Min Chen, Pinglei Bao, Julia Lehman, Yuki Hori, Pengcheng Li, Julien Vezoli, Peter H. Rudebeck, Yao Meng, Julian 'Bene' Ramirez, Pierre Pouget, Guillermo Gallardo, Rogier B. Mars, Charles E. Schroeder, Minqing Jiang, Steve Frey, Michael P. Milham, Mohammad Hadi Aarabi, Pascal Belin, Patrick Friedrich, Bichan Wu, Hector Figueroa, Ye He, Charles L. Wilson, Melanie Wilke, Eunha Baeg, Fadila Hadj-Bouziane, Danny Garside, Marco Pagani, Ting-Yat Wong, Igor Kagan, Abdelhadi Essamlali, Bharat B. Biswal, Wasana Ediri Arachchi, Julio Villalon, Zheng Wang, Kacie Dougherty, Neo Sunhang Shi, Luciano Simone, Roberto Toro, Benjamin Jung, Masaki Fukunaga, Zhanguang Zuo, Loïc Magrou, Xiaowei Song, Kadharbatcha S. Saleem, Michele A. Basso, Eduardo A. Garza-Villarreal, Chihiro Yokoyama, Aaron Tanenbaum, Brian E. Russ, Alexandre Rosa Franco, Alison R. Weiss, Isabel Restrepo, Alan C. Evans, Lixia Gao, Nobuyuki Kimura, Augix Guohua Xu, Piotr Majka, Colline Poirier, Justine Cléry, Bassem Hiba, Alessandro Gozzi, Xiaojie Wang, Nick Upright, Stan Colcombe, Yang Gao, Won Mok Shim, Eduardo Rojas Hortelano, Takuya Hayashi, Anna S. Mitchell, Andrew F. Rossi, Itamar Kahn, Jorge Jaramillo, Henry C. Evrard, Xin Yumeng, Gregory Kiar, Sean Froudist-Walsh, Elise Roger, Roberto A. Gulli, Yufan Wang, Damien A. Fair, Yuguang Zhao, Stephen J. Sawiak, Boris C. Bernhardt, Ulysse Klatzmann, Ashkan Alvand, Kep Kee Loh, David Schaeffer, Virginie Sivan, Daniel S. Margulies, Carly M. Drzewiecki, Tomoko Sakai, Ting Xu, Cirong Liu, Essa Yacoub, Theresa M. Desrochers, Seok-Jun Hong, Sethu Boopathy, Reza Azadi, Lu Yuheng, Aarit Ahuja, Zhifeng Liang, Elena Borra, Fernanda Ponce, Robert Dahnke, Julien Sein, Li Deying, Jitendra Sharma, A.J. Mitchell, Roger Little, Luqi Cheng, Du Xiao, Choong-Wan Woo, Xinhui Li, Chris Petkov, Ruiliang Bai, D Zaldivar, Sheyla Mejia, Haidong D. Lu, Nikoloz Sirmpilatze, Diego Emanuel Ortuzar Martinez, Suzanne N. Haber, Catherine Elorette, Yue Cui, Michael Hawrylycz, Jerome Sallet, Wim Vanduffel, Daniel R. Glen, Ralph Adolphs, Dongrong Xu, Simon Clavagnier, Rakshit Dadarwal, Marzio Gerbella, Hannah Doyle, Ningrong Ye, Xiaojin Liu, Xinyu Liu, Quansheng He, Christopher R. Madan, Vikas Pareek, James Cavanaugh, Sze Chai Kwok, Zhang Ying, Sam Vickery, Xiaoguang Tian, Zhou Xufeng, Bevil R. Conway, Mark Postans, Wei-an Sheng, Gianfranco Chavez, Rober Boshra, Yuki Kikuchi, Michael Ortiz-Rios, Céline Amiez, Felix Hoffstaedter, Elizabeth A. Buffalo, Amy Howard, Hsin-Yi Lai, Marianne Duyck, Samy Rima, Froesel Mathilda, Towela Mvula, Guilherme Freches, Alfonso Fajardo, Maria de la Iglesia-Vaya, Ana Rita Ribeiro Gomes, Xiongjie Yu, Afonso C. Silva, Andrew S. Fox, Long Cao, Anna W. Roe, Meizhen Qian, David Meunier, Erika Raven, Nicola Palomero-Gallagher, Jordy Tasserie, Joonas A. Autio, Francois Chouinard-Decorte, Hank P. Jedema, Shasha Yue, Xinjian Li, Xiaodong Chen, Kathleen Rockland, Satoka Hashimoto Fujimoto, Amiez Celine, Melina Cordeau, Olivier Coulon, Ravi S. Menon, Sandra Gonzalez Torrecilla, Bjørg Elisabeth Kilavik, Adam Messinger, Hecheng Jin, Steven Giavasis, Pierce Perkins, Conor Liston, Yujie Hou, Jakob Seidlitz, Kelly Shen, Yvonne Bennett, Franck Lamberton, Maxime Gaudet-Trafit, Suliann Ben Hamed, Chris Klink, Sabine Kastner, Lucas R. Trambaiolli, Lucija Jankovic-Rapan, Atsushi Fujimoto, Nadira Yusif Rodriguez, Maeva Gacoin, Amir Shmuel, Katja Heuer, Austin K. Behel, Susann Boretius, Paul A. Taylor, Child Mind Institute, Institute of Neurosciences and Psychology [Glasgow], University of Glasgow, Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), University of Tübingen, Oregon Health and Science University [Portland] (OHSU), New York University [New York] (NYU), NYU System (NYU), Princeton Neuroscience Institute [Princeton], Consortium, PRIMatE Data and Resource Exchange (PRIME-DRE) Global Collaboration Workshop and, Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Nencki Institute of Experimental Biology, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Monash University [Clayton], Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, Donders Institute for Brain, Cognition and Behaviour, Radboud University [Nijmegen], National Institute of Mental Health (NIMH), Newcastle University [Newcastle], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Laboratorium voor Neuro- en Psychofysiologie, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), and Washington University in Saint Louis (WUSTL)
Subjects: Strategic planning, 0303 health sciences, Open science, biology, Action, intention, and motor control, Resource exchange, [SCCO.NEUR]Cognitive science/Neuroscience, Neuroscience(all), General Neuroscience, Nonhuman primate, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, biology.animal, Primate, Beacon - Precision Imaging, ddc:610, Psychology, Neuroscience, ComputingMilieux_MISCELLANEOUS, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Contains fulltext : 239574.pdf (Publisher’s version ) (Closed access) Open science initiatives are creating opportunities to increase research coordination and impact in nonhuman primate (NHP) imaging. The PRIMatE Data and Resource Exchange community recently developed a collaboration-based strategic plan to advance NHP imaging as an integrative approach for multiscale neuroscience. 5 p.
Published: 2022
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6. Atypical functional connectome hierarchy in autism

Author: Jonathan Smallwood, Richard A. I. Bethlehem, Reinder Vos de Wael, Sofie L. Valk, Daniel S. Margulies, Casey Paquola, Seok-Jun Hong, Sara Larivière, Boris C. Bernhardt, Adriana Di Martino, Michael P. Milham, Hong, Seok-Jun [0000-0002-1847-578X], Bethlehem, Richard AI [0000-0002-0714-0685], Apollo - University of Cambridge Repository, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], McGill University = Université McGill [Montréal, Canada], Child Mind Institute, University of Cambridge [UK] (CAM), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Institute of Neuroscience and Medicine [Jülich] (INM-1), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, 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), and University of York [York, UK]
Subjects: 0301 basic medicine, Adult, Male, Adolescent, Science, General Physics and Astronomy, Sensory system, behavioral disciplines and activities, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Motion, Young Adult, 0302 clinical medicine, Social cognition, mental disorders, medicine, Connectome, Humans, Autistic Disorder, lcsh:Science, Default mode network, Cerebral Cortex, Multidisciplinary, Hierarchy (mathematics), medicine.diagnostic_test, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Social Behavior Disorders, General Chemistry, medicine.disease, Magnetic Resonance Imaging, 030104 developmental biology, Autism spectrum disorder, Autism, Female, lcsh:Q, ddc:500, Nerve Net, Functional magnetic resonance imaging, Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract: One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance—specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features., Autism spectrum disorder (ASD) is associated with symptoms ranging from sensory hypersensitivity to social difficulties. Here, the authors provide evidence of atypical connectivity transitions between sensory and higher-order cortical areas in people with ASD, which could underlie the diverse symptoms.
Published: 2019
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7. A collaborative resource platform for non-human primate neuroimaging

Author: Rogier B. Mars, Benjamin Jung, Ting Xu, Cirong Liu, R. Austin Benn, Jordy Tasserie, Henry C. Evrard, Adam Messinger, Steven Giavasis, Michael P. Milham, Kep Kee Loh, Bastien Cagna, Pamela Garcia-Saldivar, Daniel R. Glen, Marcello G. P. Rosa, Lynn Uhrig, Jakob Seidlitz, Hugo Merchant, Rakshit Dadarwal, Xindi Wang, Paul A. Taylor, Eduardo A. Garza-Villarreal, Nikoloz Sirmpilatze, Caleb Sponheim, P. Christiaan Klink, Claude Lepage, Renée Hartig, Roberto Toro, Daniel S. Margulies, Katja Heuer, Julien Sein, Piotr Majka, Fondation pour la recherche médicale (Francia), Fondation de France (Francia), Human Brain Project, Biotechnology and Biological Sciences Research Council (Reino Unido), Wellcome Trust, National Institute of Mental Health (NIMH), German Primate Center - Deutsches Primatenzentrum -- Leibniz Insitute for Primate Research -- [Göttingen, Allemagne] (GPC - DPZ), Georg-August-University = Georg-August-Universität Göttingen, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institute of Language, Communication and the Brain (ILCB), Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, Donders Institute for Brain, Cognition and Behaviour, Radboud University [Nijmegen], Child Mind Institute, Brown University, Children’s Hospital of Philadelphia (CHOP ), University of Pennsylvania, Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), University of Chicago, Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), University of Tübingen, Max Planck Institute for Biological Cybernetics, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Chinese Academy of Sciences [Beijing] (CAS), University Medical Center [Mainz], University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Nencki Institute of Experimental Biology, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Monash University [Clayton], 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), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre Neurosciences intégratives et Cognition (INCC - UMR 8002), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences (KNAW), This research was supported in part by the Intramural Research Program of the NIMH and utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). Pypreclin work was supported by the Fondation pour la Recherche Medicale (FRM grant number ECO20160736100 to JT), Fondation de France, Human Brain Project (Corticity project). RT and KH are supported by ANR-19-DATA-0025-01 NeuroWebLab. RBM is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/N019814/1]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203129/Z/16/Z]., ANR-19-DATA-0025,NeuroWebLab,Un laboratoire de neuroscience collectif: Au delà de FAIR(2019), Fondation pour la recherche médicale, Fondation de France, Biotechnology and Biological Sciences Research Council (United Kingdom), University of Göttingen - Georg-August-Universität Göttingen, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University of Oxford [Oxford], Radboud university [Nijmegen], University of Pennsylvania [Philadelphia], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Universidad Nacional Autónoma de México (UNAM), 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, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Loh, Kep Kee, and Un laboratoire de neuroscience collectif: Au delà de FAIR - - NeuroWebLab2019 - ANR-19-DATA-0025 - DONNEES - VALID
Subjects: Primates, Computer science, Cognitive Neuroscience, [SDV]Life Sciences [q-bio], Neuroimaging, Online Systems, 050105 experimental psychology, lcsh:RC321-571, Access to Information, Diffusion, 03 medical and health sciences, 0302 clinical medicine, Resource (project management), Structural, Pipeline, Animals, 0501 psychology and cognitive sciences, MESH: Animals, MESH: Neuroimaging, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Non human primate, Functional, Action, intention, and motor control, Resource sharing, 05 social sciences, Data science, MESH: Online Systems, MESH: Access to Information, [SDV] Life Sciences [q-bio], MESH: Primates, Neurology, Open science, Toolbox, 030217 neurology & neurosurgery
Abstract: Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field . Pypreclin work was supported by the Fondation pour la Recherche Medicale (FRM grant number ECO20160736100 to JT), Fondation de France, Human Brain Project (Corticity project). RT and KH are supported by ANR-19-DATA-0025-01 NeuroWebLab. RBM is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/N019814/1]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203129/Z/16/Z]. Sí
Published: 2021
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8. Serotonergic regulation of the dopaminergic system: Implications for reward-related functions

