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178 results on '"Colliot, O."'

1. 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
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2. 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
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3. Association of cerebrospinal fluid α-synuclein with total and phospho-tau181 protein concentrations and brain amyloid load in cognitively normal subjective memory complainers stratified by Alzheimer's disease biomarkers

Author

Audrain, C., Auffret, A., Bakardjian, H., Baldacci, F., Batrancourt, B., Benakki, I., Benali, H., Bertin, H., Bertrand, A., Boukadida, L., Cacciamani, F., Causse, V., Cavedo, E., Cherif Touil, S., Chiesa, P.A., Colliot, O., Dalla Barba, G., Depaulis, M., Dos Santos, A., Dubois, B., Dubois, M., Epelbaum, S., Fontaine, B., Francisque, H., Gagliardi, G., Genin, A., Genthon, R., Glasman, P., Gombert, F., Habert, M.O., Hampel, H., Hewa, H., Houot, M., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Le Roy, F., Lehericy, S., Letondor, C., Levy, M., Lista, S., Lowrey, M., Ly, J., Makiese, O., Masetti, I., Mendes, A., Metzinger, C., Michon, A., Mochel, F., Nait Arab, R., Nyasse, F., Perrin, C., Poirier, F., Poisson, C., Potier, M.C., Ratovohery, S., Revillon, M., Rojkova, K., Santos-Andrade, K., Schindler, R., Servera, M.C., Seux, L., Simon, V., Skovronsky, D., Thiebaut, M., Uspenskaya, O., Vlaincu, M., Aguilar, L.F., Babiloni, C., Benda, N., Black, K.L., Bokde, A.L.W., Bonuccelli, U., Broich, K., Bun, R.S., Cacciola, F., Castrillo, J., Ceravolo, R., Coman, C.M., Corvol, J.C., Cuello, A.C., Cummings, J.L., Depypere, H., Duggento, A., Durrleman, S., Escott-Price, V., Federoff, H., Ferretti, M.T., Fiandaca, M., Frank, R.A., Garaci, F., George, N., Giorgi, F.S., Graziani, M., Haberkamp, M., Herholz, K., Karran, E., Kim, S.H., Koronyo, Y., Koronyo-Hamaoui, M., Lamari, F., Langevin, T., Lehéricy, S., Lorenceau, J., Mapstone, M., Neri, C., Nisticò, R., Nyasse-Messene, F., O'Bryant, S.E., Perry, G., Ritchie, C., Rossi, S., Santarnecchi, E., Schneider, L.S., Sporns, O., Toschi, N., Verdooner, S.R., Vergallo, A., Villain, N., Welikovitch, L., Woodcock, J., Younesi, E., Vergallo, Andrea, Bun, René-Sosata, Toschi, Nicola, Baldacci, Filippo, Zetterberg, Henrik, Blennow, Kaj, Cavedo, Enrica, Lamari, Foudil, Habert, Marie-Odile, Dubois, Bruno, Floris, Roberto, Garaci, Francesco, Lista, Simone, and Hampel, Harald

Published
2018
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4. Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers: An INSIGHT-preAD study

Author

Audrain, C., Auffret, A., Bakardjian, H., Baldacci, F., Batrancourt, B., Benakki, I., Benali, H., Bertin, H., Bertrand, A., Boukadida, L., Cacciamani, F., Causse, V., Cavedo, E., Cherif Touil, S., Chiesa, P.A., Colliot, O., Dalla Barba, G., Depaulis, M., Dos Santos, A., Dubois, B., Dubois, M., Epelbaum, S., Fontaine, B., Francisque, H., Gagliardi, G., Genin, A., Genthon, R., Glasman, P., Gombert, F., Habert, M.O., Hampel, H., Hewa, H., Houot, M., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Le Roy, F., Lehericy, S., Letondor, C., Levy, M., Lista, S., Lowrey, M., Ly, J., Makiese, O., Masetti, I., Mendes, A., Metzinger, C., Michon, A., Mochel, F., Nait Arab, R., Nyasse, F., Perrin, C., Poirier, F., Poisson, C., Potier, M.C., Ratovohery, S., Revillon, M., Rojkova, K., Santos-Andrade, K., Schindler, R., Servera, M.C., Seux, L., Simon, V., Skovronsky, D., Thiebaut, M., Uspenskaya, O., Vlaincu, M., Teipel, Stefan J., Cavedo, Enrica, Lista, Simone, Habert, Marie-Odile, Potier, Marie-Claude, Grothe, Michel J., Epelbaum, Stephane, Sambati, Luisa, Gagliardi, Geoffroy, Toschi, Nicola, Greicius, Michael D., Dubois, Bruno, and Hampel, Harald

Published
2018
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5. Evaluation of amyloid status in a cohort of elderly individuals with memory complaints: validation of the method of quantification and determination of positivity thresholds

Author

Habert, Marie-Odile, Bertin, Hugo, Labit, Mickael, Diallo, Mamadou, Marie, Sullivan, Martineau, Kelly, Kas, Aurélie, Causse-Lemercier, Valérie, Bakardjian, Hovagim, Epelbaum, Stéphane, Chételat, Gael, Houot, Marion, Hampel, Harald, Dubois, Bruno, Mangin, Jean-François, Audrain, C., Bakardjian, H., Benali, H., Bertin, H., Boukadida, L., Cacciamani, F., Causse-Lemercier, V., Cavedo, E., Chiesa, P., Colliot, O., Dos Santos, A., Dubois, B., Durrleman, S., Epelbaum, S., Gagliardi, G., Genthon, R., Habert, M.-O., Hampel, H., Jungalee, N., Kas, A., Lehericy, S., Lamari, F., Letondor, C., Levy, M., Lista, S., Mochel, F., Nyasse, F., Poisson, C., Potier, M. C., Revillon, M., Rojkova, K., Roy, P., Santos-Andrade, K., Santos, A., Simon, V., Sole, M., Tandetnik, C., Thiebaud De Schotten, M., and INSIGHT-AD study group

Published
2017
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7. Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease

