Search

Your search keyword '"Lehéricy, Stéphane"' showing total 934 results
Start Over Author "Lehéricy, Stéphane"
934 results on '"Lehéricy, Stéphane"'

3. Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data

Author

Fu, Guanghui, Jimenez, Gabriel, Loizillon, Sophie, Chougar, Lydia, Dormont, Didier, Valabregue, Romain, Burgos, Ninon, Lehéricy, Stéphane, Racoceanu, Daniel, Colliot, Olivier, and Group, the ICEBERG Study

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

One often lacks sufficient annotated samples for training deep segmentation models. This is in particular the case for less common imaging modalities such as Quantitative Susceptibility Mapping (QSM). It has been shown that deep models tend to fit the target function from low to high frequencies. One may hypothesize that such property can be leveraged for better training of deep learning models. In this paper, we exploit this property to propose a new training method based on frequency-domain disentanglement. It consists of two main steps: i) disentangling the image into high- and low-frequency parts and feature learning; ii) frequency-domain fusion to complete the task. The approach can be used with any backbone segmentation network. We apply the approach to the segmentation of the red and dentate nuclei from QSM data which is particularly relevant for the study of parkinsonian syndromes. We demonstrate that the proposed method provides considerable performance improvements for these tasks. We further applied it to three public datasets from the Medical Segmentation Decathlon (MSD) challenge. For two MSD tasks, it provided smaller but still substantial improvements (up to 7 points of Dice), especially under small training set situations., Comment: 10 pages

Published
2023

4. Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI

Author

Fu, Guanghui, Jimenez, Gabriel, Loizillon, Sophie, Jurdi, Rosana El, Chougar, Lydia, Dormont, Didier, Valabregue, Romain, Burgos, Ninon, Lehéricy, Stéphane, Racoceanu, Daniel, Colliot, Olivier, and Group, the ICEBERG Study

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Early and accurate diagnosis of parkinsonian syndromes is critical to provide appropriate care to patients and for inclusion in therapeutic trials. The red nucleus is a structure of the midbrain that plays an important role in these disorders. It can be visualized using iron-sensitive magnetic resonance imaging (MRI) sequences. Different iron-sensitive contrasts can be produced with MRI. Combining such multimodal data has the potential to improve segmentation of the red nucleus. Current multimodal segmentation algorithms are computationally consuming, cannot deal with missing modalities and need annotations for all modalities. In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation. The method consists of three main stages. First, it disentangles the image into high-level information representing the brain structure, and low-frequency information representing the contrast. The high-frequency information is then fed into a network to learn anatomical features, while the list of multimodal low-frequency information is processed by another module. Finally, feature fusion is performed to complete the segmentation task. The proposed method was used with several iron-sensitive contrasts (iMag, QSM, R2*, SWI). Experiments demonstrate that our proposed model substantially outperforms a baseline UNet model when the training set size is very small.

Published
2022

5. Volumetric changes and clinical trajectories in Parkinson’s disease: a prospective multicentric study

Author

Marques, Ana, Macias, Elise, Pereira, Bruno, Durand, Elodie, Chassain, Carine, Vidal, Tiphaine, Defebvre, Luc, Carriere, Nicolas, Fraix, Valerie, Moro, Elena, Thobois, Stéphane, Metereau, Elise, Mangone, Graziella, Vidailhet, Marie, Corvol, Jean-Christophe, Lehéricy, Stéphane, Menjot de Champfleur, Nicolas, Geny, Christian, Spampinato, Umberto, Meissner, Wassilios G., Frismand, Solène, Schmitt, Emmanuelle, Doé de Maindreville, Anne, Portefaix, Christophe, Remy, Philippe, Fénelon, Gilles, Houeto, Jean Luc, Colin, Olivier, Rascol, Olivier, Peran, Patrice, Bonny, Jean-Marie, Fantini, Maria Livia, and Durif, Franck

Published
2023
Full Text
View/download PDF

6. Learning brain MRI quality control: a multi-factorial generalization problem

Author

Reguig, Ghiles, Chupin, Marie, Dary, Hugo, Bardinet, Eric, Lehéricy, Stéphane, and Valabregue, Romain

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Due to the growing number of MRI data, automated quality control (QC) has become essential, especially for larger scale analysis. Several attempts have been made in order to develop reliable and scalable QC pipelines. However, the generalization of these methods on new data independent of those used for learning is a difficult problem because of the biases inherent in MRI data. This work aimed at evaluating the performances of the MRIQC pipeline on various large-scale datasets (ABIDE, N = 1102 and CATI derived datasets, N = 9037) used for both training and evaluation purposes. We focused our analysis on the MRIQC preprocessing steps and tested the pipeline with and without them. We further analyzed the site-wise and study-wise predicted classification probability distributions of the models without preprocessing trained on ABIDE and CATI data. Our main results were that a model using features extracted from MRIQC without preprocessing yielded the best results when trained and evaluated on large multi-center datasets with a heterogeneous population (an improvement of the ROC-AUC score on unseen data of 0.10 for the model trained on a subset of the CATI dataset). We concluded that a model trained with data from a heterogeneous population, such as the CATI dataset, provides the best scores on unseen data. In spite of the performance improvement, the generalization abilities of the models remain questionable when looking at the site-wise/study-wise probability predictions and the optimal classification threshold derived from them.

Published
2022

7. Global displacement induced by rigid motion simulation during MRI acquisition

Author

Reguig, Ghiles, Lapert, Marc, Lehericy, Stephane, and Valabregue, Romian

Subjects
Physics - Medical Physics
Abstract

Purpose: Simulation of motion in the frequency domain during the MRI acquisition process is an important tool to study the effect of object motion. It is natural in this context to rely on a voxel-to-voxel difference metric between the original image and its motion-simulated version to quantify the alteration. These metrics are very sensitive to any mis-coregistration between the two images. As we observed global displacements of the corrupted image for specific simulated motions, we focused this work on the critical choice of a reference position of the object for the motion simulation that avoids any global displacement. Methods: We used the motion simulation framework and we studied the different proposed solutions. Classically, the position around the k-space center is considered as a reference with two analytical solutions to correct for global image shifts: demeaning the image in the Fourier domain either using the exact position at k-space center or using a weighted average around the center. Results: We demonstrated with motion simulated examples that these analytical solutions did not yield the same results as a direct co-registration method. Indeed, short transitions around the k-space center did not induce any global displacement, in contrast with what both analytical solutions assumed. We also observed a dependence of the global shift to both the motion amplitude and the translation direction. Conclusion: In the absence of a theoretical solution, we concluded that an extra co-registration step after motion simulation was needed in order to compare the simulation with its reference image., Comment: 8 pages, 6 figures