Author: Cátia M. Teixeira, Edenia C. Menezes, Emmanuelle Courtiol, Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU Grossman School of Medicine), Courtiol, Emmanuelle, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Serotonin, Cognitive Neuroscience, Dopamine, Biology, Optogenetics, Serotonergic, Article, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Reward, medicine, 0501 psychology and cognitive sciences, 050102 behavioral science & comparative psychology, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS, Raphe, Dopaminergic Neurons, 05 social sciences, Dopaminergic, Ventral Tegmental Area, Ventral tegmental area, Neuropsychology and Physiological Psychology, medicine.anatomical_structure, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Raphe nuclei, Neuroscience, 030217 neurology & neurosurgery, medicine.drug
Abstract: Serotonin is a critical neuromodulator involved in development and behavior. Its role in reward is however still debated. Here, we first review classical studies involving electrical stimulation protocols and pharmacological approaches. Contradictory results on the serotonergic' involvement in reward emerge from these studies. These differences might be ascribable to either the diversity of cellular types within the raphe nuclei or/and the specific projection pathways of serotonergic neurons. We continue to review more recent work, using optogenetic approaches to activate serotonergic cells in the Raphe to VTA pathway. From these studies, it appears that activation of this pathway can lead to reinforcement learning mediated through the excitation of dopaminergic neurons by serotonergic neurons co-transmitting glutamate. Finally, given the importance of serotonin during development on adult emotion, the effect of abnormal early-life levels of serotonin on the dopaminergic system will also be discussed. Understanding the interaction between the serotonergic and dopaminergic systems during development and adulthood is critical to gain insight into the specific facets of neuropsychiatric disorders.
Published: 2021
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9. Accelerating the Evolution of Nonhuman Primate Neuroimaging

Author: Olivier Coulon, Michael P. Milham, Patrik Lindenfors, Karl-Heinz Nenning, Xiaojin Liu, Ravi S. Menon, Stephanie J. Forkel, Adam Messinger, Zheng Wang, Alexander Thiele, Luciano Simone, Benjamin Jung, Chika Sato, Jamie Nagy, Sean Froudist-Walsh, Kelvin Mok, Renée Hartig, Julien Sein, Alessandro Gozzi, Julien Vezoli, Tomoko Sakai, Lynn Uhrig, Martine Meunier, Christienne G. Damatac, Bonhwang Koo, Roberto Toro, Rogier B. Mars, Henrietta Howells, Lea Roumazeilles, Ming Zhan, Ann-Marie Mallon, Román Rossi-Pool, Elinor L. Sullivan, Yannick Becker, Doris Y. Tsao, Antoine Grigis, Lei Ai, Céline Amiez, Sara Wells, Reza Rajimehr, Aki Nikolaidis, Anna S. Mitchell, Simon M. Reader, Michele A. Basso, Béchir Jarraya, Amir Raz, Wim Vanduffel, Charles R.E. Wilson, Brian E. Russ, Christopher R. Madan, Orlin S. Todorov, Wasana Madushanka, Carole Guedj, Mark A. Pinsk, Clémentine Bodin, Hugo Merchant, Jennifer Nacef, Damien A. Fair, Anna W. Roe, Sze Chai Kwok, Stephen J. Sawiak, Essa Yacoub, Bastien Cagna, Kevin N. Laland, Wilbert Zarco, Charles E. Schroeder, Ting Xu, P. Christiaan Klink, Stanislas Dehaene, Takuya Hayashi, Matthew F. S. Rushworth, Amir Shmuel, Fadila Hadj-Bouziane, Katja Heuer, Ioana-Sabina Rautu, Andrew S. Fox, Austin Benn, Sabine Kastner, Thomas Brochier, Emmanuel Procyk, Marco Pagani, David C. Van Essen, Frank Q. Ye, Dirk Jan Ardesch, Régis Trapeau, Jakob Seidlitz, Marike Schiffer, Bassem Hiba, John H. Morrison, David A. Rudko, Paula L. Croxson, Patrick Friedrich, Augix Guohua Xu, Lazar Fleysher, Piotr Majka, Jonathan Smallwood, Aihua Chen, Timothy D. Griffiths, Fabien Balezeau, Stefan Everling, Michael C. Schmid, Robert Leech, Leslie G. Ungerleider, Mark G. Baxter, Afonso C. Silva, Clare Kelly, Zhi-ming Shen, Daniel S. Margulies, Mark J. Prescott, Pascal Belin, Erwin L. A. Blezer, Igor Kagan, Suliann Ben Hamed, David A. Leopold, Adrien Meguerditchian, Wendy Jarrett, Michel Thiebaut de Schotten, Nikoloz Sirmpilatze, Julia Sliwa, Henry Kennedy, Vikas Pareek, Yong-di Zhou, Michael Ortiz-Rios, Sherif Hamdy El-Gohary, Susann Boretius, Christopher I. Petkov, Pamela Garcia-Saldivar, Bella Williams, Jordy Tasserie, Hank P. Jedema, Jerome Sallet, Pieter R. Roelfsema, Winrich A. Freiwald, Eduardo A. Garza-Villarreal, Noam Harel, Caspar M. Schwiedrzik, Kevin Marche, Colline Poirier, Yang Gao, Henry C. Evrard, Ashkan Alvand, ANS - Cellular & Molecular Mechanisms, Laboratoire des Sciences de l'Information et des Systèmes (LSIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Centre National de la Recherche Scientifique (CNRS), Institut cellule souche et cerveau (SBRI), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Collège de France - Chaire Psychologie cognitive expérimentale, Collège de France (CdF (institution)), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Consortium, PRIMatE Data Exchange Global Collaboration Workshop and, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Newcastle University [Newcastle], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Medical Oncology, Department of Internal Medicine, Università Cattolica del Sacro Cuore [Roma] (Unicatt), Voice Neurocognition Laboratory, University of Glasgow, Oregon Health and Science University [Portland] (OHSU), Manchester Royal Infirmary, University of Manchester [Manchester], Princeton Neuroscience Institute [Princeton], University of Pennsylvania [Philadelphia], National Institute of Mental Health (NIMH), Harvard Medical School [Boston] (HMS), Washington University in St Louis, Laboratoire de psychologie cognitive (LPC), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, New York University [New York] (NYU), NYU System (NYU), State Key Laboratory of Novel Software Technology, University of Nanjing, Center for Nanotechnology Innovation, @NEST (CNI), National Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA)-Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA), Unité Analyse et Traitement de l'Information (UNATI), 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-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, Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Saclay (COmUE), Institut cellule souche et cerveau (U846 Inserm - UCBL1), Royal Netherlands Academy of Arts and Sciences (KNAW), East China Normal University [Shangaï] (ECNU), The Computational, Cognitive and Clinical Neuroimaging Lab, Imperial College London, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Medical Research Counc, Station de primatologie (SP), Centre National de la Recherche Scientifique (CNRS), Institute of Language, Communication and the Brain (ILCB), The University of Western Ontario, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Institute of integrative biology (Liverpool), University of Liverpool, McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), University of Cambridge [UK] (CAM), Rockefeller University [New York], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Laboratory for Neuro- and Psychofysiology, katho, 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), University of York [York, UK], Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), 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), Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Tianjin University of Science and Technology (TUST), Child Mind Institute, Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University Medical Center [Utrecht], Radboud university [Nijmegen], Chaire Psychologie cognitive expérimentale, Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of St Andrews [Scotland], Stockholm University, Hôpital du Bocage, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Princeton University, Utrecht University [Utrecht], Netherlands Institute for Neuroscience, Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford [Oxford], National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), National Institutes of Health [Bethesda] (NIH), Oregon National Primate Research Center (ONPRC), California Institute of Technology (CALTECH), Johns Hopkins University (JHU), The PRIMatE Data Exchange (PRIME-DE) Global Collaboration Workshop and Consortium, ANR-16-CONV-0002,ILCB,ILCB: Institute of Language Communication and the Brain(2016), Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Complex Trait Genetics, Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA), and Vanduffel, Wim
Subjects: Primates, 0301 basic medicine, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, education, Neuroimaging, Article, [SPI]Engineering Sciences [physics], 03 medical and health sciences, 0302 clinical medicine, London, Psychology, Animals, Humans, Sociology, ComputingMilieux_MISCELLANEOUS, Cognitive science, Science & Technology, Human Connectome Project, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Action, intention, and motor control, Information Dissemination, General Neuroscience, Neurosciences, Brain, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Congresses as Topic, Nonhuman primate, 030104 developmental biology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurosciences & Neurology, Life Sciences & Biomedicine, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract: Contains fulltext : 217200.pdf (Publisher’s version ) (Closed access) Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions. 4 p.
Published: 2020
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10. Neurotrophin & synaptogenesis

Author: Jeanneteau, Freddy, Arango-Lievano, Margarita, Chao, Moses, Institut de Génomique Fonctionnelle (IGF), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, John Rubenstein, Pasko Rakic, Bin Chen, Kenneth Y. Kwan, Hollis T. Cline, Jessica Cardin, freddy, jeanneteau, and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: TrkBVal66 met polymorphism, BDNF, Anterograde–retrograde signaling, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, mTOR, LTP–LTD, Autocrine–paracrine, P75NTR, Sortilin-related receptors, Actin
Abstract: International audience; Synaptogenesis is encoded by multiple genes that program the assembly of neural networks in the immature brain during development and later in life, in the experienced brain that must respond to changes of the external world and of proprioception. Processing of neural network activity cannot solely rely on the activity of established synapses as their plasticity can saturate. For this reason, synaptogenesis is reversible. Assembly and disassembly of synapses depend on the exchange of signals between the pre- and postsynaptic terminals. Synaptic trophic factors are paramount for neural network remodeling, homeostasis, and survival because they are present in limited supply. Neurotrophins present the necessary attributes to operate as synaptic trophin. It is speculated that many diverse and pleiotropic actions of neurotrophins on the synapse depend on the sources of neurotrophin ligands, its modes of secretion, and signaling, all influenced by their locations at the synapse. What information is encoded when synaptic neurotrophin signaling is retrograde or anterograde, paracrine or autocrine? Answers emerged from the characterization of the cells that secrete neurotrophins, the cells that respond to neurotrophins, the various modes of secretion, receptor–signaling pathways, and the temporal constraints imposed by synaptic transmission. Failure to communicate the appropriate synaptic trophic signals impairs neuronal networks maintenance and updated wiring.
Published: 2020
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11. The ebb and flow of attention: Between-subject variation in intrinsic connectivity and cognition associated with the dynamics of ongoing experience