Author

Penner, G, Lecocq, S, Chopin, A, Vedoya, X, Lista, S, Vergallo, A, Cavedo, E, Lejeune, F, Dubois, B, Hampel, H, Bakardjian, H, Benali, H, Bertin, H, Bonheur, J, Boukadida, L, Boukerrou, N, Chiesa, Pa, Colliot, O, Dubois, M, Epelbaum, S, Gagliardi, G, Genthon, R, Habert, M, Houot, M, Kas, A, Lamari, F, Levy, M, Metzinger, C, Mochel, F, Nyasse, F, Poisson, C, Potier, M, Revillon, M, Santos, A, Andrade, Ks, Sole, M, Surtee, M, de Schotten, Mt, Younsi, N, Afshar, M, Aguilar, Lf, Akman-Anderson, L, Aremas, J, Avila, J, Babiloni, C, Baldacci, F, Batrla, R, Benda, N, Black, Kl, Bokde, Alw, Bonuccelli, U, Broich, K, Cacciola, F, Caraci, F, Caruso, G, Castrillo, J, Ceravolo, R, Corbo, M, Corvol, J, Cuello, Ac, Cummings, Jl, Depypere, H, Duggento, A, Emanuele, E, Escott-Price, V, Federoff, H, Ferretti, Mt, Fiandaca, M, Frank, Ra, Garaci, F, Geerts, H, Giacobini, E, Giorgi, Fs, Goetzl, Ej, Graziani, M, Haberkamp, M, Hanisch, B, Herholz, K, Hernandez, F, Imbimbo, Bp, Kapogiannis, D, Karran, E, Kiddle, Sj, Kim, Sh, Koronyo, Y, Koronyo-Hamaoui, M, Langevin, T, Lehericy, S, Lemercier, P, Llavero, F, Lorenceau, J, Lucia, A, Mango, D, Mapstone, M, Neri, C, Nistico, R, O'Bryant, Se, Palermo, G, Perry, G, Ritchie, C, Rossi, S, Saidi, A, Santarnecchi, E, Schneider, Ls, Sporns, O, Toschi, N, Valenzuela, Pl, Vellas, B, Verdooner, Sr, Villain, N, Giudici, Kv, Watling, M, Welikovitch, La, Woodcock, J, Younesi, E, Zugaza, Jl, Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Gasset, Maria

Subjects
Male, Aging, Amyloid β, MESH: SELEX Aptamer Technique, [SDV]Life Sciences [q-bio], Oligonucleotides, Artificial Gene Amplification and Extension, Disease, Neurodegenerative, Alzheimer's Disease, Pathology and Laboratory Medicine, Biochemistry, Polymerase Chain Reaction, Diagnostic Radiology, Negative selection, Medical Conditions, Mathematical and Statistical Techniques, 0302 clinical medicine, MESH: Aged, 80 and over, MESH: Early Diagnosis, 80 and over, Medicine and Health Sciences, Biomarker discovery, Tomography, Aged, 80 and over, MESH: Aged, screening and diagnosis, 0303 health sciences, Multidisciplinary, Nucleotides, Mathematical Models, Radiology and Imaging, SELEX Aptamer Technique, Settore MED/37 - Neuroradiologia, Neurodegenerative Diseases, MESH: Case-Control Studies, MESH: Amyloid beta-Peptides, Detection, Neurology, Neurological, Medicine, Biomedical Imaging, Female, Biotechnology, 4.2 Evaluation of markers and technologies, Research Article, Amyloid, General Science & Technology, Imaging Techniques, Science, Aptamer, Neuroimaging, and over, Computational biology, Biology, Research and Analysis Methods, 03 medical and health sciences, Clinical Research, Diagnostic Medicine, Alzheimer Disease, Mental Health and Psychiatry, Acquired Cognitive Impairment, Humans, Risk factor, Molecular Biology Techniques, Molecular Biology, Aged, 030304 developmental biology, Amyloid beta-Peptides, MESH: Humans, Prevention, Neurosciences, Alzheimer Precision Medicine Initiative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biology and Life Sciences, Omics, MESH: Male, Brain Disorders, 4.1 Discovery and preclinical testing of markers and technologies, Early Diagnosis, Case-Control Studies, MESH: Biomarkers, Dementia, INSIGHT-preAD study group, MESH: Female, Biomarkers, Positron Emission Tomography, 030217 neurology & neurosurgery, MESH: Alzheimer Disease, Neuroscience
Abstract

International audience; The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of individuals exhibiting a pathology and a group of healthy individuals. To achieve this, we focused on developing a predictive tool based on plasma for the amount of brain amyloid-β deposition as observed in PET scans. The accumulation of brain amyloid-β (Aβ) plaques is a key risk factor for the development of Alzheimer's disease. A contrast was established between cognitively normal individuals above the age of 70 that differed for the amount of brain amyloid-β observed in PET scans (INSIGHT study group). Positive selection was performed against a pool of plasma from individuals with high brain amyloid and negative selection against a pool of plasma from individuals with low brain amyloid This enriched, selected library was then applied to plasma samples from 11 individuals with high levels of brain amyloid and 11 individuals with low levels of brain Aβ accumulation. Each of these individually selected libraries was then characterized by next generation sequencing, and the relative frequency of 10,000 aptamer sequences that were observed in each selection was screened for ability to explain variation in brain amyloid using sparse partial least squares discriminant analysis. From this analysis a subset of 44 aptamers was defined, and the individual aptamers were synthesized. This subset was applied to plasma samples from 70 cognitively normal individuals all above the age of 70 that differed for brain amyloid deposition. 54 individuals were used as a training set, and 15 as a test set. Three of the 15 individuals in the test set were mis-classified resulting in an overall accuracy of 80% with 86% sensitivity and 75% specificity. The aptamers included in the subset serve directly as biomarkers, thus we have named them Aptamarkers. There are two potential applications of these results: extending the predictive capacity of these aptamers across a broader range of individuals, and/or using the individual aptamers to identify targets through covariance analysis and reverse omics approaches. We are currently expanding applications of the Aptamarker platform to other diseases and target matrices.