Published
2022

9. Sustained reduction of essential tremor with low-power non-thermal transcranial focused ultrasound stimulations in humans

Author

Bancel, Thomas, Béranger, Benoît, Daniel, Maxime, Didier, Mélanie, Santin, Mathieu, Rachmilevitch, Itay, Shapira, Yeruham, Tanter, Mickael, Bardinet, Eric, Fernandez Vidal, Sara, Attali, David, Galléa, Cécile, Dizeux, Alexandre, Vidailhet, Marie, Lehéricy, Stéphane, Grabli, David, Pyatigorskaya, Nadya, Karachi, Carine, Hainque, Elodie, and Aubry, Jean-François

Published
2024
Full Text
View/download PDF

10. Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T

Author

Genovese, Guglielmo, Palombo, Marco, Santin, Mathieu D., Valette, Julien, Ligneul, Clémence, Aigrot, Marie-Stéphane, Abdoulkader, Nasteho, Langui, Dominique, Millecamps, Aymeric, Evercooren, Anne Baron-Van, Stankoff, Bruno, Lehericy, Stéphane, Petiet, Alexandra, and Branzoli, Francesca

Subjects
Physics - Medical Physics
Abstract

Inflammation of brain tissue is a complex response of the immune system to the presence of toxic compounds or to cell injury, leading to a cascade of pathological processes that include glial cell activation. Noninvasive magnetic resonance imaging markers of glial reactivity would be very useful for in vivo detection and monitoring of inflammation processes in the brain, as well as for evaluating the efficacy of personalized treatments. Due to their specific location in glial cells, myo-inositol (mIns) and choline compounds (tCho) seem the best candidates for probing glial-specific intra-cellular compartments. However, their concentrations quantified using conventional proton magnetic resonance spectroscopy (MRS) are not specific for inflammation. In contrast, it has been recently suggested that mIns intra-cellular diffusion, measured using diffusion-weighted MRS (DW-MRS) in a mouse model of reactive astrocytes, could be a specific marker of astrocytic hypertrophy. In order to evaluate the specificity of both mIns and tCho diffusion to inflammation-driven glial alterations, we performed DW-MRS in the corpus callosum of cuprizone-fed mice after 6 weeks of intoxication and evaluated the extent of astrocytic and microglial alterations using immunohistochemistry. Both mIns and tCho apparent diffusion coefficients were significantly elevated in cuprizone-fed mice compared to control mice, while histologic evaluation confirmed the presence of severe inflammation. Additionally, mIns and tCho diffusion showed, respectively, strong and moderate correlations with histological measures of astrocytic and microglial area fractions, confirming DW-MRS as a promising tool for specific detection of glial changes under pathological conditions., Comment: 31 pages, 9 figures

Published
2020

11. X-vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection from Speech

Author

Jeancolas, Laetitia, Petrovska-Delacrétaz, Dijana, Mangone, Graziella, Benkelfat, Badr-Eddine, Corvol, Jean-Christophe, Vidailhet, Marie, Lehéricy, Stéphane, and Benali, Habib

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, Quantitative Biology - Quantitative Methods
Abstract

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect an early stage of PD from voice analysis. X-vectors are embeddings extracted from a deep neural network, which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients - Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (including recently diagnosed PD subjects and healthy controls) with a high-quality microphone and with their own telephone. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of audio segment duration, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7 to 15% improvement). This result was observed for both recording types (high-quality microphone and telephone)., Comment: Submitted

Published
2020
Full Text
View/download PDF

13. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

Author

Hill, Ioana, Palombo, Marco, Santin, Mathieu, Branzoli, Francesca, Philippe, Anne-Charlotte, Wassermann, Demian, Aigrot, Marie-Stephane, Stankoff, Bruno, Evercooren, Anne Baron-Van, Felfi, Mehdi, Langui, Dominique, Zhang, Hui, Lehericy, Stephane, Petiet, Alexandra, Alexander, Daniel C., Ciccarelli, Olga, and Drobnjak, Ivana

Subjects
Physics - Medical Physics, Quantitative Biology - Neurons and Cognition
Abstract

The intra-axonal water exchange time {\tau}i, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI is sensitive to changes in permeability, however, the parameter has remained elusive due to the intractability of the mathematical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters, and recently, a theoretical framework using a random forest (RF) suggests this is a promising approach. In this study, we adopt such an RF approach and experimentally investigate its suitability as a biomarker for demyelinating pathologies through direct comparison with histology. For this, we use an in-vivo cuprizone (CPZ) mouse model of demyelination with available ex-vivo electron microscopy (EM) data. We test our model on noise-free simulations and find very strong correlations between the predicted and ground truth parameters. For realistic noise levels as in our in-vivo data, the performance is affected, however, the parameters are still well estimated. We apply our RF model on in-vivo data from 8 CPZ and 8 wild-type (WT) mice and validate the RF estimates using histology. We find a strong correlation between the in-vivo RF estimates of {\tau}i and the EM measurements of myelin thickness ({\rho_\tau}i = 0.82), and between RF estimates and EM measurements of intra-axonal volume fraction ({\rho_f} = 0.98). When comparing {\tau}i in CPZ and WT mice we find a statistically significant decrease in the corpus callosum of the CPZ compared to the WT mice, in line with our expectations that {\tau}i is lower in regions where the myelin sheath is damaged. Overall, these results demonstrate the suitability of machine learning compartment models with permeability as a potential biomarker for demyelinating pathologies.