Author: Kernbach, Julius, Yeo, B., Thiebaut De Schotten, Michel, Walter, Henrik, Sabuncu, Mert, Holmes, Avram, Gramfort, Alexandre, Varoquaux, Gaël, Thirion, Bertrand, Bzdok, Danilo, Milham, Michael, Ai, Lei, Koo, Bonhwang, Xu, Ting, Amiez, Céline, Balezeau, Fabien, Baxter, Mark, Blezer, Erwin L.A., Brochier, Thomas, Chen, Aihua, Croxson, Paula, Damatac, Christienne, Dehaene, Stanislas, Everling, Stefan, Fair, Damian, Fleysher, Lazar, Freiwald, Winrich, Froudist-Walsh, Seán, Griffiths, Timothy, Guedj, Carole, Hadj-Bouziane, Fadila, Ben Hamed, Suliann, Harel, Noam, Hiba, Bassem, Jarraya, Bechir, Jung, Benjamin, Kastner, Sabine, Klink, P. Christiaan, Kwok, Sze Chai, Laland, Kevin, Leopold, David, Lindenfors, Patrik, Mars, Rogier, Menon, Ravi, Messinger, Adam, Meunier, Martine, Mok, Kelvin, Morrison, John, Nacef, Jennifer, Nagy, Jamie, Rios, Michael Ortiz, Petkov, Christopher, Pinsk, Mark, Poirier, Colline, Procyk, Emmanuel, Rajimehr, Reza, Reader, Simon, Roelfsema, Pieter, Rudko, David, Rushworth, Matthew F.S., Russ, Brian, Sallet, Jérôme, Schmid, Michael Christoph, Schwiedrzik, Caspar, Seidlitz, Jakob, Sein, Julien, Shmuel, Amir, Sullivan, Elinor, Ungerleider, Leslie, Thiele, Alexander, Todorov, Orlin, Tsao, Doris, Wang, Zheng, Wilson, Charles R.E., Yacoub, Essa, Ye, Frank, Zarco, Wilbert, Zhou, Yong-di, Schroeder, Charles, Turnbull, Adam, Wang, Hao-Ting, Schooler, Jonathan, Jefferies, Elizabeth, Margulies, Daniel, Smallwood, Jonathan, Department of Psychology [York, UK], University of York [York, UK], Department of Psychological and Brain Sciences, University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California (UC), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Centre National de la Recherche Scientifique (CNRS), European Project: 646927,H2020,ERC-2014-CoG,WANDERINGMINDS(2015), Margulies, Daniel, Not all minds that wander are lost: A neurocognitive test of mind-wandering state’s contribution to human cognition. - WANDERINGMINDS - - H20202015-12-01 - 2020-05-31 - 646927 - VALID, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Natbrainlab, Department of Forensic and Neurodevelopmental Sciences, Institute of psychiatry-King‘s College London, Berlin School of Mind and Brain [Berlin], Humboldt Universität zu Berlin, Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Massachusetts Institute of Technology (MIT), Harvard University [Cambridge], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, Univ Calif Berkeley, Dept Mat Sci & Engn, Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University Medical Center [Utrecht], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), East China Normal University [Shangaï] (ECNU), Radboud university [Nijmegen], Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, The University of Western Ontario, Oregon Health and Science University [Portland] (OHSU), The Rockefeller University, Cognition and Brain Plasticity Unit, Institut d'Investigació Biomèdica de Bellvitge [Barcelone] (IDIBELL), Equipe Impact, Centre de Recherche en Neurosciences de Lyon, INSERM U1028, CNRS UMR5292, Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, ANIMAGE, Rhône-Alpes Genopole, Université de Lorraine (UL), Princeton Neuroscience Institute [Princeton], Royal Netherlands Academy of Arts and Sciences (KNAW), University of St Andrews [Scotland], Stockholm University, University of Pennsylvania [Philadelphia], National Institute of Mental Health (NIMH), National Institute of Health, Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University-McGill University, Hôpital du Bocage, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Newcastle University [Newcastle], Princeton University, Neural Rehabilitation Engineering Laboratory, Université Catholique de Louvain (UCL), Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (SBRI), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), McGovern Institute for Brain Research [Cambridge], Utrecht University [Utrecht], Netherlands Institute for Neuroscience, Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford [Oxford], Laboratoire de Chimie - UMR5182 (LC), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Oregon National Primate Research Center (ONPRC), California Institute of Technology (CALTECH), Department of Computational and Applied Mathematics [Houston], Rice University [Houston], Johns Hopkins University (JHU), Nathan Kline Institute for Psychiatric Research (NKI), and Department of Psychology, The University of York, Heslington, England
Subjects: Male, Cognitive Neuroscience, 050105 experimental psychology, Task (project management), Visual processing, Young Adult, 03 medical and health sciences, Cognition, 0302 clinical medicine, Task-positive network, medicine, Humans, Attention, 0501 psychology and cognitive sciences, ComputingMilieux_MISCELLANEOUS, Default mode network, Brain Mapping, Working memory, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Brain, Flexibility (personality), Magnetic Resonance Imaging, Memory, Short-Term, Visual cortex, medicine.anatomical_structure, Neurology, Female, Nerve Net, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; Cognition is dynamic, allowing us the flexibility to shift focus from different aspects of the environment, or between internally- and externally-oriented trains of thought. Although we understand how individuals switch attention across different tasks, the neurocognitive processes that underpin the dynamics of less constrained elements of cognition are less well understood. To explore this issue, we developed a paradigm in which participants intermittently responded to external events across two conditions that systematically vary in their need for updating working memory based on information in the external environment. This paradigm distinguishes the influences on cognition that emerge because of demands placed by the task (sustained) from changes that result from the time elapsed since the last task response (transient). We used experience sampling to identify dynamic changes in ongoing cognition in this paradigm, and related between subject variation in these measures to variations in the intrinsic organisation of large-scale brain networks. We found systems important for attention were involved in the regulation of off-task thought. Coupling between the ventral attention network and regions of primary motor cortex was stronger for individuals who were able to regulate off-task thought in line with the demands of the task. This pattern of coupling was linked to greater task-related thought when environmental demands were high and elevated off-task thought when demands were low. In contrast, the coupling of the dorsal attention network with a region of lateral visual cortex was stronger for individuals for whom off-task thoughts transiently increased with the time since responding to the external world increased. This pattern is consistent with a role for this system in the time-limited top-down biasing of visual processing to increase behavioural efficiency. Unlike the attention networks, coupling between regions of the default mode network and dorsal occipital cortex was weaker for individuals for whom the level of detail decreased with the passage of time when the external task did not require continuous monitoring of external information. These data provide novel evidence for how neural systems vary across subjects and may be underpin individual variation in the dynamics of thought, linking attention systems to the maintenance of task-relevant information, and the default mode network to supporting experiences with vivid detail.
Published: 2019
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12. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults

Author: Hong, Seok-Jun, Vos De Wael, Reinder, Bethlehem, Richard, Lariviere, Sara, Paquola, Casey, Valk, Sofie, Milham, Michael, Di Martino, Adriana, Bernhardt, Boris, Falkiewicz, Marcel, Ellamil, Melissa, Krause, Sarah, Baczkowski, Blazej, Osoianu, Anastasia, Pool, Jared, Haueis, Philipp, Kramarenko, Yelyzaveta, Engen, Haakon, Ohrnberger, Katharina, Farrugia, Nicolas, Smallwood, Jonathan, Babayan, Anahit, Erbey, Miray, Kumral, Deniz, Reinelt, Janis, Reiter, Andrea, Röbbig, Josefin, Schaare, H. Lina, Uhlig, Marie, Anwander, Alfred, Bazin, Pierre-Louis, Horstmann, Annette, Lampe, Leonie, Nikulin, Vadim, Okon-Singer, Hadas, Preusser, Sven, Pampel, André, Rohr, Christiane, Sacher, Julia, Thöne-Otto, Angelika, Trapp, Sabrina, Nierhaus, Till, Altmann, Denise, Arelin, Katrin, Blöchl, Maria, Bongartz, Edith, Breig, Patric, Cesnaite, Elena, Chen, Sufang, Cozatl, Roberto, Czerwonatis, Saskia, Dambrauskaite, Gabriele, Dreyer, Maria, Enders, Jessica, Engelhardt, Melina, Fischer, Marie Michele, Forschack, Norman, Golchert, Johannes, Golz, Laura, Guran, C. Alexandrina, Hedrich, Susanna, Hentschel, Nicole, Hoffmann, Daria, Huntenburg, Julia, Jost, Rebecca, Kosatschek, Anna, Kunzendorf, Stella, Lammers, Hannah, Lauckner, Mark, Mahjoory, Keyvan, Kanaan, Ahmad, Mendes, Natacha, Menger, Ramona, Morino, Enzo, Näthe, Karina, Neubauer, Jennifer, Noyan, Handan, Oligschläger, Sabine, Panczyszyn-Trzewik, Patricia, Poehlchen, Dorothee, Putzke, Nadine, Roski, Sabrina, Schaller, Marie-Catherine, Schieferbein, Anja, Schlaak, Benito, Schmidt, Robert, Gorgolewski, Krzysztof, Schmidt, Hanna Maria, Schrimpf, Anne, Stasch, Sylvia, Voss, Maria, Wiedemann, Annett, Margulies, Daniel, Gaebler, Michael, Villringer, Arno, Spinoza Centre for Neuroimaging, Netherlands Institute for Neuroscience (NIN), Brain and Cognition, Brein en Cognitie (Psychologie, FMG), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], McGill University = Université McGill [Montréal, Canada], University of Cambridge [UK] (CAM), Heinrich-Heine-Universität Düsseldorf [Düsseldorf], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, NYU Langone Medical Center, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Adam Mickiewicz University in Poznań (UAM), Lab-STICC_IMTA_CACS_IAS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Université de Brest (UBO)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université Bretagne Loire (UBL)-Centre National de la Recherche Scientifique (CNRS)-Université de Bretagne Sud (UBS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Brest (UBO)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université Bretagne Loire (UBL)-Centre National de la Recherche Scientifique (CNRS)-Université de Bretagne Sud (UBS)-École Nationale d'Ingénieurs de Brest (ENIB), University of York [York, UK], Department of neurophysics [Leipzig], Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, University of British Columbia (UBC), Technische Universität Berlin (TUB), Department of Psychology [Stanford, CA, USA], Stanford University [Stanford], Laboratoire de Neurosciences cognitives (LNC), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Adult, Male, Statistics and Probability, Data Descriptor, Electroencephalography - EEG, 010504 meteorology & atmospheric sciences, Functional magnetic resonance imaging, Emotions, Electroencephalography, Library and Information Sciences, 01 natural sciences, Education, Young Adult, 03 medical and health sciences, Cognition, Germany, Human behaviour, medicine, Humans, Psychological testing, Young adult, ComputingMilieux_MISCELLANEOUS, Depression (differential diagnoses), Aged, 030304 developmental biology, 0105 earth and related environmental sciences, Emotion, 0303 health sciences, medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Age Factors, Middle Aged, Anthropometry, medicine.disease, Magnetic Resonance Imaging, Cognitive test, Computer Science Applications, Female, Statistics, Probability and Uncertainty, Addictive behavior, business, Psychophysiology, Clinical psychology, Information Systems
Abstract: We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20–35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.
Published: 2019
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13. Gradients of connectivity distance in the cerebral cortex of the macaque monkey

Author: Gary S. Linn, Marcel Falkiewicz, Blazej M. Baczkowski, Ting Xu, Arnaud Falchier, Daniel S. Margulies, Sabine Oligschläger, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Universität Leipzig [Leipzig], Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, 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), CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Gestionnaire, Hal Sorbonne Université, 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), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: Cortical topography, Histology, Macaque, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Similarity (network science), biology.animal, Cortex (anatomy), Neural Pathways, medicine, Animals, 0501 psychology and cognitive sciences, Primate, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030304 developmental biology, Cerebral Cortex, 0303 health sciences, Brain Mapping, Connectivity, biology, General Neuroscience, Functional connectivity, 05 social sciences, Organizing principles, Macaca mulatta, Magnetic Resonance Imaging, medicine.anatomical_structure, Cerebral cortex, Primate phylogeny, Original Article, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Anatomy, Nerve Net, Neuroscience, 030217 neurology & neurosurgery
Abstract: Cortical connectivity conforms to a series of organizing principles that are common across species. Spatial proximity, similar cortical type, and similar connectional profile all constitute factors for determining the connectivity between cortical regions. We previously demonstrated another principle of connectivity that is closely related to the spatial layout of the cerebral cortex. Using functional connectivity from resting-state fMRI in the human cortex, we found that the further a region is located from primary cortex, the more distant are its functional connections with the other areas of the cortex. However, it remains unknown whether this relationship between cortical layout and connectivity extends to other primate species. Here, we investigated this relationship using both resting-state functional connectivity as well as gold-standard tract-tracing connectivity in the macaque monkey cortex. For both measures of connectivity, we found a gradient of connectivity distance extending between primary and frontoparietal regions. In the human cortex, the further a region is located from primary areas, the stronger its connections to distant portions of the cortex, with connectivity distance highest in frontal and parietal regions. The similarity between the human and macaque findings provides evidence for a phylogenetically conserved relationship between the spatial layout of cortical areas and connectivity. Electronic supplementary material The online version of this article (10.1007/s00429-018-1811-1) contains supplementary material, which is available to authorized users.
Published: 2019
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14. The SONATA data format for efficient description of large-scale network models

Author: Salvador Dura-Bernal, Jean-Denis Courcol, Arseny V. Povolotsky, Michael Gevaert, Sergey L. Gratiy, Padraig Gleeson, James G. King, Anton Arkhipov, Adrien Devresse, Eilif Muller, Werner Van Geit, Benjamin Dichter, Juan Hernando, Judit Planas, Yazan N. Billeh, Kael Dai, Andrew P. Davison, Allen Institute for Brain Science [Seattle, WA, USA], Ecole Polytechnique Fédérale de Lausanne (EPFL), Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), SUNY Downstate Medical Center, State University of New York (SUNY), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Department of Neuroscience, Physiology & Pharmacology, University College of London [London] (UCL), Department of Neurosurgery [Stanford], Stanford Medicine, Stanford University-Stanford University, and Biological Systems and Engineering
Subjects: 0301 basic medicine, Databases, Factual, Physiology, Computer science, network models, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Distributed computing, MESH: Neurons, Nervous System, 0302 clinical medicine, Animal Cells, Medicine and Health Sciences, Biology (General), MESH: Brain Mapping, Network model, Neurons, Brain Mapping, 0303 health sciences, Computational model, Neuronal Morphology, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Ecology, Application programming interface, Simulation and Modeling, Physics, Brain, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Electrophysiology, MESH: Reproducibility of Results, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Scalability, MESH: Programming Languages, Cellular Types, Anatomy, Model building, Network Analysis, Algorithms, Research Article, MESH: Computational Biology, Network analysis, Computer and Information Sciences, Biophysical Simulations, Neural Networks, QH301-705.5, Models, Neurological, Biophysics, Neurophysiology, MESH: Algorithms, Research and Analysis Methods, MESH: Brain, MESH: Software, 03 medical and health sciences, Cellular and Molecular Neuroscience, MESH: Computer Simulation, MESH: Models, Neurological, Genetics, Humans, Computer Simulation, Molecular Biology, Ecology, Evolution, Behavior and Systematics, data format, 030304 developmental biology, Flexibility (engineering), MESH: Humans, Scale (chemistry), Neurosciences, Biology and Life Sciences, Computational Biology, Reproducibility of Results, Cell Biology, MESH: Neurosciences, MESH: Databases, Factual, Visualization, SONATA, 030104 developmental biology, Cellular Neuroscience, Synapses, Open network architecture, Programming Languages, Software, 030217 neurology & neurosurgery, Neuroscience
Abstract: Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility., Author summary Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.
Published: 2020
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15. Cortical processing of configurally perceived odor mixtures