Published
2021
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8. Differential default mode network trajectories in asymptomatic individuals at risk for Alzheimer's disease

Author

Chiesa P. A., Cavedo E., Vergallo A., Lista S., Potier M. -C., Habert M. -O., Dubois B., Thiebaut de Schotten M., Hampel H., Audrain C., Auffret A., Bakardjian H., Baldacci F., Batrancourt B., Benakki I., Benali H., Bertin H., Bertrand A., Boukadida L., Cacciamani F., Causse V., Cherif Touil S., Colliot O., Dalla Barba G., Depaulis M., Dos Santos A., Dubois M., Epelbaum S., Fontaine B., Francisque H., Gagliardi G., Genin A., Genthon R., Glasman P., Gombert F., Habert M. O., Hewa H., Houot M., Jungalee N., Kas A., Kilani M., La Corte V., Le Roy F., Lehericy S., Letondor C., Levy M., Lowrey M., Ly J., Makiese O., Masetti I., Mendes A., Metzinger C., Michon A., Mochel F., Nait Arab R., Nyasse F., Perrin C., Poirier F., Poisson C., Potier M. C., Ratovohery S., Revillon M., Rojkova K., Santos-Andrade K., Schindler R., Servera M. C., Seux L., Simon V., Skovronsky D., Uspenskaya O., Vlaincu M., Aguilar L. F., Babiloni C., Benda N., Black K. L., Bokde A. L. W., Bonuccelli U., Broich K., Cacciola F., Castrillo J., Ceravolo R., Corvol J. -C., Claudio Cuello A., Cummings J. L., Depypere H., Duggento A., Durrleman S., Escott-Price V., Federoff H., Teresa Ferretti M., Fiandaca M., Frank R. A., Garaci F., Geerts H., George N., Giorgi F. S., Graziani M., Haberkamp M., Herholz K., Karran E., Kim S. H., Koronyo Y., Koronyo-Hamaoui M., Lamari F., Langevin T., Lorenceau J., Mango D., Mapstone M., Neri C., Nistico R., O'Bryant S. E., Palermo G., Perry G., Ritchie C., Rossi S., Saidi A., Santarnecchi E., Schneider L. S., Sporns O., Toschi N., Verdooner S. R., Villain N., Welikovitch L. A., Woodcock J., Younesi E., 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), Service de Neuroradiologie [CHU Pitié-Salpêtrière], 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), Treat SVD, Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), sans affiliation, Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB), Service de neurologie 1 [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Service de médecine nucléaire [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre des Maladies Cognitives et Comportementales [Paris], Fraunhofer Center for Assistive Information and Communication Solutions [Porto] (Fraunhofer AICOS), Fraunhofer (Fraunhofer-Gesellschaft), Ariana Pharmaceuticals, McGill University = Université McGill [Montréal, Canada], Universidad Autonoma de Madrid (UAM), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], University of Pisa - Università di Pisa, Federal Institute of Drugs and Medical Devices [Bonn], Discipline of Psychiatry [Dublin], School of Medicine [Dublin], Trinity College Dublin-Trinity College Dublin, Universita degli Studi di Messina, University of Catania [Italy], University of Cambridge [UK] (CAM), Lou Ruvo Center for Brain Health [Las Vegas], Cleveland Clinic, Università degli Studi di Roma Tor Vergata [Roma], University of Pavia, Cardiff University, Universität Zürich [Zürich] = University of Zurich (UZH), University of California [Irvine] (UCI), University of California, Siemens Healthineers, Digital Services, Digital Technology and Innovation, In Silico Biosciences (ISB), Abdus Salam International Centre for Theoretical Physics [Trieste] (ICTP), University of California [San Francisco] (UCSF), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), University of Manchester [Manchester], National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Abbvie Inc. [North Chicago], Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, University of Britsh Columbia [Vancouver], Cedars-Sinai Medical Center, Functional Neuromodulation, CIBER de Enfermedades Raras (CIBERER), Institut de la Vision, Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), European Brain Research Institute [Rome, Italy] (EBRI), Adaptation Biologique et Vieillissement = Biological Adaptation and Ageing (B2A), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Aging and Alzheimer’s Disease Research [Fort Worth] (IAADR), University of North Texas Health Science Center [Fort Worth], University of Auckland [Auckland], University of Edinburgh, Università degli Studi di Siena = University of Siena (UNISI), Harvard Medical School [Boston] (HMS), Keck School of Medicine [Los Angeles], University of Southern California (USC), Indiana State University, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), NeuroVision Imaging, Fondation pour la Recherche sur Alzheimer, Center for Drug Evaluation and Research (CDER), European Society for Translational Medicine (EUSTM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service d'Explorations Fonctionnelles Neurologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de médecine nucléaire [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP), Fraunhofer AICOS [Porto], McGill University, Sapienza University [Rome], University of Zürich [Zürich] (UZH), Università degli Studi di Roma 'La Sapienza' [Rome], CHU Pitié-Salpêtrière [APHP], Service de neuro-radiologie [CHU Pitié-Salpêtrière], Università degli Studi di Siena (UNISI), Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Service de Médecine nucléaire [CHU Pitié-Salpétrière], Universidad Autónoma de Madrid (UAM), 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), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Sans affiliation, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Messina = University of Messina (UniMe), Università degli Studi di Pavia = University of Pavia (UNIPV), University of California [Irvine] (UC Irvine), University of California (UC), University of California [San Francisco] (UC San Francisco), University of British Columbia [Vancouver], Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Chiesa, P. A., Cavedo, E., Vergallo, A., Lista, S., Potier, M. -C., Habert, M. -O., Dubois, B., Thiebaut de Schotten, M., Hampel, H., Audrain, C., Auffret, A., Bakardjian, H., Baldacci, F., Batrancourt, B., Benakki, I., Benali, H., Bertin, H., Bertrand, A., Boukadida, L., Cacciamani, F., Causse, V., Cherif Touil, S., Colliot, O., Dalla Barba, G., Depaulis, M., Dos Santos, A., Dubois, M., Epelbaum, S., Fontaine, B., Francisque, H., Gagliardi, G., Genin, A., Genthon, R., Glasman, P., Gombert, F., Habert, M. O., Hewa, H., Houot, M., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Le Roy, F., Lehericy, S., Letondor, C., Levy, M., Lowrey, M., Ly, J., Makiese, O., Masetti, I., Mendes, A., Metzinger, C., Michon, A., Mochel, F., Nait Arab, R., Nyasse, F., Perrin, C., Poirier, F., Poisson, C., Potier, M. C., Ratovohery, S., Revillon, M., Rojkova, K., Santos-Andrade, K., Schindler, R., Servera, M. C., Seux, L., Simon, V., Skovronsky, D., Uspenskaya, O., Vlaincu, M., Aguilar, L. F., Babiloni, C., Benda, N., Black, K. L., Bokde, A. L. W., Bonuccelli, U., Broich, K., Cacciola, F., Castrillo, J., Ceravolo, R., Corvol, J. -C., Claudio Cuello, A., Cummings, J. L., Depypere, H., Duggento, A., Durrleman, S., Escott-Price, V., Federoff, H., Teresa Ferretti, M., Fiandaca, M., Frank, R. A., Garaci, F., Geerts, H., George, N., Giorgi, F. S., Graziani, M., Haberkamp, M., Herholz, K., Karran, E., Kim, S. H., Koronyo, Y., Koronyo-Hamaoui, M., Lamari, F., Langevin, T., Lorenceau, J., Mango, D., Mapstone, M., Neri, C., Nistico, R., O'Bryant, S. E., Palermo, G., Perry, G., Ritchie, C., Rossi, S., Saidi, A., Santarnecchi, E., Schneider, L. S., Sporns, O., Toschi, N., Verdooner, S. R., Villain, N., Welikovitch, L. A., Woodcock, J., Younesi, E., and 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)