Published
2019

14. Ground-Truth Segmentation of the Spinal Cord from 3T MR Images Using Evolutionary Computation

Author

EL Mendili, Mohamed Mounir, Villard, Noémie, Tiret, Brice, Chen, Raphaël, Galanaud, Damien, Magnin, Benoit, Lehericy, Stéphane, Pradat, Pierre-François, Lutton, Evelyne, Mesmoudi, Salma, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jiménez Laredo, Juan Luis, editor, Hidalgo, J. Ignacio, editor, and Babaagba, Kehinde Oluwatoyin, editor

Published
2022
Full Text
View/download PDF

18. Ultra-short time-echo based ray tracing for transcranial focused ultrasound aberration correction in human calvaria

Author

Manuel, Thomas J, primary, Bancel, Thomas, additional, Tiennot, Thomas, additional, Didier, Mélanie, additional, Santin, Mathieu, additional, Daniel, Maxime, additional, Attali, David, additional, Tanter, Mickael, additional, Lehéricy, Stéphane, additional, Pyatigorskaya, Nadya, additional, and Aubry, Jean-Francois, additional

Published
2024
Full Text
View/download PDF

22. Personality assessment with Temperament and Character Inventory in Parkinson's disease

Author

Moreau, Caroline, Defebvre, Pr Luc, Carriere, Dr Nicolas, Grolez, Dr Guillaume, Baille, Dr Guillaume, Kreisler, Dr, Jean-Pierre Pruvo, Pr, Leclerc, Pr, Lopes, Dr Renaud, Viard, Dr Romain, Kuchcinski, Dr Gregory, Dumont, Mr Julien, Dujardin, Pr Kathy, Delliaux, Mme M., Brion, Mrs M., Touzet, Dr Gustavo, Reyns, Pr Nicolas, Delval, Pr Arnaud, Santraine, Mrs Valerie, Pleuvret, Mrs Marie, Dautrevaux, Mrs Nolwen, Laugeais, Mr Victor, Ouk, Thavarak, Potey, Camille, Leclercq, Celine, Gers, Elise, Corvol, Jean-Christophe, Marie-Vidailhet, Hainque, Elodie, Welter, Marie-Laure, Lacomblez, Lucette, Grabli, David, Roze, Emmanuel, Worbe, Yulia, Delorme, Cécile, You, Hana, Ihle, Jonas, Guimeraes-Costa, Raquel, Cormier-Dequaire, Florence, Méneret, Aurélie, Hartmann, Andréas, Mariani, Louise-Laure, Lehericy, Stéphane, Czernecki, Virginie, Pineau, Fanny, Bozon, Frédérique, Huiban, Camille, Benchetrit, Eve, Karachi, Carine, Navarro, Soledad, Cornu, Philippe, Welaratne, Arlette, Dongmo-Kenfack, Carole, Mantisi, Lise, Jarry, Nathalie, Aix, Sophie, Lefort, Carine, Rouaud, Dr Tiphaine, Damier, Pr Philippe, Derkinderen, Pr Pascal, Anne-Gaelle Corbille, Dr, Calvier-Auffray, Dr Elisabeth, Rocher, Mrs Laetitia, Anne-Laure Deruet, Mrs, Sylvie, Dr Raoul, Vincent, Dr Roualdes, Le Dily Séverine, Mrs, Marques, Dr Ana, Debilly, Dr Berangere, Durif, Pr Franck, Derost, Dr Philippe, Beal, Dr Charlotte, Chassain, Carine, Delaby, Laure, Vidal, Tiphaine, Jacques Lemaire, Pr Jean, Rieu, Isabelle, Durand, Elodie, Eusebio, Pr Alexandre, Jean-Philippe Azulay, Pr, Witjas, Dr Tatiana, Fluchère, Dr Frédérique, Grimaldi, Dr Stephan, Girard, Pr Nadine, Delfini, Marie, Carron, Dr Romain, Regis, Pr Jean, Spatola, Dr Giorgio, Magnaudet, Camille, Solène, Dr Ansquer, Isabelle, Dr Benatru, Olivier, Dr Colin, Houeto Jl, Pr, Remy, Pr Guillevin, Anne, Mrs Fradet, Manssouri, Mrs Anziza, Sophie, Mrs Blondeau, Philippe, Dr Richard, Philippe, Dr Cam, Philippe, Dr Page, Benoit, Pr Bataille, Emilie, Mrs Rabois, Annie, Mrs Guillemain, Sophie, Dr Drapier, Leh, Dr Frédérique, Bonnet, Dr Alexandre, Vérin, Pr Marc, Jean-Christophe Ferré, Dr, François Houvenaghel, Mr Jean, Haegelen, Pr Claire, Kestens, Mrs Francoise, Ory, Mrs Solenn, Burbaud, Pr Pierre, Damon-Perriere, Dr Nathalie, Meissner, Pr Wassilios, Tison, Pr Francois, Bannier, Dr Stéphanie, Krim, Dr Elsa, Guehl, Pr Dominique, Molinier-Blossier, Sandrine, Ollivier, Morgan, Lacoste, Marion, Auzou, Nicolas, Bonnet, Marie, Cuny, Pr Emmanuel, Engelhardt, Dr Julien, Branchard, Olivier, Huet, Clotilde, Blanchard, Julie, Olivier, Pr Rascol, Brefel Courbon, Dr Christine, Ory Magne, Dr Fabienne, Simonetta Moreau, Dr Marion, Arbus, Pr Christophe, Bonneville, Pr Fabrice, Albert Lotterie, Dr Jean, Sarrail, Marion, Scotto d’Apollonia, Charlotte, Chaynes, Pr Patrick, Caire, Pr François, Harroch, Estelle, Maltete, Pr David, Lefaucheur, Dr Romain, Fetter, Dr Damien, Magne, Dr Nicolas, Bioux, Mrs Sandrine, Loubeyre, Mrs Maud, Bliaux, Mrs Evangéline, Pouliquen, Mrs Dorothée, Derrey, Pr Stéphane, Vernon, Mrs Linda, Ziegler, Dr Frédéric, Anheim, Mathieu, Lagha-Boukbiza, Ouhaid, Tranchant, Christine, Gebus, Odile, Montaut, Solveig, Kremer, S., Longato, Nadine, Phillips, Clélie, Voirin, Jimmy, Santin, Marie des Neiges, Chaussemy, Dominique, Mengin, Dr Amaury, Giordana, Dr Caroline, Marsé, Dr Claire, Mondot, Lydiane, Giordana, Bruno, Kardous, Robin, Bailet, Bernadette, Joly, Héloise, Fontaine, Denys, Leplus, Dr Aurélie, Faustini, Amélie, Ferrier, Vanessa, Krystkowiak, Pr Pierre, Tir, Dr Mélissa, Jean-Marc Constans, Pr, Wannepain, Sandrine, Seling, Audrey, Lefranc, Dr Michel, Blin, Stéphanie, Schuler, Béatrice, Thobois, Pr Stephane, Danaila, Dr Teodor, Laurencin, Dr Chloe, Berthezene, Pr Yves, Ameli, Dr Roxana, Klinger, Helene, Polo, Dr Gustavo, Mertens, Patrick, Nunes, A., Metereau, Elise, Hopes, Dr Lucie, Frismand, Dr Solène, Schmitt, Dr Emmanuelle, Meyer, Mrs Mylène, Dillier, Mrs Céline, Colnat, Pr Sophie, Chatelain, Mrs Anne, Philippe Brandel, Dr Jean, Hubsch, Dr Cécile, Karsenti, Dr Patte, Lebouteux, Dr Marie, Ziegler, Dr Marc, Delmaire, Dr Christine, Savatowky, Dr Julien, Vrillac, Mrs Juliette, Nakache, Mrs Claire, D'Hardemare, Dr Vincent, Belamri, Mr Lhaouas, Mesnage, Dr Valérie, Bonnet, Dr Cécilia, Correa Lino, Dr Jarbas, Jr., Decrocq, Dr Camille, Boulin, Dr Anne, Barre, Mrs Inès, Manouvrier, Mrs Jordane, Gardel, Dr Bérénice, Jarraya, Pr Béchir, Ziz, Mrs Catherine, Prette, Mrs Lydie, Douzane, Mr Hassen, Gay, David, Bonicel, Robin, El Mountassir, Fouzia, Fischer, Clara, Mangin, Jean-François, Chupin, Marie, Cointepas, Yann, Accart, Bertrand, Gelé, Patrick, Fievet, Florine, Chabel, Matthieu, Derenaucourt, Virginie, Facon, Loïc, Njosse, Yanick Tchantchou, Deplanque, Dominique, Duhamel, Alain, Djemmane, Lynda, Duflot, Florence, Boussac, Mathilde, Arbus, Christophe, Colin, Olivier, Laurencin, Chloé, Eusebio, Alexandre, Corvol, Jean Christophe, Versace, Nathalie, Rascol, Olivier, Rousseau, Vanessa, Ory-Magne, Fabienne, Fabbri, Margherita, Rolland, Anne-Sophie, Jarraya, Béchir, Maltête, David, Drapier, Sophie, Marques, Ana-Raquel, Wirth, Thomas, Meyer, Mylène, Tir, Mélissa, Rouaud, Tiphaine, Devos, David, and Brefel-Courbon, Christine