Author: Samantha M. Vervoordt, Gloria Fleming, Gérard Coureaud, Donald A. Wilson, Coureaud, Gérard, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en neurosciences de Lyon (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, 0301 basic medicine, Article, Cortical processing, Mice, piriform cortex, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Animals, Rats, Long-Evans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], configural perception, Molecular Biology, elemental perception, General Neuroscience, Ethyl maltol, single-unit recording, Olfactory Perception, odor mixtures, Rats, 030104 developmental biology, chemistry, Odor, Odorants, Biophysics, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), psychological phenomena and processes, 030217 neurology & neurosurgery, Developmental Biology
Abstract: International audience; Most odors are not composed of a single volatile chemical species, but rather are mixtures of many different volatile molecules, the perception of which is dependent on the identity and relative concentrations of the components. Changing either the identity or ratio of components can lead to shifts between configural and elemental perception of the mixture. For example, a 30/70 ratio of ethyl isobutyrate (odorant A, a strawberry scent) and ethyl maltol (odorant B, a caramel scent) is perceived as pineapple by humans-a configural percept distinct from the components. In contrast, a 68/32 ratio of the same odorants is perceived elementally, and is identified as the component odors. Here, we examined single-unit responses in the anterior and posterior piriform cortex (aPCX and pPCX) of mice to these A and B mixtures. We first demonstrate that mouse behavior is consistent with a configural/elemental perceptual shift as concentration ratio varies. We then compared responses to the configural mixture to those evoked by the elemental mixture, as well as to the individual components. Hierarchical cluster analyses suggest that in the mouse aPCX, the configural mixture was coded as distinct from both components, while the elemental mixture was coded as similar to the components. In contrast, mixture perception did not predict pPCX ensemble coding. Similar electrophysiological results were also observed in rats. The results suggest similar perceptual characteristics of the AB mixture across species, and a division in the roles of aPCX and pPCX in the coding of configural and elemental odor mixtures.
Published: 2020
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16. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Author: Eugene P. Duff, Gunnar Schaefer, R. Cameron Craddock, Xiangrui Li, Vince D. Calhoun, Tibor Auer, Michael Hanke, David Keator, Camille Maumet, Ariel Rokem, Thomas E. Nichols, Jean-Baptiste Poline, Daniel A. Handwerker, Guillaume Flandin, Yaroslav O. Halchenko, Vanessa Sochat, Samir Das, Satrajit S. Ghosh, Gaël Varoquaux, Zachary Michael, Russell A. Poldrack, Tristan Glatard, B. Nolan Nichols, John Pellman, William Triplett, Jessica A. Turner, Krzysztof J. Gorgolewski, Stanford University, University of Cambridge [UK] (CAM), Department of Electrical and Computer Engineering [Albuquerque] (ECE Department), The University of New Mexico [Albuquerque], The Mind Research Network, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, McGill University = Université McGill [Montréal, Canada], Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Wellcome Trust Centre for Neuroimaging, University College of London [London] (UCL), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Psychological and Brain Sciences, Dartmouth College [Hanover], National Institute of Mental Health (NIMH), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UC Irvine), University of California (UC), Shandong University, Squishymedia [Portland], Warwick Manufacturing Group [Coventry] (WMG), University of Warwick [Coventry], Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council (BBSRC), Department of Statistics [Warwick], Child Mind Institute, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), University of Washington [Seattle], Georgia State University, University System of Georgia (USG), 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), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Maumet, Camille, University of Oxford [Oxford], Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Otto-von-Guericke University [Magdeburg] (OVGU), University of California [Irvine] (UCI), University of California, Biotechnology and Biological Sciences Research Council, University of California [Berkeley], University of California-University of California, 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, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Statistics and Probability, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Datasets as Topic, Neuroimaging, Library and Information Sciences, computer.software_genre, Data publication and archiving, Field (computer science), Article, Education, 03 medical and health sciences, 0302 clinical medicine, Software, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data processing, business.industry, Data Collection, Neurosciences, Data structure, File format, Magnetic Resonance Imaging, R1, Brain Disorders, Research data, Computer Science Applications, Metadata, 030104 developmental biology, Neurological, Biomedical Imaging, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Data mining, Statistics, Probability and Uncertainty, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract: The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Published: 2018
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17. An Open Resource for Non-human Primate Imaging

Author: Jennifer Nacef, Kevin N. Laland, Wilbert Zarco, Charles E. Schroeder, Jamie Nagy, Julien Sein, P. Christiaan Klink, Kelvin Mok, Michael P. Milham, Stanislas Dehaene, Charles R.E. Wilson, Orlin S. Todorov, Erwin L. A. Blezer, Winrich A. Freiwald, Béchir Jarraya, Fadila Hadj-Bouziane, John H. Morrison, David A. Rudko, Zheng Wang, Essa Yacoub, Leslie G. Ungerleider, Brian E. Russ, Amir Shmuel, Emmanuel Procyk, Stefan Everling, Elinor L. Sullivan, Doris Y. Tsao, Noam Harel, Bassem Hiba, Thomas Brochier, Jakob Seidlitz, Sabine Kastner, Michael Ortiz Rios, Martine Meunier, Alexander Thiele, Carole Guedj, Paula L. Croxson, Sze Chai Kwok, Yong-di Zhou, Christopher I. Petkov, Ting Xu, Caspar M. Schwiedrzik, Frank Q. Ye, Fabien Balezeau, Aihua Chen, Ravi S. Menon, Adam Messinger, Mark G. Baxter, Matthew F. S. Rushworth, Sean Froudist-Walsh, Rogier B. Mars, Reza Rajimehr, Christienne G. Damatac, Bonhwang Koo, Lei Ai, Colline Poirier, David A. Leopold, Simon M. Reader, Daniel S. Margulies, Mark A. Pinsk, Benjamin Jung, Lazar Fleysher, Timothy D. Griffiths, Michael C. Schmid, Céline Amiez, Suliann Ben Hamed, Jerome Sallet, Pieter R. Roelfsema, Damian A. Fair, Patrik Lindenfors, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (SBRI), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Newcastle University [Newcastle], Icahn School of Medicine at Mount Sinai [New York] (MSSM), University Medical Center [Utrecht], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), East China Normal University [Shangaï] (ECNU), Radboud University [Nijmegen], Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Collège de France - Chaire Psychologie cognitive expérimentale, Collège de France (CdF (institution)), The University of Western Ontario, Oregon Health and Science University [Portland] (OHSU), Rockefeller University [New York], New York University [New York] (NYU), NYU System (NYU), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), University of Minnesota Medical School, University of Minnesota System, National Institute of Mental Health (NIMH), Princeton Neuroscience Institute [Princeton], Royal Netherlands Academy of Arts and Sciences (KNAW), University of St Andrews [Scotland], National Institute of Neurological Disorders and Stroke [Bethesda] (NINDS), National Institutes of Health [Bethesda] (NIH), Stockholm University, Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], University of California [Davis] (UC Davis), University of California (UC), Princeton University, McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Utrecht University [Utrecht], McGill University = Université McGill [Montréal, Canada], Netherlands Institute for Neuroscience, National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), Oregon National Primate Research Center (ONPRC), California Institute of Technology (CALTECH), Tianjin University of Science and Technology (TUST), Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota System-University of Minnesota System, Johns Hopkins University (JHU), Centre de Recherche en Éthique [UdeM - Montréal] (CREUM), Université de Montréal (UdeM), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, 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), Columbia University College of Physicians and Surgeons, University of St Andrews. School of Biology, University of St Andrews. Centre for Biological Diversity, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Institute of Behavioural and Neural Sciences, University of St Andrews. Centre for Social Learning & Cognitive Evolution, Netherlands Institute for Neuroscience (NIN), Equipe Impact, Centre de Recherche en Neurosciences de Lyon, INSERM U1028, CNRS UMR5292, Academic Medical Center, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI - USC 1361 INRAE), Radboud university [Nijmegen], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Chaire Psychologie cognitive expérimentale, Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), University of Oxford [Oxford], University of California, 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), Seidlitz, Jakob [0000-0002-8164-7476], Apollo - University of Cambridge Repository, Integrative Neurophysiology, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France, Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Neuroimagerie cognitive (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Sud - Paris 11 (UP11), The Rockefeller University, McGill University-McGill University, McGill University, Centre de Recherche en Éthique de l'Université de Montréal (CREUM), Université de Montréal [Montréal], 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 Ben Hamed, Suliann
Subjects: 0301 basic medicine, Primates, Computer science, Neuroscience(all), [SDV]Life Sciences [q-bio], QH301 Biology, Datasets as Topic, Neuroimaging, Article, 03 medical and health sciences, QH301, 0302 clinical medicine, Resource (project management), All institutes and research themes of the Radboud University Medical Center, SDG 17 - Partnerships for the Goals, Connectome, Animals, Psychology, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Independent data, Biological sciences, ComputingMilieux_MISCELLANEOUS, Non human primate, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Neurology & Neurosurgery, Quality assessment, Action, intention, and motor control, Information Dissemination, General Neuroscience, Neurosciences, Perception, Action and Control [DI-BCB_DCC_Theme 2], Brain, DAS, Data science, Magnetic Resonance Imaging, 030104 developmental biology, Data exchange, Biomedical Imaging, Cognitive Sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery
Abstract: Summary Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets., Highlights • Openly shared, large non-human primate neuroimaging data resource • Multiple imaging modalities contributed from investigators around the world • Quality assessments of the dataset • Discussed pitfalls and challenges in analyzing the non-human primate MRI data, The PRIMatE Data Exchange (PRIME-DE) consortium is an open science resource for the neuroimaging community aiming to facilitate efforts to map the non-human primate connectome. It aggregates and shares anatomical, functional, and diffusion MRI datasets from laboratories throughout the world.
Published: 2018
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18. Benchmarking functional connectome-based predictive models for resting-state fMRI

Author: Michael P. Milham, Bertrand Thirion, Alexandre Abraham, Darya Chyzhyk, Kamalaker Dadi, Mehdi Rahim, Gaël Varoquaux, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay
Subjects: Computer science, Cognitive Neuroscience, Rest, Models, Neurological, Machine learning, computer.software_genre, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Humans, 0501 psychology and cognitive sciences, Resting-state fMRI, Functional connectomes, Resting state fMRI, business.industry, 05 social sciences, Supervised learning, Brain, Benchmarking, medicine.disease, Classification, Magnetic Resonance Imaging, Predictive modeling, Neurology, Schizophrenia, Benchmark (computing), Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Population study
Abstract: International audience; Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2 000 individuals, encompassing neuro-degenerative (Alzheimer’s, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions –either pre-defined or generated from the rest-fMRI data– 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations inthe populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.
Published: 2018
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19. Controlling a confound in predictive models with a test set minimizing its effect

Author: Michael P. Milham, Bertrand Thirion, Gaël Varoquaux, Darya Chyzhyk, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay
Subjects: 0301 basic medicine, Computer science, phenotype, subsampling, Machine learning, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], statistical testing, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], confound, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Healthy aging, Statistical hypothesis testing, business.industry, Linear model, biomarkers, Mutual information, Variance (accounting), predictive models, Confounding effect, 030104 developmental biology, Test set, Biomarker (medicine), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: International audience; Predictive models applied on brain images can extract imaging biomarkers of pathologies or psychological traits. Yet, a successful prediction may be driven by a confounding effect that is correlated with the effect of interest. For instance fluid intelligence is strongly impacted by age; age is well predicted from brain images; hence successful prediction of fluid intelligence from brain images might have captured nothing more than a biomarker of aging. Here we introduce a non-parametric approach to control for a confounding effect in a predictive model. It is based on crafting a test set on which the effect of interest is independent from the confounding effect. We name this strategy " anti mutual-information subsampling ". We demonstrate the approach with a large sample resting-state fMRI and psychometric data of healthy aging subjects (n = 608). We show that using a linear model to remove the effect of age on the brain signals (" deconfounding ") leads to pessimistic scores, as previously reported. Anti mutual-information subsampling does not require to remove from the brain signals the shared variance between aging and fluid intelligence, and hence does not display this pessimistic behavior. In addition, it is non-parametric and hence robust to violations of the linear hypothesis.
Published: 2018

20. Patterns of thought: population variation in the associations between large-scale network organisation and self-reported experiences at rest