Subjects
0301 basic medicine, Apolipoprotein E, Epidemiology, Brain activity and meditation, Precuneus, Disease, Neuropsychological Tests, Hippocampus, Cohort Studies, [SCCO]Cognitive science, 0302 clinical medicine, Medicine, Longitudinal Studies, Default mode network, ComputingMilieux_MISCELLANEOUS, Brain Mapping, Subjective memory complaints, Brain functional dynamic, Health Policy, Precision medicine, fMRI, Settore BIO/14, Brain, Brain functional dynamics, Alzheimer's disease, Magnetic Resonance Imaging, Temporal Lobe, Frontal Lobe, Psychiatry and Mental health, medicine.anatomical_structure, Cohort, Biomarker (medicine), Female, Amyloid, 03 medical and health sciences, Cellular and Molecular Neuroscience, Apolipoproteins E, Developmental Neuroscience, Alzheimer Disease, Humans, Aged, Resting state fMRI, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 030104 developmental biology, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

Introduction The longitudinal trajectories of functional brain dynamics and the impact of genetic risk factors in individuals at risk for Alzheimer's disease are poorly understood. Methods In a large-scale monocentric cohort of 224 amyloid stratified individuals at risk for Alzheimer's disease, default mode network (DMN) resting state functional connectivity (FC) was investigated between two serial time points across 2 years. Results Widespread DMN FC changes were shown in frontal and posterior areas, as well as in the right hippocampus. There were no cross-sectional differences, however, apolipoprotein E e4 (APOE e4) carriers demonstrated slower increase in FC in frontal lobes. There was no impact of individual brain amyloid load status. Discussion For the first time, we demonstrated that the pleiotropic biological effect of the APOE e4 allele impacts the dynamic trajectory of the DMN during aging. Dynamic functional biomarkers may become useful surrogate outcomes for the development of preclinical targeted therapeutic interventions.

Published
2019
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9. Association of cerebrospinal fluid α-synuclein with total and phospho-tau181 protein concentrations and brain amyloid load in cognitively normal subjective memory complainers stratified by Alzheimer's disease biomarkers

Author

Vergallo, A, Bun, R, Toschi, N, Baldacci, F, Zetterberg, H, Blennow, K, Cavedo, E, Lamari, F, Habert, M, Dubois, B, Floris, R, Garaci, F, Lista, S, Hampel, H, Audrain, C, Auffret, A, Bakardjian, H, Batrancourt, B, Benakki, I, Benali, H, Bertin, H, Bertrand, A, Boukadida, L, Cacciamani, F, Causse, V, Cherif Touil, S, Chiesa, Pa, Colliot, O, Dalla Barba, G, Depaulis, M, Dos Santos, A, Dubois, M, Epelbaum, S, Fontaine, B, Francisque, H, Gagliardi, G, Genin, A, Genthon, R, Glasman, P, Gombert, F, Habert, Mo, Hewa, H, Houot, M, Jungalee, N, Kas, A, Kilani, M, La Corte, V, Le Roy, F, Lehericy, S, Letondor, C, Levy, M, Lowrey, M, Ly, J, Makiese, O, Masetti, I, Mendes, A, Metzinger, C, Michon, A, Mochel, F, Nait Arab, R, Nyasse, F, Perrin, C, Poirier, F, Poisson, C, Potier, Mc, Ratovohery, S, Revillon, M, Rojkova, K, Santos-Andrade, K, Schindler, R, Servera, Mc, Seux, L, Simon, V, Skovronsky, D, Thiebaut, M, Uspenskaya, O, Vlaincu, M, Aguilar, Lf, Babiloni, C, Benda, N, Black, Kl, Bokde, Alw, Bonuccelli, U, Broich, K, Bun, Rs, Cacciola, F, Castrillo, J, Ceravolo, R, Coman, Cm, Corvol, Jc, Cuello, Ac, Cummings, Jl, Depypere, H, Duggento, A, Durrleman, S, Escott-Price, V, Federoff, H, Ferretti, Mt, Fiandaca, M, Frank, Ra, George, N, Giorgi, Fs, Graziani, M, Haberkamp, M, Herholz, K, Karran, E, Kim, Sh, Koronyo, Y, Koronyo-Hamaoui, M, Langevin, T, Lehéricy, S, Lorenceau, J, Mapstone, M, Neri, C, Nisticò, R, Nyasse-Messene, F, O'Bryant, Se, Perry, G, Ritchie, C, Rossi, S, Santarnecchi, E, Schneider, Ls, Sporns, O, Verdooner, Sr, Villain, N, Welikovitch, L, Woodcock, J, Younesi, E, Vergallo, A., Bun, R. -S., Toschi, N., Baldacci, F., Zetterberg, H., Blennow, K., Cavedo, E., Lamari, F., Habert, M. -O., Dubois, B., Floris, R., Garaci, F., Lista, S., Hampel, H., Audrain, C., Auffret, A., Bakardjian, H., Batrancourt, B., Benakki, I., Benali, H., Bertin, H., Bertrand, A., Boukadida, L., Cacciamani, F., Causse, V., Cherif Touil, S., Chiesa, P. A., Colliot, O., Dalla Barba, G., Depaulis, M., Dos Santos, A., Dubois, M., Epelbaum, S., Fontaine, B., Francisque, H., Gagliardi, G., Genin, A., Genthon, R., Glasman, P., Gombert, F., Habert, M. O., Hewa, H., Houot, M., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Le Roy, F., Lehericy, S., Letondor, C., Levy, M., Lowrey, M., Ly, J., Makiese, O., Masetti, I., Mendes, A., Metzinger, C., Michon, A., Mochel, F., Nait Arab, R., Nyasse, F., Perrin, C., Poirier, F., Poisson, C., Potier, M. C., Ratovohery, S., Revillon, M., Rojkova, K., Santos-Andrade, K., Schindler, R., Servera, M. C., Seux, L., Simon, V., Skovronsky, D., Thiebaut, M., Uspenskaya, O., Vlaincu, M., Aguilar, L. F., Babiloni, C., Benda, N., Black, K. L., Bokde, A. L. W., Bonuccelli, U., Broich, K., Bun, R. S., Cacciola, F., Castrillo, J., Ceravolo, R., Coman, C. M., Corvol, J. C., Cuello, A. C., Cummings, J. L., Depypere, H., Duggento, A., Durrleman, S., Escott-Price, V., Federoff, H., Ferretti, M. T., Fiandaca, M., Frank, R. A., George, N., Giorgi, F. S., Graziani, M., Haberkamp, M., Herholz, K., Karran, E., Kim, S. H., Koronyo, Y., Koronyo-Hamaoui, M., Langevin, T., Lorenceau, J., Mapstone, M., Neri, C., Nistico, R., Nyasse-Messene, F., O'Bryant, S. E., Perry, G., Ritchie, C., Rossi, S., Santarnecchi, E., Schneider, L. S., Sporns, O., Verdooner, S. R., Villain, N., Welikovitch, L., Woodcock, J., and Younesi, E.