Published
2022
Full Text
View/download PDF

25. Intrasubject subcortical quantitative referencing to boost MRI sensitivity to Parkinson's disease

Author

Khedher, Laila, Bonny, Jean-Marie, Marques, Ana, Durand, Elodie, Pereira, Bruno, Chupin, Marie, Vidal, Tiphaine, Chassain, Carine, Defebvre, Luc, Carriere, Nicolas, Fraix, Valerie, Moro, Elena, Thobois, Stéphane, Metereau, Elise, Mangone, Graziella, Vidailhet, Marie, Corvol, Jean-Christophe, Lehéricy, Stéphane, Menjot de Champfleur, Nicolas, Geny, Christian, Spampinato, Umberto, Meissner, Wassilios, Frismand, Solène, Schmitt, Emmanuelle, Doé de Maindreville, Anne, Portefaix, Christophe, Remy, Philippe, Fénelon, Gilles, Luc Houeto, Jean, Colin, Olivier, Rascol, Olivier, Peran, Patrice, and Durif, Franck

Published
2022
Full Text
View/download PDF

26. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

Author

Gerardin, Emilie, Chételat, Gaël, Chupin, Marie, Cuingnet, Rémi, Desgranges, Béatrice, Kim, Ho-Sung, Niethammer, Marc, Dubois, Bruno, Lehéricy, Stéphane, Garnero, Line, Eustache, Francis, and Colliot, Olivier

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Abstract

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age $\pm$ standard-deviation (SD) = 73 $\pm$ 6 years, mini-mental score (MMS) = 24.4 $\pm$ 2.8), 23 patients with amnestic MCI (10 males, 13 females, age $\pm$ SD = 74 $\pm$ 8 years, MMS = 27.3 $\pm$ 1.4) and 25 elderly healthy controls (13 males, 12 females, age $\pm$ SD = 64 $\pm$ 8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease., Comment: Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database

Published
2017
Full Text
View/download PDF

27. Basal Forebrain Volume, but Not Hippocampal Volume, Is a Predictor of Global Cognitive Decline in Patients With Alzheimer's Disease Treated With Cholinesterase Inhibitors

Author

Teipel, Stefan J, Cavedo, Enrica, Hampel, Harald, Grothe, Michel J, Initiative, Alzheimer's Disease Neuroimaging, Initiative, Alzheimer Precision Medicine, Aguilar, Lisi Flores, Babiloni, Claudio, Baldacci, Filippo, Benda, Norbert, Black, Keith L, Bokde, Arun LW, Bonuccelli, Ubaldo, Broich, Karl, Bun, René S, Cacciola, Francesco, Castrillo, Juan, Ceravolo, Roberto, chiesa, Patrizia A, Colliot, Olivier, Coman, Cristina-Maria, Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L, Depypere, Herman, Dubois, Bruno, Duggento, Andrea, Durrleman, Stanley, Escott-price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A, Garaci, Francesco, Genthon, Remy, George, Nathalie, Giorgi, Filippo S, Graziani, Manuela, Haberkamp, Marion, Habert, Marie-Odile, Herholz, Karl, Karran, Eric, Seung, H KIM, Koronyo, Yosef, Koronyo-Hamaoui, Maya, Lamari, Foudil, Langevin, Todd, Lehéricy, Stéphane, Lista, Simone, Lorenceau, Jean, Mapstone, Mark, Neri, Christian, Nisticò, Robert, Nyasse-Messene, Francis, O'Bryant, Sid E, Perry, George, Ritchie, Craig, Rojkova, Katrine, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S, Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R, Vergallo, Andrea, Villain, Nicolas, Welikovitch, Lindsay A, Woodcock, Janet, and Younesi, Erfan