Author: Michael P. Milham, Elizabeth Jefferies, Jonathan Smallwood, R. Cameron Craddock, Danilo Bzdok, Hao-Ting Wang, Daniel S. Margulies, Department of Psychology [York, UK], University of York [York, UK], Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-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), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, EJ was supported by ERC (SEMBIND -283530). JS was supported by ERC (WANDERINGMINDS -646927), Volkswagen Foundation (Wandering Minds -89440 and 89439) and the John Templeton Foundation, 'Prospective Psychology Stage 2: A Research Competition'. DB was supported by the German Research Foundation (DFG, BZ2/2-1, BZ2/3-1, and BZ2/4-1), International Research Training Group (IRTG2150), Amazon AWS Research Grant (2016 and 2017), the German National Academic Foundation, and the START-Program of the Faculty of Medicineand an Exploratory Research Space Seed Fund, RWTH Aachen University., Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Bzdok, Danilo
Subjects: Adult, Male, Adolescent, Cognitive Neuroscience, Rest, Cognitive neuroscience, 050105 experimental psychology, Session (web analytics), Machine Learning, Thinking, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Limbic system, medicine, Connectome, Limbic System, Humans, 0501 psychology and cognitive sciences, Attention, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Association (psychology), Default mode network, Cerebral Cortex, medicine.diagnostic_test, 05 social sciences, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, Middle Aged, Magnetic Resonance Imaging, medicine.anatomical_structure, Variation (linguistics), Neurology, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Nerve Net, Functional magnetic resonance imaging, Psychology, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: International audience; Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whether dimensions of population variation in different modes of unconstrained processing can be described by the associations between patterns of neural activity and self-reports of experience during the same period. We selected 258 individuals from a publicly available data set who had measures of resting-state functional magnetic resonance imaging, and self-reports of experience during the scan. We used machine learning to determine patterns of association between the neural and self-reported data, finding variation along four dimensions. ‘Purposeful’ experiences were associated with lower connectivity -in particular default mode and limbic networks were less correlated with attention and sensorimotor networks. ‘Emotional’ experiences were associated with higher connectivity, especially between limbic and ventral attention networks. Experiences focused on themes of ‘personal importance’ were associated with reduced functional connectivity within attention and control systems. Finally, visual experiences were associated with stronger connectivity between visual and other networks, in particular the limbic system. Some of these patterns had contrasting links with cognitive function as assessed in a separate laboratory session -purposeful thinking was linked to greater intelligence and better abstract reasoning, while a focus on personal importance had the opposite relationship. Together these findings are consistent with an emerging literature on unconstrained states and also underlines that these states are heterogeneous, with distinct modes of population variation reflecting the interplay of different large-scale networks.
Published: 2018

21. Selective decline of neurotrophin and neurotrophin receptor genes within CA1 pyramidal neurons and hippocampus proper: Correlation with cognitive performance and neuropathology in mild cognitive impairment and Alzheimer's disease

Author: Yinghua Chen, Moses V. Chao, Shaoli Che, Melissa J. Alldred, Thorsten M. Kranz, Irina Elarova, Stephen D. Ginsberg, Elliott J. Mufson, Scott E. Counts, Michael Malek-Ahmadi, Freddy Jeanneteau, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Skirball Institute of Biomolecular Medicine, New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Institut de Génomique Fonctionnelle (IGF), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Department of Translational Science and Molecular Medicine, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, and Barrow Neurological Institute
Subjects: Male, Cognitive Neuroscience, [SDV]Life Sciences [q-bio], Hippocampus, Receptors, Nerve Growth Factor, Tropomyosin receptor kinase B, Tropomyosin receptor kinase A, Hippocampal formation, Trk receptors, Tropomyosin receptor kinase C, Article, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Humans, Medicine, Cognitive Dysfunction, 0501 psychology and cognitive sciences, Nerve Growth Factors, CA1 Region, Hippocampal, Aged, Aged, 80 and over, Brain-derived neurotrophic factor, biology, business.industry, Pyramidal Cells, 05 social sciences, brain-derived neurotrophic factor, neuritic plaques, nervous system, neurofibrillary tangles, Trk receptor, Disease Progression, biology.protein, Female, business, Neuroscience, microarray, 030217 neurology & neurosurgery, Neurotrophin
Abstract: International audience; Hippocampal CA1 pyramidal neurons, a major component of the medial temporal lobe memory circuit, are selectively vulnerable during the progression of Alzheimer's disease (AD). The cellular mechanism(s) underlying degeneration of these neurons and the relationship to cognitive performance remains largely undefined. Here, we profiled neurotrophin and neurotrophin receptor gene expression within microdissected CA1 neurons along with regional hippocampal dissections from subjects who died with a clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), or AD using laser capture microdissection (LCM), custom-designed microarray analysis, and qPCR of CA1 subregional dissections. Gene expression levels were correlated with cognitive test scores and AD neuropathology criteria. We found a significant downregulation of several neurotrophin genes (e.g., Gdnf, Ngfb, and Ntf4) in CA1 pyramidal neurons in MCI compared to NCI and AD subjects. In addition, the neurotrophin receptor transcripts TrkB and TrkC were decreased in MCI and AD compared to NCI. Regional hippocampal dissections also revealed select neurotrophic gene dysfunction providing evidence for vulnerability within the hippocampus proper during the progression of dementia. Downregulation of several neurotrophins of the NGF family and cognate neurotrophin receptor (TrkA, TrkB, and TrkC) genes correlated with antemortem cognitive measures including the Mini-Mental State Exam (MMSE), a composite global cognitive score (GCS), and Episodic, Semantic, and Working Memory, Perceptual Speed, and Visuospatial domains. Significant correlations were found between select neurotrophic expression downregulation and neuritic plaques (NPs) and neurofibrillary tangles (NFTs), but not diffuse plaques (DPs). These data suggest that dysfunction of neurotrophin signaling complexes have profound negative sequelae within vulnerable hippocampal cell types, which play a role in mnemonic and executive dysfunction during the progression of AD.
Published: 2017
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22. Enhancing studies of the connectome in autism using the Autism Brain Imaging Data Exchange II

Author: R. Joanne Jao Keehn, Joel T. Nigg, Leslie C. Baxter, Jeffrey S. Anderson, David H. O’Connor, Nicole Wenderoth, Michal Assaf, Michael P. Milham, Sylvie Bernaerts, B. Blair Braden, Bosi Chen, Marjorie Solomon, Daniel P. Kennedy, Richard Delorme, Adriana Di Martino, Susan Y. Bookheimer, Inna Fishman, Jacqueline Fitzgerald, Stewart H. Mostofsky, Ralph-Axel Müller, Chandan J. Vaidya, Joshua H. Balsters, Roberto Toro, Mirella Dapretto, R. Cameron Craddock, Anita Beggiato, Janet E. Lainhart, Kirsten O'Hearn, Beatriz Luna, Kaat Alaerts, Lisa Byrge, F. Xavier Castellanos, Louise Gallagher, Mary Beth Nebel, Tonya White, Damien A. Fair, Bennett L. Leventhal, Catherine Lord, Laura M. E. Blanken, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Utah, Hartford Hospital, Yale University School of Medicine, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Barrow Neurological Institute, AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), 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), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of California [Los Angeles] (UCLA), University of California, Arizona State University [Tempe] (ASU), Indiana University [Bloomington], Indiana University System, Oregon Health and Science University [Portland] (OHSU), San Diego State University (SDSU), St James’s Hospital [Dublin, Ireland], Trinity College Dublin, University of Wisconsin-Madison, University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Kennedy Krieger Institute [Baltimore], Johns Hopkins University School of Medicine [Baltimore], University of California [Davis] (UC Davis), Children's National Medical Center, Georgetown University [Washington] (GU), Weill Medical College of Cornell University [New York], University of California [San Francisco] (UCSF), Support for ABIDE II coordination and data aggregation was provided by NIMH (521MH107045) to A.D.M. and gifts from Joseph P. Healey, Phyllis Green and Randolph Cowen to M.P.M. (M.P.M. is a Randolph Cowen and Phyllis Green Scholar). Support for data collection at each site was provided by NIH (MH084961-GU_1, MH094409-IU_1, NS048527, MH085328, MH078160-KKI_1, MH102660, MH087770, MH084126, MH081218, HD065282-NYU_1, MH102660-NYU_2, MH095888-ONRC_2, MH096773, MH091238, MH096773-03S1, MH086654-OHSU_1, MH081023, MH097972-SDSU_1, MH099250-01-UCD_1, HD055784-UCLA_1, MH092697, MH080826, MH60450, DC008553, NS34783-USM_1, HD065280-01-UCLA_Long, MH081191, MH67924, HD55748-UPSM_Long), Autism Speaks (KKI_1, 04593, UPSM_Long), the Simons Foundation (307280, EMC_1, OHSU_1), IDDRC (HD040677-07, GU_1), State of Arizona Alzheimer’s Consortium, BNI and Department of Defense (AR140105, BNI_1), Dutch ZonMw TOP grant (91211021, EMC_1), European Community’s 7th Framwork Programme (FP7/2008-2013, 212652, EMC_1), Physical Sciences Division, SURFsara, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond STAR-MDC (EMC_1), Institut Pasteur, CNRS, INSERM, AP-HP, University Paris 7 Diderot, the BioPsy Labex, the DHU PROTECT, the Bettencourt-Schueller Foundation, the FondaMental Foundation, and the ANR SynDiv (IP_1), Branco Weiss fellowship of the Society in Science ETH Zürich, Marguerite-Marie Delacroix Foundation, Flanders Fund for Scientific Research (FWO project 1521313N, G.0401.12, 1206013N, KUL_3), the Stavros Niarchos Foundation, The Leon Levy Foundation, an endowment provided by Phyllis Green and Randolph Cowen, and Goldman Sachs Gives on behalf of Ram Sundaram (NYU_1), DeStefano Family Foundation, Oregon Clinical and Translational Institute, and Medical Research Foundation (OHSU_1), National Children’s Research Centre Our Lady’s Children’s Hospital (TCD_1), University of Utah Multidisciplinary Research Seed Grant, NRSA Predoctoral Fellowship (F32 DC010143, USM_1), Ben B. and Iris M. Margolis Foundation (USM_1), and UCLA Autism Center for Excellence (UCLA_Long)., Yale School of Medicine [New Haven, Connecticut] (YSM), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), University of California (UC), University of California [San Francisco] (UC San Francisco), and Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich)
Subjects: 0301 basic medicine, Statistics and Probability, Connectomics, Data Descriptor, Autism Spectrum Disorder, [SDV]Life Sciences [q-bio], Functional magnetic resonance imaging, Autism spectrum disorders, Brain imaging, Funcional magnetic resonance imaging, Magnetic resonance imaging, Neuroscience, Neuroimaging, Library and Information Sciences, Education, 03 medical and health sciences, 0302 clinical medicine, medicine, Connectome, Humans, Neural correlates of consciousness, Autism brain imaging data exchange, medicine.disease, 3. Good health, Computer Science Applications, Identification (information), 030104 developmental biology, Autism spectrum disorder, Autism, Statistics, Probability and Uncertainty, Psychology, 030217 neurology & neurosurgery, Information Systems
Abstract: The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity. ispartof: Scientific Data vol:4 ispartof: location:England status: published
Published: 2017
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23. Predicting brain-age from multimodal imaging data captures cognitive impairment

Author: Mehdi Rahim, Leonie Lampe, Daniel S. Margulies, Markus Loeffler, Matthias L. Schroeter, Arno Villringer, R. Cameron Craddock, Frauke Beyer, A. V. Witte, Gaël Varoquaux, Jana Kynast, Franziskus Liem, Tobias Luck, Steffi G. Riedel-Heller, Julia M. Huntenburg, Shahrzad Kharabian Masouleh, Alexandre Abraham, Max-Planck-Institut für Mathematik in den Naturwissenschaften (MPI-MiS), Max-Planck-Gesellschaft, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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-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, 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), University of British Columbia (UBC), Service NEUROSPIN (NEUROSPIN), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Universität Leipzig [Leipzig], Institute for Medical Informatics, Statistics and Epidemiology [Leipzig] (IMISE), 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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Universität Leipzig
Subjects: 0301 basic medicine, Male, Audiology, Neuropsychological Tests, computer.software_genre, Multimodal Imaging, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Early prediction, Cognitive impairment, ComputingMilieux_MISCELLANEOUS, Aged, 80 and over, Cerebral Cortex, Functional connectivity, 05 social sciences, Confounding, Brain, Cognition, Middle Aged, Magnetic Resonance Imaging, Neurology, Head Movements, Female, Psychology, Adult, medicine.medical_specialty, Mean squared prediction error, Cognitive Neuroscience, Models, Neurological, Machine learning, 050105 experimental psychology, 03 medical and health sciences, Functional brain, Young Adult, Predictive Value of Tests, medicine, Humans, 0501 psychology and cognitive sciences, Generalizability theory, Cognitive Dysfunction, Aged, Multimodal imaging, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Reproducibility of Results, 030104 developmental biology, Artificial intelligence, business, Neurocognitive, computer, 030217 neurology & neurosurgery
Abstract: The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.
Published: 2017
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24. Brain-Derived Neurotrophic Factor Signaling Rewrites the Glucocorticoid Transcriptome via Glucocorticoid Receptor Phosphorylation