Subjects
0301 basic medicine, Epidemiology, Alzheimer's disease, Amyloid PET, Cerebrospinal fluid, Monocentric, Preclinical, Subjective memory complainers, SUVR, Synergistic, Tau protein, α-Synuclein, chemistry.chemical_compound, 0302 clinical medicine, biology, Health Policy, Settore FIS/07, Settore BIO/14, Pathophysiology, Psychiatry and Mental health, medicine.symptom, medicine.medical_specialty, Amyloid, Asymptomatic, 03 medical and health sciences, Cellular and Molecular Neuroscience, Developmental Neuroscience, Neurology (clinical), Geriatrics and Gerontology, Psychiatry and Mental Health, Internal medicine, mental disorders, medicine, Dementia, Alpha-synuclein, business.industry, Alzheimer's disease biomarkers, medicine.disease, 030104 developmental biology, Endocrinology, nervous system, chemistry, Subjective memory complainer, biology.protein, business, 030217 neurology & neurosurgery
Abstract

Introduction Several neurodegenerative brain proteinopathies, including Alzheimer's disease (AD), are associated with cerebral deposition of insoluble aggregates of α-synuclein. Previous studies reported a trend toward increased cerebrospinal fluid (CSF) α-synuclein (α-syn) concentrations in AD compared with other neurodegenerative diseases and healthy controls. Methods The pathophysiological role of CSF α-syn in asymptomatic subjects at risk of AD has not been explored. We performed a large-scale cross-sectional observational monocentric study of preclinical individuals at risk for AD (INSIGHT-preAD). Results We found a positive association between CSF α-syn concentrations and brain β-amyloid deposition measures as mean cortical standard uptake value ratios. We demonstrate positive correlations between CSF α-syn and both CSF t-tau and p-tau 181 concentrations. Discussion Animal models presented evidence, indicating that α-syn may synergistically and directly induce fibrillization of both tau and β-amyloid. Our data indicate an association of CSF α-syn with AD-related pathophysiological mechanisms, during the preclinical phase of the disease.

Published
2018

13. Subjective cognitive decline and rates of incident Alzheimer's disease and non–Alzheimer's disease dementia

Author

Slot, R.E.R. Sikkes, S.A.M. Berkhof, J. Brodaty, H. Buckley, R. Cavedo, E. Dardiotis, E. Guillo-Benarous, F. Hampel, H. Kochan, N.A. Lista, S. Luck, T. Maruff, P. Molinuevo, J.L. Kornhuber, J. Reisberg, B. Riedel-Heller, S.G. Risacher, S.L. Roehr, S. Sachdev, P.S. Scarmeas, N. Scheltens, P. Shulman, M.B. Saykin, A.J. Verfaillie, S.C.J. Visser, P.J. Vos, S.J.B. Wagner, M. Wolfsgruber, S. Jessen, F. Boada, M. de Deyn, P.P. Jones, R. Frisoni, G. Spiru, L. Nobili, F. Freund-Levi, Y. Soininen, H. Verhey, F. Wallin, Å.K. Touchon, J. Rikkert, M.O. Rigaud, A.-S. Bullock, R. Tsolaki, M. Vellas, B. Wilcock, G. Froelich, L. Bakardjian, H. Benali, H. Bertin, H. Bonheur, J. Boukadida, L. Boukerrou, N. Chiesa, P. Colliot, O. Dubois, B. Dubois, M. Epelbaum, S. Gagliardi, G. Genthon, R. Habert, M.-O. Houot, M. Kas, A. Lamari, F. Levy, M. 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. van der Flier, W.M. Alzheimer's Disease Neuroimaging Initiative DESCRIPA working group INSIGHT-preAD study group SCD-I working group

Abstract

Introduction: In this multicenter study on subjective cognitive decline (SCD) in community-based and memory clinic settings, we assessed the (1) incidence of Alzheimer's disease (AD) and non-AD dementia and (2) determinants of progression to dementia. Methods: Eleven cohorts provided 2978 participants with SCD and 1391 controls. We estimated dementia incidence and identified risk factors using Cox proportional hazards models. Results: In SCD, incidence of dementia was 17.7 (95% Poisson confidence interval 15.2-20.3)/1000 person-years (AD: 11.5 [9.6-13.7], non-AD: 6.1 [4.7-7.7]), compared with 14.2 (11.3-17.6) in controls (AD: 10.1 [7.7-13.0], non-AD: 4.1 [2.6-6.0]). The risk of dementia was strongly increased in SCD in a memory clinic setting but less so in a community-based setting. In addition, higher age (hazard ratio 1.1 [95% confidence interval 1.1-1.1]), lower Mini–Mental State Examination (0.7 [0.66-0.8]), and apolipoprotein E ε4 (1.8 [1.3-2.5]) increased the risk of dementia. Discussion: SCD can precede both AD and non-AD dementia. Despite their younger age, individuals with SCD in a memory clinic setting have a higher risk of dementia than those in community-based cohorts. © 2018 The Authors