Subjects
Biological Psychology, Psychology, Neurosciences, Brain Disorders, Dementia, Neurodegenerative, Clinical Research, Acquired Cognitive Impairment, Behavioral and Social Science, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Neurological, Alzheimer's Disease Neuroimaging Initiative, Alzheimer Precision Medicine Initiative, MRI, basal forebrain, cholinergic treatment, executive function, hippocampus, memory, prediction, Clinical Sciences, Clinical sciences, Biological psychology
Abstract

Background: Predicting the progression of cognitive decline in Alzheimer's disease (AD) is important for treatment selection and patient counseling. Structural MRI markers such as hippocampus or basal forebrain volumes might represent useful instruments for the prediction of cognitive decline. The primary objective was to determine the predictive value of hippocampus and basal forebrain volumes for global and domain specific cognitive decline in AD dementia during cholinergic treatment. Methods: We used MRI and cognitive data from 124 patients with the clinical diagnosis of AD dementia, derived from the ADNI-1 cohort, who were on standard of care cholinesterase inhibitor treatment during a follow-up period between 0.4 and 3.1 years. We used linear mixed effects models with cognitive function as outcome to assess the main effects as well as two-way interactions between baseline volumes and time controlling for age, sex, and total intracranial volume. This model accounts for individual variation in follow-up times. Results: Basal forebrain volume, but not hippocampus volume, was a significant predictor of rates of global cognitive decline. Larger volumes were associated with smaller rates of cognitive decline. Left hippocampus volume had a modest association with rates of episodic memory decline. Baseline performance in global cognition and memory was significantly associated with hippocampus and basal forebrain volumes; in addition, basal forebrain volume was associated with baseline performance in executive function. Conclusions: Our findings indicate that in AD dementia patients, basal forebrain volume may be a useful marker to predict subsequent cognitive decline during cholinergic treatment.

Published
2018

28. Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty

Author

Chougar, Lydia, primary, Faucher, Alice, additional, Faouzi, Johann, additional, Lejeune, François‐Xavier, additional, Gama Lobo, Gonçalo, additional, Jovanovic, Carna, additional, Cormier, Florence, additional, Dupont, Gwendoline, additional, Vidailhet, Marie, additional, Corvol, Jean‐Christophe, additional, Colliot, Olivier, additional, Lehéricy, Stéphane, additional, Grabli, David, additional, and Degos, Bertrand, additional

Published
2024
Full Text
View/download PDF

29. Plasma p-tau181 as an outcome and predictor of multidomain intervention effects: a secondary analysis of a randomised, controlled, dementia prevention trial

Author

Coley, Nicola, primary, Zetterberg, Henrik, additional, Cantet, Christelle, additional, Guyonnet, Sophie, additional, Ashton, Nicholas J, additional, Vellas, Bruno, additional, Blennow, Kaj, additional, Andrieu, Sandrine, additional, Carrié, Isabelle, additional, Brigitte, Lauréane, additional, Faisant, Catherine, additional, Lala, Françoise, additional, Delrieu, Julien, additional, Villars, Hélène, additional, Combrouze, Emeline, additional, Badufle, Carole, additional, Zueras, Audrey, additional, Morin, Christophe, additional, Abellan Van Kan, Gabor, additional, Dupuy, Charlotte, additional, Rolland, Yves, additional, Caillaud, Céline, additional, Ousset, Pierre-Jean, additional, Willis, Sherry, additional, Belleville, Sylvie, additional, Gilbert, Brigitte, additional, Fontaine, Francine, additional, Dartigues, Jean-François, additional, Marcet, Isabelle, additional, Delva, Fleur, additional, Foubert, Alexandra, additional, Cerda, Sandrine, additional, Cuffi, Marie-Noëlle, additional, Costes, Corinne, additional, Rouaud, Olivier, additional, Manckoundia, Patrick, additional, Quipourt, Valérie, additional, Marilier, Sophie, additional, Franon, Evelyne, additional, Bories, Lawrence, additional, Pader, Marie-Laure, additional, Basset, Marie-France, additional, Lapoujade, Bruno, additional, Faure, Valérie, additional, Li Yung Tong, Michael, additional, Malick-Loiseau, Christine, additional, Cazaban-Campistron, Evelyne, additional, Desclaux, Françoise, additional, Blatge, Colette, additional, Dantoine, Thierry, additional, Laubarie-Mouret, Cécile, additional, Saulnier, Isabelle, additional, Clément, Jean-Pierre, additional, Picat, Marie-Agnès, additional, Bernard-Bourzeix, Laurence, additional, Willebois, Stéphanie, additional, Désormais, Iléana, additional, Cardinaud, Noëlle, additional, Bonnefoy, Marc, additional, Livet, Pierre, additional, Rebaudet, Pascale, additional, Gédéon, Claire, additional, Burdet, Catherine, additional, Terracol, Flavien, additional, Pesce, Alain, additional, Roth, Stéphanie, additional, Chaillou, Sylvie, additional, Louchart, Sandrine, additional, Sudres, Kristel, additional, Lebrun, Nicolas, additional, Barro-Belaygues, Nadège, additional, Touchon, Jacques, additional, Bennys, Karim, additional, Gabelle, Audrey, additional, Romano, Aurélia, additional, Touati, Lynda, additional, Marelli, Cécilia, additional, Pays, Cécile, additional, Robert, Philippe, additional, Le Duff, Franck, additional, Gervais, Claire, additional, Gonfrier, Sébastien, additional, Gasnier, Yannick, additional, Bordes, Serge, additional, Begorre, Danièle, additional, Carpuat, Christian, additional, Khales, Khaled, additional, Lefebvre, Jean-François, additional, Misbah El Idrissi, Samira, additional, Skolil, Pierre, additional, Salles, Jean-Pierre, additional, Dufouil, Carole, additional, Lehéricy, Stéphane, additional, Chupin, Marie, additional, Mangin, Jean-François, additional, Bouhayia, Ali, additional, Allard, Michèle, additional, Ricolfi, Frédéric, additional, Dubois, Dominique, additional, Bonceour Martel, Marie Paule, additional, Cotton, François, additional, Bonafé, Alain, additional, Chanalet, Stéphane, additional, Hugon, Françoise, additional, Bonneville, Fabrice, additional, Cognard, Christophe, additional, Chollet, François, additional, Payoux, Pierre, additional, Voisin, Thierry, additional, Peiffer, Sophie, additional, Hitzel, Anne, additional, Zanca, Michel, additional, Monteil, Jacques, additional, Darcourt, Jacques, additional, Molinier, Laurent, additional, Derumeaux, Hélène, additional, Costa, Nadège, additional, Perret, Bertrand, additional, Vinel, Claire, additional, Caspar-Bauguil, Sylvie, additional, Olivier-Abbal, Pascale, additional, and Coley, Nicola, additional