Author: Chong-Feng Xu, Freddy Jeanneteau, Michael J. Garabedian, Thomas A. Neubert, Moses V. Chao, W. Marcus Lambert, New York University [New York] (NYU), NYU System (NYU), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Institut de Génomique Fonctionnelle (IGF), and Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Tropomyosin receptor kinase B, Dexamethasone, Mice, Glucocorticoid receptor, 0302 clinical medicine, Neurotrophic factors, Phosphorylation, Cyclic AMP Response Element-Binding Protein, Promoter Regions, Genetic, Cells, Cultured, Conserved Sequence, Mice, Knockout, Neurons, 0303 health sciences, biology, Articles, Biological Evolution, Erratum, Signal transduction, Glucocorticoid, medicine.drug, Neurotrophin, Signal Transduction, medicine.medical_specialty, 03 medical and health sciences, Receptors, Glucocorticoid, Internal medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], medicine, Animals, Humans, Receptor, trkB, Glucocorticoids, Molecular Biology, 030304 developmental biology, Brain-derived neurotrophic factor, Brain-Derived Neurotrophic Factor, Cell Biology, Rats, Mice, Inbred C57BL, Endocrinology, HEK293 Cells, nervous system, biology.protein, Mutagenesis, Site-Directed, Transcriptome, 030217 neurology & neurosurgery
Abstract: International audience; Abnormal glucocorticoid and neurotrophin signaling has been implicated in numerous psychiatric disorders. However, the impact of neurotrophic signaling on glucocorticoid receptor (GR)-dependent gene expression is not understood. We therefore examined the impact of brain-derived neurotrophic factor (BDNF) signaling on GR transcriptional regulatory function by gene expression profiling in primary rat cortical neurons stimulated with the selective GR agonist dexamethasone (Dex) and BDNF, alone or in combination. Simultaneous treatment with BDNF and Dex elicited a unique set of GR-responsive genes associated with neuronal growth and differentiation and also enhanced the induction of a large number of Dex-sensitive genes. BDNF via its receptor TrkB enhanced the transcriptional activity of a synthetic GR reporter, suggesting a direct effect of BDNF signaling on GR function. Indeed, BDNF treatment induces the phosphorylation of GR at serine 155 (S155) and serine 287 (S287). Expression of a nonphosphorylatable mutant (GR S155A/S287A) impaired the induction of a subset of BDNF-and Dex-regulated genes. Mechanistically, BDNF-induced GR phosphorylation increased GR occupancy and cofactor recruitment at the promoter of a BDNF-enhanced gene. GR phosphorylation in vivo is sensitive to changes in the levels of BDNF and TrkB as well as stress. Therefore, BDNF signaling specifies and amplifies the GR transcriptome through a coordinated GR phosphorylation-dependent detection mechanism.
Published: 2013
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25. Deletion of Neurotrophin Signaling through the Glucocorticoid Receptor Pathway Causes Tau Neuropathology

Author: Moses V. Chao, Matthias Catteau, Freddy Jeanneteau, Stephen D. Ginsberg, Marie-Laure Parmentier, Camille Peguet, Synphen H. Wu, Margarita Arango-Lievano, Institut de Génomique Fonctionnelle (IGF), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Nathan S. Kline Institute for Psychiatric Research (NKI), and New York State Office of Mental Health
Subjects: 0301 basic medicine, Adult, Male, Dendritic spine, [SDV]Life Sciences [q-bio], Dendritic Spines, Neuropathology, Article, 03 medical and health sciences, Mice, 0302 clinical medicine, Glucocorticoid receptor, Receptors, Glucocorticoid, Neurotrophic factors, Pregnancy, medicine, Animals, Humans, Nerve Growth Factors, Cognitive decline, Phosphorylation, Glucocorticoids, Cells, Cultured, Aged, Aged, 80 and over, Cerebral Cortex, Multidisciplinary, biology, Brain-Derived Neurotrophic Factor, Dual Specificity Phosphatase 1, Middle Aged, 030104 developmental biology, Tauopathies, biology.protein, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, RNA Interference, Neuroscience, 030217 neurology & neurosurgery, Glucocorticoid, Neurotrophin, medicine.drug, Signal Transduction
Abstract: Glucocorticoid resistance is a risk factor for Alzheimer’s disease (AD). Molecular and cellular mechanisms of glucocorticoid resistance in the brain have remained unknown and are potential therapeutic targets. Phosphorylation of glucocorticoid receptors (GR) by brain-derived neurotrophic factor (BDNF) signaling integrates both pathways for remodeling synaptic structure and plasticity. The goal of this study is to test the role of the BDNF-dependent pathway on glucocorticoid signaling in a mouse model of glucocorticoid resistance. We report that deletion of GR phosphorylation at BDNF-responding sites and downstream signaling via the MAPK-phosphatase DUSP1 triggers tau phosphorylation and dendritic spine atrophy in mouse cortex. In human cortex, DUSP1 protein expression correlates with tau phosphorylation, synaptic defects and cognitive decline in subjects diagnosed with AD. These findings provide evidence for a causal role of BDNF-dependent GR signaling in tau neuropathology and indicate that DUSP1 is a potential target for therapeutic interventions.
Published: 2016
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26. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

Author: Michael P. Milham, Gaël Varoquaux, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Alexandre Abraham, Adriana Di Martino, 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, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), 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)), Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Stony Brook University [SUNY] (SBU), State University of New York (SUNY), NiConnect project from Digiteo, France, SUBSample from Digiteo, France, NIMH grant 1R21MH107045-01A1, 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), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0301 basic medicine, FOS: Computer and information sciences, Adult, Adolescent, Autism Spectrum Disorder, Cognitive Neuroscience, autism spectrum disorders, data heterogeneity, Datasets as Topic, Machine Learning (stat.ML), computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, data pipelines, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Connectome, Image Processing, Computer-Assisted, Humans, Multicenter Studies as Topic, Biomarker discovery, Child, Cerebral Cortex, medicine.diagnostic_test, Resting state fMRI, [SCCO.NEUR]Cognitive science/Neuroscience, Multi site, Reproducibility of Results, medicine.disease, Pipeline (software), Magnetic Resonance Imaging, 3. Good health, 030104 developmental biology, Neurology, Sample size determination, FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Autism, Neurons and Cognition (q-bio.NC), Data mining, Functional magnetic resonance imaging, Psychology, computer, 030217 neurology & neurosurgery, resting-state fMRI, Biomarkers
Abstract: Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks., in NeuroImage, Elsevier, 2016
Published: 2016
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27. Updating temporal expectancy of an aversive event engages striatal plasticity under amygdala control

Author: Tatiane Ferreira Tavares, Sophie Höhn, Lorenzo Diaz-Mataix, Glenn E. Schafe, Julie Boulanger Bertolus, Jeroen Knippenberg, Raquel Chacon Ruiz Martinez, Michael Graupner, Nicole El Massioui, Alex D. Reyes, Joseph E. LeDoux, Valérie Doyère, Glenn Dallérac, Anna Verschueren, Lucille Tallot, Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Center for Neural Science [New York] (CNS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Laboratory of Neuromodulation, Teaching and Research Institute, NYU System (NYU), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Department of Psychology [Hunter College], Hunter College [CUNY, New York], and City University of New York [New York] (CUNY)-City University of New York [New York] (CUNY)
Subjects: 0301 basic medicine, Male, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Science, Conditioning, Classical, General Physics and Astronomy, Striatum, Local field potential, Amygdala, General Biochemistry, Genetics and Molecular Biology, Article, Rats, Sprague-Dawley, 03 medical and health sciences, 0302 clinical medicine, Memory, Neuroplasticity, medicine, Animals, Theta Rhythm, Association (psychology), Multidisciplinary, Neuronal Plasticity, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Classical conditioning, General Chemistry, Fear, Time perception, Corpus Striatum, Electrodes, Implanted, Rats, 030104 developmental biology, medicine.anatomical_structure, nervous system, Time Perception, Aversive Stimulus, Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract: Pavlovian aversive conditioning requires learning of the association between a conditioned stimulus (CS) and an unconditioned, aversive stimulus (US) but also involves encoding the time interval between the two stimuli. The neurobiological bases of this time interval learning are unknown. Here, we show that in rats, the dorsal striatum and basal amygdala belong to a common functional network underlying temporal expectancy and learning of a CS–US interval. Importantly, changes in coherence between striatum and amygdala local field potentials (LFPs) were found to couple these structures during interval estimation within the lower range of the theta rhythm (3–6 Hz). Strikingly, we also show that a change to the CS–US time interval results in long-term changes in cortico-striatal synaptic efficacy under the control of the amygdala. Collectively, this study reveals physiological correlates of plasticity mechanisms of interval timing that take place in the striatum and are regulated by the amygdala., Aversive conditioning requires the learning of time intervals between conditioned and unconditioned stimuli, as well as the associations of the stimuli themselves. Here the authors show that dorsal striatum and basal amygdala are part of a functional network that encodes interval timing.
Published: 2016
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28. Brainhack: A collaborative workshop for the open neuroscience community

Author: Caroline Froehlich, David H. O’Connor, Robert C. Welsh, Sook-Lei Liew, Katharine Dunlop, Angela R. Laird, Yonggang Shi, Satrajit S. Ghosh, John W. Van Meter, Pierre-Olivier Quirion, Lucina Q. Uddin, R. Matthew Hutchison, Pierre Bellec, Salma Mesmoudi, Christopher J. Cannistraci, Maarten Mennes, Jonathan Downar, Sebastien Dery, Donald G. McLaren, Ariel Rokem, Prantik Kundu, Daniel S. Margulies, Fernando A. Barrios, Alexandre Rosa Franco, Ramon Fraga Pereira, Andrew J. Gerber, B. Nolan Nichols, R. Cameron Craddock, Erick H. Pasaye, Ziad S. Saad, Felipe Meneguzzi, Benjamin De Leener, Thomas J. Grabowski, Sean Hill, Sarael Alcauter, Julien Cohen-Adad, Daniel J. Lurie, Gautam Prasad, Roberto Toro, Ting Xu, John D. Van Horn, Yves Burnod, Anibal Sólon Heinsfeld, Jean-Baptiste Poline, Scott Peltier, Stephen C. Strother, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, The Neuro Bureau [Leipzig], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Université de Montréal (UdeM), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council, Stanford University, Universidad Nacional Autónoma de México (UNAM), 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), Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-École polytechnique (X), Icahn School of Medicine at Mount Sinai [New York] (MSSM), École Polytechnique de Montréal (EPM), McGill University = Université McGill [Montréal, Canada], University Health Network, University of Toronto, Pontifícia Universidade Católica do Rio Grande do Sul [Porto Alegre] (PUCRS), New York State Psychiatric Institute, Columbia University [New York], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Harvard Medical School [Boston] (HMS), University of Washington [Seattle], International Neuroinformatics Coordinating Facility, Karolinska Institutet [Stockholm], Harvard University [Cambridge], Florida International University [Miami] (FIU), University of Southern California (USC), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Biospective [Montréal], Massachusetts General Hospital [Boston], Donders Institute for Brain, Cognition and Behaviour, Radboud university [Nijmegen], MATRICE Project (ISC-PIF), Sorbonnes university Paris 1, University of Michigan [Ann Arbor], University of Michigan System, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Keck School of Medicine [Los Angeles], eScience Institute Seattle, National Institute of mental health , Bethesda, Rotman Research Institute at the Baycrest Centre (RRI), 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), Miami University, Miami University [Ohio] (MU), University of Miami Leonard M. Miller School of Medicine (UMMSM), Georgetown University Medical Center, We would also like to thank our sponsors, whose funds have been used to enrich the educational experience at Brainhack and have provided travel support for attendees. These include (in alphabetical order): Allen Institute for Brain Science (OHBM 2013), Amazon Web Services (OHBM 2013, OHBM 2014, Boston 2014, Brainhack AMX 2015), Athinoula A. Martinos Center for Biomedical Imaging (Boston 2014), Child Mind Institute, Inc. (NYC 2014, NYC 2015, MX 2015), FIU Division of Research (Miami 2014), Frontiers (OHBM 2014), Frontiers in Neuroscience (OHBM 2013), International Neuroinformatics Coordinating Facility (OHBM 2013, OHBM 2014, OHBM 2015, MX 2015), MATRICE (Paris 2013), Max Planck Institute for Cognitive and Brain Sciences (Leipzig 2012), Microsoft Azure (OHBM 2015), NIH BD2K Center (1U54EB020406-01) Big Data for Discovery Science (USC, PI: Toga, LA 2015), NIH BD2K Center (1U54EB020403-01) Enigma Center for Worldwide Medicine, Imaging, and Genomics (USC, PI: Thompson, LA 2015), NIH BD2K Supplement for NCANDA (3U01AA021697-04S1) and NCANDA: Data Analysis Component (5U01AA021697-04) (SRI International, PI: Pohl, OHBM2015, MX 2015), Organization for Human Brain Mapping (OHBM 2013, OHBM 2014, OHBM 2015), Ontario Brain Institute (Toronto 2014), Quebec Bio-Imaging Network (MTL 2014, MTL 2015), Siemens (Paris 2013), and University of Miami Flipse Funds (Miami 2015).References, HAL UPMC, Gestionnaire, Biotechnology and Biological Sciences Research Council (BBSRC), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), 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), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École polytechnique (X)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Pontifical Catholic University of Rio Grande do Sul (PUC-RS), Radboud University [Nijmegen], Université Paris 1 Panthéon-Sorbonne (UP1), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S., Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Harvard University, and ANR-16-EQPX-0003,Matrice - 13 novembre,Matrice - 13 novembre(2016)
Subjects: 0301 basic medicine, Open science, Biomedical Research, Computer science, International Cooperation, Health Informatics, Review, Education, 03 medical and health sciences, Networking, 0302 clinical medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Cooperative Behavior, Hackathon, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Neurosciences, 220 Statistical Imaging Neuroscience, Brain, Computational Biology, Congresses as Topic, Unconference, Data science, Collaboration, Research Personnel, Computer Science Applications, Data sharing, 030104 developmental biology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neuroscience, 030217 neurology & neurosurgery
Abstract: Contains fulltext : 167121.pdf (Publisher’s version ) (Open Access) Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.
Published: 2016
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29. Competing Mechanisms of Gamma and Beta Oscillations in the Olfactory Bulb Based on Multimodal Inhibition of Mitral Cells Over a Respiratory Cycle