Published
2019

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

Author

Vergallo, A., Houot, M., Cavedo, E., Lemercier, P., Vanmechelen, E., De Vos, A., Habert, M. -O., Potier, M. -C., Dubois, B., Lista, S., Hampel, H., Bakardjian, H., Benali, H., Bertin, H., Bonheur, J., Boukadida, L., Boukerrou, N., Chiesa, P., Colliot, O., Dubois, M., Epelbaum, S., Gagliardi, G., Genthon, R., Habert, M. O., Kas, A., Lamari, F., Levy, M., 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., Younsi, N., Afshar, M., Flores Aguilar, L., Akman-Anderson, L., Arenas, J., Avila, J., Babiloni, C., Baldacci, F., Batrla, R., Benda, N., Black, K. L., Bokde, A. L. W., Bonuccelli, U., Broich, K., Cacciola, F., Caraci, F., Castrillo, J., Ceravolo, R., Chiesa, P. A., Corvol, J. -C., Claudio Cuello, A., Cummings, J. L., Depypere, H., Duggento, A., Emanuele, E., Escott-Price, V., Federoff, H., Teresa Ferretti, M., Fiandaca, M., Frank, R. A., Garaci, F., Geerts, H., Giorgi, F. S., Goetzl, E. J., Graziani, M., Haberkamp, M., Marie-Odile, H., Herholz, K., Hernandez, F., Kapogiannis, D., Karran, E., Kiddle, S. J., Kim, S. H., Koronyo, Y., Koronyo-Hamaoui, M., Langevin, T., Lehericy, S., Lucia, A., Lorenceau, J., Mango, D., Mapstone, M., Neri, C., Nistico, R., O'Bryant, S. E., Palermo, G., Perry, G., Ritchie, C., Rossi, S., Saidi, A., Santarnecchi, E., Schneider, L. S., Sporns, O., Toschi, N., Verdooner, S. R., Villain, N., Welikovitch, L. A., Woodcock, J., Younesi, E., Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'investigation clinique Neurosciences [CHU Pitié Salpêtrière] (CIC Neurosciences), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Service de médecine nucléaire [CHU Pitié-Salpétrière]

Subjects
Male, 0301 basic medicine, Apolipoprotein E, Epidemiology, [SDV]Life Sciences [q-bio], PROGRESSION, Disease, Cognition, 0302 clinical medicine, Amyloid precursor protein, Medicine and Health Sciences, Aspartic Acid Endopeptidases, medicine.diagnostic_test, biology, Health Policy, Settore BIO/14, Brain, Alzheimer's disease, Healthy Volunteers, 3. Good health, GENOTYPE, ALZHEIMERS-DISEASE, Psychiatry and Mental health, Positron emission tomography, Cohort, Biomarker (medicine), Female, EXPRESSION, medicine.medical_specialty, BIOMARKERS, Standardized uptake value, 03 medical and health sciences, Cellular and Molecular Neuroscience, Sexual dimorphism, Apolipoproteins E, Sex Factors, Developmental Neuroscience, Alzheimer Disease, Internal medicine, mental disorders, medicine, Humans, BACE1 biomarkers, Aged, Plasma BACE1, DECLINE, Amyloid beta-Peptides, business.industry, 030104 developmental biology, Endocrinology, Positron-Emission Tomography, Disease modifying, biology.protein, Neurology (clinical), Amyloid Precursor Protein Secretases, Geriatrics and Gerontology, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

Introduction: Successful development of effective beta-site amyloid precursor protein cleaving enzyme 1 (BACE1)-targeted therapies for early stages of Alzheimer's disease requires biomarker-guided intervention strategies. Methods: We investigated whether key biological factors such as sex, apolipoprotein E (APOE epsilon 4) allele, and age affect longitudinal plasma BACE1 concentrations in a large monocenter cohort of individuals at risk for Alzheimer's disease. We explored the relationship between plasma BACE1 concentrations and levels of brain amyloid-beta (A beta) deposition, using positron emission tomography global standard uptake value ratios. Results: Baseline and longitudinal mean concentrations of plasma BACE1 were significantly higher in women than men. We also found a positive significant impact of plasma BACE1 on baseline A beta-positron emission tomography global standard uptake value ratios. Discussion: Our results suggest a sexual dimorphism in BACE1-related upstream mechanisms of brain A beta production and deposition. We argue that plasma BACE1 should be considered in further biomarker validation and qualification studies as well as in BACE1 clinical trials. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.

Published
2019
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18. Low Cognitive Awareness, but Not Complaint, is a Good Marker of Preclinical Alzheimer's Disease

Author

Cacciamani, Federica, Tandetnik, Caroline, Gagliardi, Geoffroy, Bertin, Hugo, Habert, Marie-Odile, Hampel, Harald, Boukadida, Laurie, Révillon, Marie, Epelbaum, Stéphane, Dubois, Bruno, Audrain, C, Auffret, A, Bakardjian, H, Baldacci, F, Batrancourt, B, Benakki, I, Benali, H, Bertin, H, Bertrand, A, Boukadida, L, Cacciamani, F, Causse, V, Cavedo, E, Cherif Touil, S, Chiesa, P A, Colliot, O, Dalla Barba, G, Depaulis, M, Dos Santos, A, Dubois, B, Dubois, M, Epelbaum, S, Fontaine, B, Francisque, H, Gagliardi, G, Genin, A, Genthon, R, Glasman, P, Gombert, F, Habert, M O, Hampel, H, Hewa, H, Houot, M, Jungalee, N, Kas, A, Kilani, M, La Corte, V, Le Roy, F, Lehericy, S, Letondor, C, Levy, M, Lista, S, Lowrey, M, Ly, J, Makiese, O, Masetti, I, Mendes, A, Metzinger, C, Michon, A, Mochel, F, Nait Arab, R, Nyasse, F, Perrin, C, Poirier, F, Poisson, C, Potier, M C, Ratovohery, S, Revillon, M, Rojkova, K, Santos-Andrade, K, Schindler, R, Servera, M C, Seux, L, Simon, V, Skovronsky, D, Thiebaut, M, Uspenskaya, O, Vlaincu, M, Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Mécanique et d'Ingénierie de Bordeaux (I2M), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC), Service de Neuroradiologie [CHU Pitié-Salpêtrière], École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Institut National de la Recherche Agronomique (INRA), Service de médecine nucléaire [CHU Pitié-Salpétrière], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Service d'Explorations Fonctionnelles Neurologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP], HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP]-Inria de Paris, Cacciamani, Federica, Tandetnik, Caroline, Gagliardi, Geoffroy, Bertin, Hugo, Habert, Marie-Odile, Hampel, Harald, Boukadida, Laurie, Révillon, Marie, Epelbaum, Stéphane, Dubois, Bruno, Audrain, C, Auffret, A, Bakardjian, H, Baldacci, F, Batrancourt, B, Benakki, I, Benali, H, Bertin, H, Bertrand, A, Boukadida, L, Cacciamani, F, Causse, V, Cavedo, E, Cherif Touil, S, Chiesa, P A, Colliot, O, Dalla Barba, G, Depaulis, M, Dos Santos, A, Dubois, B, Dubois, M, Epelbaum, S, Fontaine, B, Francisque, H, Gagliardi, G, Genin, A, Genthon, R, Glasman, P, Gombert, F, Habert, M O, Hampel, H, Hewa, H, Houot, M, Jungalee, N, Kas, A, Kilani, M, La Corte, V, Le Roy, F, Lehericy, S, Letondor, C, Levy, M, Lista, S, Lowrey, M, Ly, J, Makiese, O, Masetti, I, Mendes, A, Metzinger, C, Michon, A, Mochel, F, Nait Arab, R, Nyasse, F, Perrin, C, Poirier, F, Poisson, C, Potier, M C, Ratovohery, S, Revillon, M, Rojkova, K, Santos-Andrade, K, Schindler, R, Servera, M C, Seux, L, Simon, V, Skovronsky, D, Thiebaut, M, Uspenskaya, O, Vlaincu, M, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Service de Médecine nucléaire [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris

Subjects
Male, Ethylene Glycol, [SDV]Life Sciences [q-bio], awarene, Neuropsychological Tests, Alzheimer’s disease, awareness, biomarkers, cognitive complaints, subjective cognitive decline, Aged, Aged, 80 and over, Alzheimer Disease, Analysis of Variance, Aniline Compounds, Brain, Brain Mapping, Cognitive Dysfunction, Ethylene Glycols, Female, Functional Laterality, Humans, Positron-Emission Tomography, Psychiatric Status Rating Scales, Surveys and Questionnaires, Awareness, cognitive complaint, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, 80 and over, Surveys and Questionnaire, Aniline Compound, Psychiatric Status Rating Scale, Alzheimer's disease, Clinical Psychology, Psychiatry and Mental Health, biomarker, Neuropsychological Test, Geriatrics and Gerontology, Human
Abstract

International audience; Background:Subjective cognitive decline (SCD) may result from many conditions, including Alzheimer’s disease (AD). Objective:In this study, we searched for a specific pattern of SCD in asymptomatic individuals at risk for AD. Methods:Cognitively normal older adults (N = 318) reporting SCD and their informants were enrolled in the INSIGHT-PreAD cohort. We examined the relationship between six SCD measures and both cognitive scores and AD neuroimaging markers (amyloid burden, hippocampal atrophy and brain hypometabolism). An awareness of cognitive decline index (ACDI) has been introduced based on the subject-informant discrepancy in a questionnaire of SCD and participants with low versus high awareness were compared. Results:Scores in the INSIGHT-PreAD SCD questionnaires did not correlate with AD neuroimaging markers. As well, no correlation has been found between SCD measures and cognitive scores. Comparing subjects with a low (n = 19) and high (n = 86) level of awareness, no significant difference in terms of demography, neuropsychiatric symptoms, autonomy, quality of life, cognition, and hippocampal volume was found. However, the “low awareness” group showed greater amyloid burden and lower cortical metabolism, compared to the “high awareness” group. Conclusion:This study provided additional evidence that reporting SCD by itself is not a specific symptom of preclinical AD. Conversely, a low cognitive awareness (namely, when subjects report fewer difficulties than their relatives do) may represent a very early form of anosognosia and serve as a specific indicator of preclinical AD. This finding is of key importance as an enrichment factor to consider in both clinical practice and research trials.

Published
2017
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19. Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults

Author

ten Kate, Mara, Visser, Pieter Jelle, Bakardjian, Hovagim, Barkhof, Frederik, Sikkes, Sietske A. M., van der Flier, Wiesje M., Scheltens, Philip, Hampel, Harald, Habert, Marie-Odile, Dubois, Bruno, Tijms, Betty M., Audrain, C., Auffret, A., Bakardjian, H., Baldacci, F., Batrancourt, B., Benakki, I., Benali, H., Bertin, H., Bertrand, A., Boukadida, L., Cacciamani, F., Causse, V., Cavedo, E., Cherif Touil, S., Chiesa, P. A., Colliot, O., Dalla Barba, G., Depaulis, M., Dos Santos, A., Dubois, B., Dubois, M., Epelbaum, S., Fontaine, B., Francisque, H., Gagliardi, G., Genin, A., Genthon, R., Glasman, P., Gombert, F., Habert, M. O., Hampel, H., Hewa, H., Houot, M., Jungalee, N., Kas, A., Kilani, M., La Corte, V., Le Roy, F., Lehericy, S., Letondor, C., Levy, M., Lista, S., Lowrey, M., Ly, J., Makiese, O., Masetti, I., Mendes, A., Metzinger, C., Michon, A., Mochel, F., Nait Arab, R., Nyasse, F., Perrin, C., Poirier, F., Poisson, C., Potier, M. C., Ratovohery, S., Revillon, M., Rojkova, K., Santos-Andrade, K., Schindler, R., Servera, M. C., Seux, L., Simon, V., Skovronsky, D., Thiebaut, M., Uspenskaya, O., Vlaincu, M., Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Université Pierre et Marie Curie - Paris 6 (UPMC), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), AXA Research Fund, Sorbonne Université (SU), Alzheimer Precision Medicine [CHU Pitié-Salpétriêre] (GRC 21 AMP), CHU Pitié-Salpêtrière [AP-HP], 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), RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychiatrie & Neuropsychologie, Radiology and nuclear medicine, Amsterdam Neuroscience - Neurodegeneration, Neurology, APH - Personalized Medicine, APH - Methodology, and Epidemiology and Data Science