Published
2024
Full Text
View/download PDF

30. Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer

Author

Bolan, Patrick J., Branzoli, Francesca, Di Stefano, Anna Luisa, Nichelli, Lucia, Valabregue, Romain, Saunders, Sara L., Akçakaya, Mehmet, Sanson, Marc, Lehéricy, Stéphane, Marjańska, Małgorzata, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor

Published
2020
Full Text
View/download PDF

31. Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices

Author

Marrakchi-Kacem, Linda, Vignaud, Alexandre, Sein, Julien, Germain, Johanne, Henry, Thomas R, Poupon, Cyril, Hertz-Pannier, Lucie, Lehéricy, Stéphane, Colliot, Olivier, Van de Moortele, Pierre-François, and Chupin, Marie

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

OBJECTIVE:Motion-robust multi-slab imaging of hippocampal inner structure in vivo at 7T.MATERIALS AND METHODS:Motion is a crucial issue for ultra-high resolution imaging, such as can be achieved with 7T MRI. An acquisition protocol was designed for imaging hippocampal inner structure at 7T. It relies on a compromise between anatomical details visibility and robustness to motion. In order to reduce acquisition time and motion artifacts, the full slab covering the hippocampus was split into separate slabs with lower acquisition time. A robust registration approach was implemented to combine the acquired slabs within a final 3D-consistent high-resolution slab covering the whole hippocampus. Evaluation was performed on 50 subjects overall, made of three groups of subjects acquired using three acquisition settings; it focused on three issues: visibility of hippocampal inner structure, robustness to motion artifacts and registration procedure performance.RESULTS:Overall, T2-weighted acquisitions with interleaved slabs proved robust. Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure.CONCLUSION:Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus.

Published
2016

33. Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

Author

Hill, Ioana, Palombo, Marco, Santin, Mathieu, Branzoli, Francesca, Philippe, Anne-Charlotte, Wassermann, Demian, Aigrot, Marie-Stephane, Stankoff, Bruno, Baron-Van Evercooren, Anne, Felfli, Mehdi, Langui, Dominique, Zhang, Hui, Lehericy, Stephane, Petiet, Alexandra, Alexander, Daniel C., Ciccarelli, Olga, and Drobnjak, Ivana

Published
2021
Full Text
View/download PDF

34. Association of plasma YKL-40 with brain amyloid-β levels, memory performance, and sex in subjective memory complainers

Author

Bakardjian, Hovagim, Benali, Habib, Bertin, Hugo, Bonheur, Joel, Boukadida, Laurie, Boukerrou, Nadia, Cavedo, Enrica, Chiesa, Patrizia, Colliot, Olivier, Dubois, Bruno, Dubois, Marion, Epelbaum, Stéphane, Gagliardi, Geoffroy, Genthon, Remy, Habert, Marie-Odile, Hampel, Harald, Houot, Marion, Kas, Aurélie, Lamari, Foudil, Levy, Marcel, Lista, Simone, Metzinger, Christiane, Mochel, Fanny, Nyasse, Francis, Poisson, Catherine, Potier, Marie-Claude, Revillon, Marie, Santos, Antonio, Andrade, Katia Santos, Sole, Marine, Surtee, Mohmed, de Schotten, Michel Thiebaut, Vergallo, Andrea, Younsi, Nadjia, Afshar, Mohammad, Aguilar, Lisi Flores, Akman-Anderson, Leyla, Arenas, Joaquín, Ávila, Jesús, Babiloni, Claudio, Baldacci, Filippo, Batrla, Richard, Benda, Norbert, Black, Keith L., Bokde, Arun L.W., Bonuccelli, Ubaldo, Broich, Karl, Cacciola, Francesco, Caraci, Filippo, Caruso, Giuseppe, Castrillo†, Juan, Ceravolo, Roberto, Chiesa, Patrizia A., Corbo, Massimo, Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L., Depypere, Herman, Duggento, Andrea, Emanuele, Enzo, Escott-Price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A., Garaci, Francesco, Geerts, Hugo, Giacobini, Ezio, Giorgi, Filippo S., Goetzl, Edward J., Graziani, Manuela, Haberkamp, Marion, Hänisch, Britta, Herholz, Karl, Hernandez, Felix, Imbimbo, Bruno P., Kapogiannis, Dimitrios, Karran, Eric, Kiddle, Steven J., Kim, Seung H., Koronyo, Yosef, Koronyo-Hamaoui, Maya, Langevin, Todd, Lehéricy, Stéphane, Lemercier, Pablo, Llavero, Francisco, Lorenceau, Jean, Lucía, Alejandro, Mango, Dalila, Mapstone, Mark, Neri, Christian, Nisticò, Robert, O’bryant, Sid E., Palermo, Giovanni, Perry, George, Ritchie, Craig, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S., Sporns, Olaf, Toschi, Nicola, Valenzuela, Pedro L., Vellas, Bruno, Verdooner, Steven R., Villain, Nicolas, Giudici, Kelly Virecoulon, Watling, Mark, Welikovitch, Lindsay A., Woodcock, Janet, Younesi, Erfan, Zugaza, José L., Zetterberg, Henrik, and Blennow, Kaj

Published
2020
Full Text
View/download PDF

39. Brain Aβ load association and sexual dimorphism of plasma BACE1 concentrations in cognitively normal individuals at risk for AD