Author: Nicolas Fourcaud-Trocmé, Nathalie Buonviso, Emmanuelle Courtiol, François David, Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Fourcaud-Trocmé, Nicolas, Centre de recherche en neurosciences de Lyon (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0303 health sciences, Chemistry, General Neuroscience, Sensory system, General Medicine, Olfactory bulb, 03 medical and health sciences, computational model, 0302 clinical medicine, Rhythm, Odor, Sniffing, Respiration, top-down processing, Beta Rhythm, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Respiratory cycle, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], gamma oscillations, sensory processing, Neuroscience, Beta oscillations, short-term plasticity, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: International audience; Gamma (ϳ40-90 Hz) and beta (ϳ15-40 Hz) oscillations and their associated neuronal assemblies are key features of neuronal sensory processing. However, the mechanisms involved in either their interaction and/or the switch between these different regimes in most sensory systems remain misunderstood. Based on in vivo recordings and biophysical modeling of the mammalian olfactory bulb (OB), we propose a general scheme where OB internal dynamics can sustain two distinct dynamic states, each dominated by either a gamma or a beta regime. The occurrence of each regime depends on the excitability level of granule cells, the main OB interneurons. Using this model framework, we demonstrate how the balance between sensory and centrifugal input can control the switch between the two oscillatory dynamic states. In parallel, we experimentally observed that sensory and centrifugal inputs to the rat OB could both be modulated by the respiration of the animal (2-12 Hz) and each one phase shifted with the other. Implementing this phase shift in our model resulted in the appearance of the alternation between gamma and beta rhythms within a single respiratory cycle, as in our experimental results under urethane anesthesia. Our theoretical framework can also account for the oscillatory frequency response, depending on the odor intensity, the odor valence, and the animal sniffing strategy observed under various conditions including animal freely-moving. Importantly, the results of the present model can form Significance Statement Neuronal oscillations accompany the sensory perception at multiple timescales. Fast-paced activities (gamma, ϳ40-90 Hz; beta, ϳ15-40 Hz) facilitate discrimination and signal cognitive response. Slower processes (2-12 Hz) gate the time window for sensory and centrifugal inputs to ascend and descend, respectively, relative to sensory relays. In the olfactory bulb, which is the first relay of the olfactory system, the main local interneurons provide a major interface between ascending and descending activities. The balance between these two pathways controls the two types of inhibition released by these interneurons on the main relay cells and thereby the network oscillatory dynamics. Using minimalist computational simulations and in vivo experiments, we proposed a general scheme intimately linked to olfactory processing. Theory/New Concepts 2015, 2(6) e0018-15.2015 1-24 a basis to understand how fast rhythms could be controlled by the slower sensory and centrifugal modulations linked to the respiration.
Published: 2016
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30. Olfactory memory networks: from emotional learning to social behaviors

Author: Regina Marie Sullivan, Donald A Wilson, Nadine eRAVEL, Anne-Marie eMouly, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), RAVEL, Nadine, Child Study Center Institute for Child and Adolescent Psychiatry, Centre de recherche en neurosciences de Lyon (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Olfactory system, Cognitive Neuroscience, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Sensory system, Olfaction, social odors, lcsh:RC321-571, Behavioral Neuroscience, sniffing behavior, Sniffing, Olfactory memory, Episodic memory, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, ComputingMilieux_MISCELLANEOUS, Cognitive science, Recall, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Editorial Article, odor preference, Neuropsychology and Physiological Psychology, odor aversion, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], olfactory memory, Psychology, Neuroscience, Social behavior, olfaction
Abstract: Odors are powerful stimuli that can evoke emotional states, and support learning and memory. Decades of research have indicated that the neural basis for this strong “odor-emotional memory” connection is due to the uniqueness of the anatomy of the olfactory pathways. Indeed, unlike the other sensory systems, the sense of smell does not pass through the thalamus to be routed to the cortex. Rather, odor information is relayed directly to the limbic system, a brain region typically associated with memory and emotional processes. This provides olfaction with a unique and potent power to influence mood, acquisition of new information, and use of information in many different contexts including social interactions. Indeed, olfaction is crucially involved in behaviors essential for survival of the individual and species, including identification of predators, recognition of individuals for procreation or social hierarchy, location of food, as well as attachment between mating pairs and infant-caretaker dyads. Importantly, odors are sampled through sniffing behavior. This active sensing plays an important role in exploratory behaviors observed in the different contexts mentioned above. Odors are also critical for learning and memory about events and places and constitute efficient retrieval cues for the recall of emotional episodic memories. This broad role for odors appears highly preserved across species. In addition, the consistent early developmental emergence of olfactory function across diverse species also provides a unique window of opportunity for analysis of myriad behavioral systems from rodents to nonhuman primates and humans. This, when combined with the relatively conserved organization of the olfactory system in mammals, provides a powerful framework to explore how complex behaviors can be modulated by odors to produce adaptive responses, and to investigate the underlying neural networks. The present research topic brings together cutting edge research on diverse species and developmental stages, highlighting convergence and divergence between humans and animals to facilitate translational research. It is composed of 25 articles and encompasses 5 sections: human olfaction, odor preferences and aversions, odors and social behavior, olfaction and sniffing, and olfactory memory.
Published: 2015
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31. A convergent functional architecture of the insula emerges across imaging modalities

Author: Roberto Toro, Adriana Di Martino, Pierre Bellec, F. Xavier Castellanos, Clare Kelly, Michael P. Milham, Christine L. Cox, New York University [New York] (NYU), NYU System (NYU), Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), Université de Montréal (UdeM), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, This work was supported by grants from the National Institute on Drug Abuse (R03DA024775 to CK, 2T32DA007254-16A2 to CLC and R01DA016979 to FXC), the National Institute of Mental Health (R01MH083246 and R01MH081218 to FXC and MPM, K23MH087770 to ADM), the National Institute of Child Health and Human Development (R01HD065282 to FXC), the Leon Levy Foundation (ADM, CK and MPM), as well as Autism Speaks, the Stavros Niarchos Foundation, and the Alicia Koplowitz Foundation (FXC)., and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cognitive Neuroscience, [SDV]Life Sciences [q-bio], Neuroimaging, behavioral disciplines and activities, Article, 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, Neural Pathways, Cluster Analysis, Humans, Cluster analysis, 030304 developmental biology, Cerebral Cortex, 0303 health sciences, Modalities, Functional connectivity, Cognition, Coactivation, Neurology, Structural covariance, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, Insula, Neuroscience, 030217 neurology & neurosurgery, psychological phenomena and processes
Abstract: International audience; Empirical evidence increasingly supports the hypothesis that patterns of intrinsic functional connectivity (iFC) are sculpted by a history of evoked coactivation within distinct neuronal networks. This, together with evidence of strong correspondence among the networks defined by iFC and those delineated using a variety of other neuroimaging techniques, suggests a fundamental brain architecture detectable across multiple functional and structural imaging modalities. Here, we leverage this insight to examine the functional organization of the human insula. We parcellated the insula on the basis of three distinct neuroimaging modalities task evoked coactivation, intrinsic (i.e., task-independent) functional connectivity, and gray matter structural covariance. Clustering of these three different covariance-based measures revealed a convergent elemental organization of the insula that likely reflects a fundamental brain architecture governing both brain structure and function at multiple spatial scales. While not constrained to be hierarchical, our parcella-tion revealed a pseudo-hierarchical, multiscale organization that was consistent with previous clustering and meta-analytic studies of the insula. Finally, meta-analytic examination of the cognitive and behavioral domains associated with each of the insular clusters obtained elucidated the broad functional dissociations likely underlying the topography observed. To facilitate future investigations of insula function across healthy and pathological states, the insular parcels have been made freely available for download via http:// fcon_1000.projects.nitrc.org, along with the analytic scripts used to perform the parcellations.
Published: 2011
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32. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

Author: Joo Hyun Song, J. John Mann, D. Louis Collins, Pierre Hellier, Brian B. Avants, John Ashburner, Daniel Rueckert, Gary E. Christensen, Mark Jenkinson, Jesper L. R. Andersson, Paul M. Thompson, Roger P. Woods, Ramin V. Parsey, James C. Gee, Arno Klein, Tom Vercauteren, Claude Lepage, Babak A. Ardekani, Ming Chang Chiang, New York State Psychiatric Institute, Columbia University [New York], Department of Clinical Neurology [Oxford], University of Oxford [Oxford]-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), Functional Imaging Laboratory (FIL), University College of London [London] (UCL), Penn Image Computing & Science Lab [Philadelphia] (PICSL), University of Pennsylvania [Philadelphia], Laboratory of Neuro Imaging [Los Angeles] (LONI), University of California [Los Angeles] (UCLA), University of California-University of California, Department of Electrical and Computer Engineering [Iowa], University of Iowa [Iowa City], McConnell Brain Imaging Center (BIC), McGill University = Université McGill [Montréal, Canada], 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 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Visual Information Processing (VIP), Imperial College London, Mauna Kea Technologies, Analysis and Simulation of Biomedical Images (ASCLEPIOS), 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), Department of Neurology [UCLA], University of California-University of California-David Geffen School of Medicine [Los Angeles], University of Oxford-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], University of Pennsylvania, University of California (UC)-University of California (UC), 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)-Télécom Bretagne-CentraleSupélec-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of California (UC)-University of California (UC)-David Geffen School of Medicine [Los Angeles], and Vercauteren, Tom
Subjects: Male, MESH: Subtraction Technique, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Normalization (image processing), [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, computer.software_genre, Pattern Recognition, Automated, 030218 nuclear medicine & medical imaging, MESH: Magnetic Resonance Imaging, 0302 clinical medicine, MESH: Pattern Recognition, Automated, Segmentation, medicine.diagnostic_test, Brain, Magnetic Resonance Imaging, MESH: Reproducibility of Results, MESH: Nonlinear Dynamics, medicine.anatomical_structure, Neurology, Pattern recognition (psychology), Female, MESH: Image Enhancement, Algorithm, Algorithms, Adult, Cognitive Neuroscience, Image registration, MESH: Algorithms, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, Machine learning, Sensitivity and Specificity, Article, [SDV.IB.MN] Life Sciences [q-bio]/Bioengineering/Nuclear medicine, 03 medical and health sciences, MESH: Brain, Neuroimaging, Artificial Intelligence, Atlas (anatomy), Image Interpretation, Computer-Assisted, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, MESH: Artificial Intelligence, MESH: Humans, business.industry, Reproducibility of Results, Magnetic resonance imaging, MESH: Adult, Image Enhancement, MESH: Sensitivity and Specificity, MESH: Male, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Nonlinear Dynamics, Subtraction Technique, Spatial normalization, Artificial intelligence, business, computer, MESH: Female, MESH: Image Interpretation, Computer-Assisted, 030217 neurology & neurosurgery
Abstract: International audience; All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
Published: 2009
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33. Detection of a Temporal Error Triggers Reconsolidation of Amygdala-Dependent Memories