Author

Bakardjian, H., Benali, H., Bertin, H., Bonheur, J., Boukadida, L., Boukerrou, N., Cavedo, E., Chiesa, P., Colliot, O., Dubois, B., Dubois, M., Epelbaum, S., Gagliardi, G., Genthon, R., Habert, M.O., Hampel, H., Houot, M., Kas, A., Lamari, F., Levy, M., Lista, S., Metzinger, C., Mochel, F., Nyasse, F., Poisson, C., Potier, M.C., Revillon, M., Santos, A., Andrade, K.S., Sole, M., Surtee, M., Thiebaud de Schotten, M., Vergallo, A., Younsi, N., Afshar, Mohammad, Flores Aguilar, Lisi, Akman-Anderson, Leyla, Arenas, Joaquín, Avila, Jesus, Babiloni, Claudio, Baldacci, Filippo, Batrla, Richard, Benda, Norbert, Black, Keith L., Bokde, Arun L.W., Bonuccelli, Ubaldo, Broich, Karl, Cacciola, Francesco, Caraci, Filippo, Castrillo, Juan, Cavedo, Enrica, Ceravolo, Roberto, Chiesa, Patrizia A., Corvol, Jean-Christophe, Claudio Cuello, Augusto, Cummings, Jeffrey L., Depypere, Herman, Dubois, Bruno, Duggento, Andrea, Emanuele, Enzo, Escott-Price, Valentina, Federoff, Howard, Teresa Ferretti, Maria, Fiandaca, Massimo, Frank, Richard A., Garaci, Francesco, Geerts, Hugo, Giorgi, Filippo S., Goetzl, Edward J., Graziani, Manuela, Haberkamp, Marion, Marie-Odile, Habert, Hampel, Harald, Herholz, Karl, Hernandez, Felix, Kapogiannis, Dimitrios, Karran, Eric, Kiddle, Steven J., Kim, Seung H., Koronyo, Yosef, Koronyo-Hamaoui, Maya, Langevin, Todd, Lehéricy, Stéphane, Lucía, Alejandro, Lista, Simone, Lorenceau, Jean, Mango, Dalila, Mapstone, Mark, Neri, Christian, Nisticò, Robert, O'Bryant, Sid E., Palermo, Giovanni, Perry, George, Ritchie, Craig, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S., Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R., Vergallo, Andrea, Villain, Nicolas, Welikovitch, Lindsay A., Woodcock, Janet, Younesi, Erfan, Houot, Marion, Lemercier, Pablo, Vanmechelen, Eugeen, De Vos, Ann, Habert, Marie-Odile, and Potier, Marie-Claude

Published
2019
Full Text
View/download PDF

40. Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτ Samples Through a Relaxed Probabilistic Model

Author

Filipiak, Patryk, Fick, Rutger, Petiet, Alexandra, Santin, Mathieu, Philippe, Anne-Charlotte, Lehericy, Stephane, Deriche, Rachid, Wassermann, Demian, Hege, Hans-Christian, Series Editor, Hoffman, David, Series Editor, Johnson, Christopher R., Series Editor, Polthier, Konrad, Series Editor, Rumpf, Martin, Series Editor, Kaden, Enrico, editor, Grussu, Francesco, editor, Ning, Lipeng, editor, Tax, Chantal M. W., editor, and Veraart, Jelle, editor

Published
2018
Full Text
View/download PDF

41. Differential default mode network trajectories in asymptomatic individuals at risk for Alzheimer's disease

Author

Bakardjian, Hovagim, Benali, Habib, Bertin, Hugo, Bonheur, Joel, Boukadida, Laurie, Boukerrou, Nadia, Cavedo, Enrica, Chiesa, Patrizia, Colliot, Olivier, Dubois, Bruno, Dubois, Marion, Epelbaum, Stéphane, Gagliardi, Geoffroy, Genthon, Remy, Habert, Marie-Odile, Hampel, Harald, Houot, Marion, Kas, Aurélie, Lamari, Foudil, Levy, Marcel, Lista, Simone, Metzinger, Christiane, Mochel, Fanny, Nyasse, Francis, Poisson, Catherine, Potier, Marie-Claude, Revillon, Marie, Santos, Antonio, Andrade, Katia Santos, Sole, Marine, Surtee, Mohmed, de Schotten, Michel Thiebaud, Vergallo, Andrea, Younsi, Nadjia, Afshar, Mohammad, Aguilar, Lisi Flores, Akman-Anderson, Leyla, Arenas, Joaquín, Avila, Jesus, Babiloni, Claudio, Baldacci, Filippo, Batrla, Richard, Benda, Norbert, Black, Keith L., Bokde, Arun L.W., Bonuccelli, Ubaldo, Broich, Karl, Cacciola, Francesco, Caraci, Filippo, Castrillo, Juan, Ceravolo, Roberto, Chiesa, Patrizia A., Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L., Depypere, Herman, Duggento, Andrea, Emanuele, Enzo, Escott-Price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A., Garaci, Francesco, Geerts, Hugo, Giorgi, Filippo S., Goetzl, Edward J., Graziani, Manuela, Haberkamp, Marion, Herholz, Karl, Hernandez, Felix, Kapogiannis, Dimitrios, Karran, Eric, Kiddle, Steven J., Kim, Seung H., Koronyo, Yosef, Koronyo-Hamaoui, Maya, Langevin, Todd, Lehéricy, Stéphane, Lucía, Alejandro, Lorenceau, Jean, Mango, Dalila, Mapstone, Mark, Neri, Christian, Nisticó, Robert, O’Bryant, Sid E., Palermo, Giovanni, Perry, George, Ritchie, Craig, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S., Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R., Villain, Nicolas, Welikovitch, Lindsay A., Woodcock, Janet, Younesi, Erfan, and Thiebaut de Schotten, Michel