Author: Joseph E. LeDoux, Raquel Chacon Ruiz Martinez, Lorenzo Diaz-Mataix, Glenn E. Schafe, Valérie Doyère, Center for Neural Science, New York University, University of Sao Paulo Medical School, Surgery Department, Department of Psychology, Yale University, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Centre de Neurosciences Paris-Sud (CNPS), and Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)
Subjects: Time Factors, Conditioning, Classical, Biology, Traumatic memories, Amygdala, General Biochemistry, Genetics and Molecular Biology, Article, Rats, Sprague-Dawley, 03 medical and health sciences, 0302 clinical medicine, Basic knowledge, Electrical Synapses, Time windows, Memory, medicine, Animals, Association (psychology), GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), 030304 developmental biology, Early Growth Response Protein 1, 0303 health sciences, Agricultural and Biological Sciences(all), Biochemistry, Genetics and Molecular Biology(all), Classical conditioning, Fear, Immunohistochemistry, Rats, medicine.anatomical_structure, Acoustic Stimulation, Synaptic plasticity, Memory consolidation, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], General Agricultural and Biological Sciences, Neuroscience, 030217 neurology & neurosurgery
Abstract: International audience; Updating memories is critical for adaptive behaviors, but the rules and mechanisms governing that process are still not well defined. During a limited time window, the reactivation of consolidated aversive memories triggers memory lability and induces a plasticity-dependent reconsolidation process in the lateral nucleus of amygdala (LA) [1-5]. However, whether new information is necessary for initiating reconsolidation is not known. Here we show that changing the temporal relationship between the conditioned stimulus (CS) and unconditioned stimulus (US) during reactivation is sufficient to trigger synaptic plasticity and reconsolidation of an aversive memory in the LA. These findings demonstrate that time is a core part of the CS-US association and that new information must be presented during reactivation in order to trigger LA-dependent reconsolidation processes. In sum, this study provides new basic knowledge about the precise rules governing memory reconsolidation of aversive memories that might be used to treat traumatic memories.
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34. Developing and using the Neuroimaging and Data Sharing Data Model: the NIDASH Working Group

Author: Keator, David, Ghosh, Satrajit, Maumet, Camille, Flandin, Guillaume, Nichols, Nolan, Nichols, Thomas, Burns, Gully, Brüehl, Rüdiger, Craddock, Cameron, Frederick, Blaise, Gorgolewski, Krzysztof, Marcus, Daniel, Hanke, Michael, Haselgrove, Christian, Helmer, Karl, Klein, Arno, Milham, Michael, Poldrack, Russell, Michel, Franck, Steffener, Jason, Schwartz, Yannick, Stoner, Rich, Turner, Jessica, Kennedy, David, Jean-Baptiste Poline, University of California [Irvine] (UC Irvine), University of California (UC), Massachusetts Institute of Technology (MIT), University of Warwick [Coventry], University College of London [London] (UCL), University of Washington [Seattle], University of Southern California (USC), Physikalisch-Technische Bundesanstalt [Braunschweig] (PTB), Child Mind Institute, McLean Hospital [Belmont, Ma.], Max-Planck-Institut, Department of Radiology, Washington University School of Medicine, Washington University School of Medicine, Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Massachusetts General Hospital [Boston], New York State Psychiatric Institute, Columbia University [New York], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, University of Texas at Austin [Austin], Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe MODALIS, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-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)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of California [San Diego] (UC San Diego), Georgia State University, University System of Georgia (USG), Child and Adolescent NeuroDevelopment Initiative (CANDI), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS)-Division of Neuroinformatics, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), University of California [Irvine] (UCI), University of California, Otto-von-Guericke University [Magdeburg] (OVGU), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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, and Maumet, Camille
Subjects: [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ComputingMilieux_MISCELLANEOUS
Abstract: Poster submitted to the 2014 Organization for Human Brain Mapping (OHBM) conference in Hamburg, 8-12 June.

35. Arrow of Time, Entropy, and Protein Folding: Holistic View on Biochirality.

Author: Dyakin VV and Uversky VN
Subjects: Adenosine Triphosphate, Entropy, Peptides, Thermodynamics, Protein Folding, Proteins chemistry
Abstract: Chirality is a universal phenomenon, embracing the space-time domains of non-organic and organic nature. The biological time arrow, evident in the aging of proteins and organisms, should be linked to the prevalent biomolecular chirality. This hypothesis drives our exploration of protein aging, in relation to the biological aging of an organism. Recent advances in the chirality discrimination methods and theoretical considerations of the non-equilibrium thermodynamics clarify the fundamental issues, concerning the biphasic, alternative, and stepwise changes in the conformational entropy associated with protein folding. Living cells represent open, non-equilibrium, self-organizing, and dissipative systems. The non-equilibrium thermodynamics of cell biology are determined by utilizing the energy stored, transferred, and released, via adenosine triphosphate (ATP). At the protein level, the synthesis of a homochiral polypeptide chain of L-amino acids (L-AAs) represents the first state in the evolution of the dynamic non-equilibrium state of the system. At the next step the non-equilibrium state of a protein-centric system is supported and amended by a broad set of posttranslational modifications (PTMs). The enzymatic phosphorylation, being the most abundant and ATP-driven form of PTMs, illustrates the principal significance of the energy-coupling, in maintaining and reshaping the system. However, the physiological functions of phosphorylation are under the permanent risk of being compromised by spontaneous racemization. Therefore, the major distinct steps in protein-centric aging include the biosynthesis of a polypeptide chain, protein folding assisted by the system of PTMs, and age-dependent spontaneous protein racemization and degradation. To the best of our knowledge, we are the first to pay attention to the biphasic, alternative, and stepwise changes in the conformational entropy of protein folding. The broader view on protein folding, including the impact of spontaneous racemization, will help in the goal-oriented experimental design in the field of chiral proteomics.
Published: 2022
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36. Fundamental Clock of Biological Aging: Convergence of Molecular, Neurodegenerative, Cognitive and Psychiatric Pathways: Non-Equilibrium Thermodynamics Meet Psychology.

Author: Dyakin VV, Dyakina-Fagnano NV, Mcintire LB, and Uversky VN
Subjects: Animals, Humans, Thermodynamics, Aging, Biological Clocks, Cognition, Nerve Degeneration pathology, Nerve Degeneration psychology
Abstract: In humans, age-associated degrading changes, widely observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psychological abilities, and memory. Cross-talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, genome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirality, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP ), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging related to irreversible PTMs SP has been associated with the nontrivial interplay between somatic (molecular aging) and mental (psychological aging) health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated "collapse" of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet underappreciated hallmark of aging. We provide the experimental arguments in support of the racemization theory of aging.
Published: 2021
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37. Racemization in Post-Translational Modifications Relevance to Protein Aging, Aggregation and Neurodegeneration: Tip of the Iceberg.

Author: Dyakin VV, Wisniewski TM, and Lajtha A
Abstract: Homochirality of DNA and prevalent chirality of free and protein-bound amino acids in a living organism represents the challenge for modern biochemistry and neuroscience. The idea of an association between age-related disease, neurodegeneration, and racemization originated from the studies of fossils and cataract disease. Under the pressure of new results, this concept has a broader significance linking protein folding, aggregation, and disfunction to an organism's cognitive and behavioral functions. The integrity of cognitive function is provided by a delicate balance between the evolutionarily imposed molecular homo-chirality and the epigenetic/developmental impact of spontaneous and enzymatic racemization. The chirality of amino acids is the crucial player in the modulation the structure and function of proteins, lipids, and DNA. The collapse of homochirality by racemization is the result of the conformational phase transition. The racemization of protein-bound amino acids (spontaneous and enzymatic) occurs through thermal activation over the energy barrier or by the tunnel transfer effect under the energy barrier. The phase transition is achieved through the intermediate state, where the chirality of alpha carbon vanished. From a thermodynamic consideration, the system in the homo-chiral (single enantiomeric) state is characterized by a decreased level of entropy. The oscillating protein chirality is suggesting its distinct significance in the neurotransmission and flow of perceptual information, adaptive associative learning, and cognitive laterality. The common pathological hallmarks of neurodegenerative disorders include protein misfolding, aging, and the deposition of protease-resistant protein aggregates. Each of the landmarks is influenced by racemization. The brain region, cell type, and age-dependent racemization critically influence the functions of many intracellular, membrane-bound, and extracellular proteins including amyloid precursor protein (APP), TAU, PrP, Huntingtin, α-synuclein, myelin basic protein (MBP), and collagen. The amyloid cascade hypothesis in Alzheimer's disease (AD) coexists with the failure of amyloid beta (Aβ) targeting drug therapy. According to our view, racemization should be considered as a critical factor of protein conformation with the potential for inducing order, disorder, misfolding, aggregation, toxicity, and malfunctions., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.
Published: 2021
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38. Sex-specific neurobiological actions of prophylactic (R,S)-ketamine, (2R,6R)-hydroxynorketamine, and (2S,6S)-hydroxynorketamine.

Author: Chen BK, Luna VM, LaGamma CT, Xu X, Deng SX, Suckow RF, Cooper TB, Shah A, Brachman RA, Mendez-David I, David DJ, Gardier AM, Landry DW, and Denny CA
Subjects: Animals, Electrophysiological Phenomena, Female, Hippocampus metabolism, Male, Mice, Receptors, N-Methyl-D-Aspartate metabolism, Ketamine analogs & derivatives, Ketamine pharmacology
Abstract: Enhancing stress resilience in at-risk populations could significantly reduce the incidence of stress-related psychiatric disorders. We have previously reported that the administration of (R,S)-ketamine prevents stress-induced depressive-like behavior in male mice, perhaps by altering α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-mediated transmission in hippocampal CA3. However, it is still unknown whether metabolites of (R,S)-ketamine can be prophylactic in both sexes. We administered (R,S)-ketamine or its metabolites (2R,6R)-hydroxynorketamine ((2R,6R)-HNK) and (2S,6S)-hydroxynorketamine ((2S,6S)-HNK) at various doses 1 week before one of a number of stressors in male and female 129S6/SvEv mice. Patch clamp electrophysiology was used to determine the effect of prophylactic drug administration on glutamatergic activity in CA3. To examine the interaction between ovarian hormones and stress resilience, female mice also underwent ovariectomy (OVX) surgery and a hormone replacement protocol prior to drug administration. (2S,6S)-HNK and (2R,6R)-HNK protected against distinct stress-induced behaviors in both sexes, with (2S,6S)-HNK attenuating learned fear in male mice, and (2R,6R)-HNK preventing stress-induced depressive-like behavior in both sexes. (R,S)-ketamine and (2R,6R)-HNK, but not (2S,6S)-HNK, attenuated large-amplitude AMPAR-mediated bursts in hippocampal CA3. All three compounds reduced N-methyl-D-aspartate receptor (NMDAR)-mediated currents 1 week after administration. Furthermore, ovarian-derived hormones were necessary for and sufficient to restore (R,S)-ketamine- and (2R,6R)-HNK-mediated prophylaxis in female mice. Our data provide further evidence that resilience-enhancing prophylactics may alter AMPAR-mediated glutamatergic transmission in CA3. Moreover, we show that prophylactics against stress-induced depressive-like behavior can be developed in a sex-specific manner and demonstrate that ovarian hormones are necessary for the prophylactic efficacy of (R,S)-ketamine and (2R,6R)-HNK in female mice.
Published: 2020
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39. Chiral Interface of Amyloid Beta (Aβ): Relevance to Protein Aging, Aggregation and Neurodegeneration.

Author: Dyakin VV, Wisniewski TM, and Lajtha A
Abstract: Biochirality is the subject of distinct branches of science, including biophysics, biochemistry, the stereochemistry of protein folding, neuroscience, brain functional laterality and bioinformatics. At the protein level, biochirality is closely associated with various post-translational modifications (PTMs) accompanied by the non-equilibrium phase transitions (PhTs NE ). PTMs NE support the dynamic balance of the prevalent chirality of enzymes and their substrates. The stereoselective nature of most biochemical reactions is evident in the enzymatic (Enz) and spontaneous (Sp) PTMs (PTMs Enz and PTMs Sp ) of proteins. Protein chirality, which embraces biophysics and biochemistry, is a subject of this review. In this broad field, we focus attention to the amyloid-beta (Aβ) peptide, known for its essential cellular functions and associations with neuropathology. The widely discussed amyloid cascade hypothesis (ACH) of Alzheimer's disease (AD) states that disease pathogenesis is initiated by the oligomerization and subsequent aggregation of the Aβ peptide into plaques. The racemization-induced aggregation of protein and RNA have been extensively studied in the search for the contribution of spontaneous stochastic stereo-specific mechanisms that are common for both kinds of biomolecules. The failure of numerous Aβ drug-targeting therapies requires the reconsolidation of the ACH with the concept of PTMs Sp . The progress in methods of chiral discrimination can help overcome previous limitations in the understanding of AD pathogenesis. The primary target of attention becomes the network of stereospecific PTMs that affect the aggregation of many pathogenic agents, including Aβ. Extensive recent experimental results describe the truncated, isomerized and racemized forms of Aβ and the interplay between enzymatic and PTMs Sp . Currently, accumulated data suggest that non-enzymatic PTMs Sp occur in parallel to an existing metabolic network of enzymatic pathways, meaning that the presence and activity of enzymes does not prevent non-enzymatic reactions from occurring. PTMs Sp impact the functions of many proteins and peptides, including Aβ. This is in logical agreement with the silently accepted racemization hypothesis of protein aggregation (RHPA). Therefore, the ACH of AD should be complemented by the concept of PTMs Sp and RHPA., Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest.
Published: 2020
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