Published
2019
Full Text
View/download PDF

42. Adherence to multidomain interventions for dementia prevention: Data from the FINGER and MAPT trials

Author

Richard, Edo, van Gool, Pim, van Charante, Eric Moll, Beishuizen, Cathrien, Jongstra, Susan, van Middelaar, Tessa, van Wanrooij, Lennard, Hoevenaar-Blom, Marieke, Soininen, Hilkka, Ngandu, Tiia, Barbera, Mariagnese, Kivipelto, Miia, Mangiasche, Francesca, Andrieu, Sandrine, Coley, Nicola, Guillemont, Juliette, Meiller, Yannick, van de Groep, Bram, Brayne, Carol, Solomon, Alina, Laatikainen, Tiina, Strandberg, Timo, Tuomilehto, Jaakko, Antikainen, Riitta, Lindström, Jaana, Lehtisalo, Jenni, Havulinna, Satu, Rauramaa, Rainer, Hänninen, Tuomo, Bäckman, Lars, Stigsdotter-Neely, Anna, Jula, Antti, Peltonen, Markku, Levälahti, Esko, Grönholm, Marko, Hemiö, Katri, Vellas, Bruno, Guyonnet, Sophie, Carrié, Isabelle, Brigitte, Lauréane, Faisant, Catherine, Lala, Françoise, Delrieu, Julien, Villars, Hélène, Combrouze, Emeline, Badufle, Carole, Zueras, Audrey, Cantet, Christelle, Morin, Christophe, Van Kan, Gabor Abellan, Dupuy, Charlotte, Rolland, Yves, Caillaud, Céline, Ousset, Pierre-Jean, Fougère, Bertrand, Willis, Sherry, Belleville, Sylvie, Gilbert, Brigitte, Fontaine, Francine, Dartigues, Jean-François, Marcet, Isabelle, Delva, Fleur, Foubert, Alexandra, Cerda, Sandrine, Marie-Noëlle-Cuffi, Costes, Corinne, Rouaud, Olivier, Manckoundia, Patrick, Quipourt, Valérie, Marilier, Sophie, Franon, Evelyne, Bories, Lawrence, Pader, Marie-Laure, Basset, Marie-France, Lapoujade, Bruno, Faure, Valérie, Yung Tong, Michael Li, Malick-Loiseau, Christine, Cazaban-Campistron, Evelyne, Desclaux, Françoise, Blatge, Colette, Dantoine, Thierry, Laubarie-Mouret, Cécile, Saulnier, Isabelle, Clément, Jean-Pierre, Picat, Marie-Agnès, Bernard-Bourzeix, Laurence, Willebois, Stéphanie, Désormais, Iléana, Cardinaud, Noëlle, Bonnefoy, Marc, Livet, Pierre, Rebaudet, Pascale, Gédéon, Claire, Burdet, Catherine, Terracol, Flavien, Pesce, Alain, Roth, Stéphanie, Chaillou, Sylvie, Louchart, Sandrine, Sudres, Kristelle, Lebrun, Nicolas, Barro-Belaygues, Nadège, Touchon, Jacques, Bennys, Karim, Gabelle, Audrey, Romano, Aurélia, Touati, Lynda, Marelli, Cécilia, Pays, Cécile, Robert, Philippe, Le Duff, Franck, Gervais, Claire, Gonfrier, Sébastien, Gasnier, Yannick, Bordes, Serge, Begorre, Danièle, Carpuat, Christian, Khales, Khaled, Lefebvre, Jean-François, El Idrissi, Samira Misbah, Skolil, Pierre, Salles, Jean-Pierre, Dufouil, Carole, Lehéricy, Stéphane, Chupin, Marie, Mangin, Jean-François, Bouhayia, Ali, Allard, Michèle, Ricolfi, Frédéric, Dubois, Dominique, Bonceour Martel, Marie Paule, Cotton, François, Bonafé, Alain, Chanalet, Stéphane, Hugon, Françoise, Bonneville, Fabrice, Cognard, Christophe, Chollet, François, Payoux, Pierre, Voisin, Thierry, Peiffer, Sophie, Hitzel, Anne, Zanca, Michel, Monteil, Jacques, Darcourt, Jacques, Molinier, Laurent, Derumeaux, Hélène, Costa, Nadège, Vincent, Christian, Perret, Bertrand, Vinel, Claire, and Olivier-Abbal, Pascale

Published
2019
Full Text
View/download PDF

43. Plasma amyloid β 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer's disease

Author

Bakardjian, H., Benali, H., Bertin, H., Bonheur, J., Boukadida, L., Boukerrou, N., Cavedo, E., Chiesa, P., Colliot, O., Dubois, B., Dubois, M., Epelbaum, S., Gagliardi, G., Genthon, R., Habert, M.O., Hampel, H., Houot, M., Kas, A., Lamari, F., Levy, M., Lista, S., Metzinger, C., Mochel, F., Nyasse, F., Poisson, C., Potier, M.C., Revillon, M., Santos, A., Andrade, K.S., Sole, M., Surtee, M., de Schotten M, Thiebaud, Vergallo, A., Younsi, N., Aguilar, Lisi Flores, Babiloni, Claudio, Baldacci, Filippo, Benda, Norbert, Black, Keith L., Bokde, Arun L.W., Bonuccelli, Ubaldo, Broich, Karl, Cacciola, Francesco, Castrillo, Juan, Cavedo, Enrica, Ceravolo, Roberto, Chiesa, Patrizia A., Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L., Depypere, Herman, Dubois, Bruno, Duggento, Andrea, Escott-Price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A., Garaci, Francesco, Geerts, Hugo, Giorgi, Filippo S., Graziani, Manuela, Haberkamp, Marion, Habert, Marie-Odile, Hampel, Harald, Herholz, Karl, Karran, Eric, Kim, Seung H., Koronyo, Yosef, Koronyo-Hamaoui, Maya, Lamari, Foudil, Langevin, Todd, Lehéricy, Stéphane, Lista, Simone, Lorenceau, Jean, Mango, Dalila, Mapstone, Mark, Neri, Christian, Nisticò, Robert, O'Bryant, Sid E., Perry, George, Ritchie, Craig, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S., Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R., Vergallo, Andrea, Villain, Nicolas, Welikovitch, Lindsay A., Woodcock, Janet, Younesi, Erfan, Mégret, Lucile, Zetterberg, Henrik, Blennow, Kaj, Vanmechelen, Eugeen, De Vos, Ann, and Potier, Marie-Claude

Published
2019
Full Text
View/download PDF

48. Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease.

Author

Di Folco, Cécile, Couronné, Raphaël, Arnulf, Isabelle, Mangone, Graziella, Leu‐Semenescu, Smaranda, Dodet, Pauline, Vidailhet, Marie, Corvol, Jean‐Christophe, Lehéricy, Stéphane, and Durrleman, Stanley

Abstract

Background: Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers. Objective: To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD). Methods: Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow‐up duration = 5.1; 95% confidence interval, 1.1–8.1] years). Disease course map was estimated by using eight clinical markers (motor and non‐motor symptoms) and four imaging markers (dopaminergic denervation). Results: PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep—all before diagnosis—and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non‐motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non‐motor symptoms and later changes in motor symptoms. Conclusions: Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results