Subjects
0301 basic medicine, PRECLINICAL ALZHEIMERS-DISEASE, Aging, 0302 clinical medicine, Original Research, biology, medicine.diagnostic_test, subjective memory complaints, APOE GENOTYPE, Alzheimer's disease, medicine.anatomical_structure, Cerebral cortex, Positron emission tomography, Cardiology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], STRUCTURAL COVARIANCE, Alzheimer’s disease, MRI, medicine.medical_specialty, Amyloid, Amyloid beta, Cognitive Neuroscience, graph theory, PLAQUE BURDEN, Standardized uptake value, Cognitive neuroscience, SPATIAL-PATTERNS, gray matter network, lcsh:RC321-571, Graph theory, Gray matter network, PET, Subjective memory complaints, 03 medical and health sciences, POSITRON-EMISSION-TOMOGRAPHY, Internal medicine, CEREBRAL-CORTEX, medicine, Dementia, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Pathological, business.industry, HUMAN CORTICAL NETWORKS, medicine.disease, amyloid beta, BRAIN NETWORKS, 030104 developmental biology, PROSPECTIVE COHORT, biology.protein, business, 030217 neurology & neurosurgery, Neuroscience
Abstract

for the INSIGHT-preAD study group; International audience; The accumulation of amyloid plaques is one of the earliest pathological changes in Alzheimer's disease (AD) and may occur 20 years before the onset of symptoms. Examining associations between amyloid pathology and other early brain changes is critical for understanding the pathophysiological underpinnings of AD. Alterations in gray matter networks might already start at early preclinical stages of AD. In this study, we examined the regional relationship between amyloid aggregation measured with positron emission tomography (PET) and gray matter network measures in elderly subjects with subjective memory complaints. Single-subject gray matter networks were extracted from T1-weigthed structural MRI in cognitively normal subjects (n = 318, mean age 76.1 ± 3.5, 64% female, 28% amyloid positive). Degree, clustering, path length and small world properties were computed. Global and regional amyloid load was determined using [ 18 F]-Florbetapir PET. Associations between standardized uptake value ratio (SUVr) values and network measures were examined using linear regression models. We found that higher global SUVr was associated with lower clustering (β = −0.12, p < 0.05), and small world values (β = −0.16, p < 0.01). Associations were most prominent in orbito-and dorsolateral frontal and parieto-occipital regions. Local SUVr values showed less anatomical variability and did not convey additional information beyond global amyloid burden. In conclusion, we found that in cognitively normal elderly subjects, increased global amyloid pathology is associated with alterations in gray matter networks that are indicative of incipient network breakdown towards AD dementia.

Published
2018
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29. Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set.

Author

Duncan, James S., Gerig, Guido, Colliot, O., Mansi, T., Bernasconi, N., Naessens, V., Klironomos, D., and Bernasconi, A.

Abstract

Focal cortical dysplasia (FCD), a malformation of cortical development, is an important cause of medically intractable epilepsy. FCD lesions are difficult to distinguish from non-lesional cortex and their delineation on MRI is a challenging task. This paper presents a method to segment FCD lesions on T1-weighted MRI, based on a 3D deformable model, implemented using the level set framework. The deformable model is driven by three MRI features: cortical thickness, relative intensity and gradient. These features correspond to the visual characteristics of FCD and allow to differentiate lesions from normal tissues. The proposed method was tested on 18 patients with FCD and its performance was quantitatively evaluated by comparison with the manual tracings of two trained raters. The validation showed that the similarity between the level set segmentation and the manual labels is similar to the agreement between the two human raters. This new approach may become a useful tool for the presurgical evaluation of patients with intractable epilepsy. [ABSTRACT FROM AUTHOR]

Published
2005
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30. Diffeomorphic Brain Registration Under Exhaustive Sulcal Constraints.

Author

Auzias, G, Colliot, O, Glaunes, J A, Perrot, M, Mangin, J-F, Trouve, A, and Baillet, S

Subjects
*BRAIN anatomy, *IMAGE registration, *BRAIN imaging, *COMPUTATIONAL geometry, *MAGNETIC resonance imaging, *GRAY matter (Nerve tissue)
Abstract

The alignment and normalization of individual brain structures is a prerequisite for group-level analyses of structural and functional neuroimaging data. The techniques currently available are either based on volume and/or surface attributes, with limited insight regarding the consistent alignment of anatomical landmarks across individuals. This article details a global, geometric approach that performs the alignment of the exhaustive sulcal imprints (cortical folding patterns) across individuals. This DIffeomorphic Sulcal-based COrtical (DISCO) technique proceeds to the automatic extraction, identification and simplification of sulcal features from T1-weighted Magnetic Resonance Image (MRI) series. These features are then used as control measures for fully-3-D diffeomorphic deformations. Quantitative and qualitative evaluations show that DISCO correctly aligns the sulcal folds and gray and white matter volumes across individuals. The comparison with a recent, iconic diffeomorphic approach (DARTEL) highlights how the absence of explicit cortical landmarks may lead to the misalignment of cortical sulci. We also feature DISCO in the automatic design of an empirical sulcal template from group data. We also demonstrate how DISCO can efficiently be combined with an image-based deformation (DARTEL) to further improve the consistency and accuracy of alignment performances. Finally, we illustrate how the optimized alignment of cortical folds across subjects improves sensitivity in the detection of functional activations in a group-level analysis of neuroimaging data. [ABSTRACT FROM PUBLISHER]

Published
2011
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36. Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Author

Moreno, A., Takemura, C.M., Colliot, O., Camara, O., and Bloch, I.

Subjects
*MATHEMATICAL logic, *FUZZY systems, *PHOTOTHERAPY, *FUZZY logic
Abstract

Abstract: Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach. [Copyright &y& Elsevier]

Published
2008
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37. Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.

Author

Hemforth L, Couvy-Duchesne B, De Matos K, Brianceau C, Joulot M, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Papadopoulos D, Lemaitre H, Paus T, Poustka L, Hohman S, Holz N, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Büchel C, Poline JB, Itterman B, Frouin V, Martin A, Cury C, and Colliot O

Abstract

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted., Competing Interests: 6.3.1Disclosure statement Competing financial interests related to the present article: none to disclose for all authors. Competing financial interests unrelated to the present article: OC reports having received consulting fees from AskBio (2020) and Therapanacea (2022–2024), and that his laboratory has received grants (paid to the institution) from Qynapse (2017–2022). Members from his laboratory have co-supervised a PhD thesis with Qynapse (2017–2022). OC’s spouse was an employee of myBrainTechnologies and is an employee of DiamPark. OC holds a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). Tobias Banaschewski served in an advisory or consultancy role for eye level, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker’s fee by Janssen, Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press; the present work is unrelated to these relationships. Dr. Barker has received honoraria from General Electric Healthcare for teaching on scanner programming courses. Dr. Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships.The other authors report no biomedical financial interests or potential conflicts of interest.

Published
2024

38. Projected pooling loss for red nucleus segmentation with soft topology constraints.

Author

Fu G, El Jurdi R, Chougar L, Dormont D, Valabregue R, Lehéricy S, and Colliot O

Abstract

Purpose: Deep learning is the standard for medical image segmentation. However, it may encounter difficulties when the training set is small. Also, it may generate anatomically aberrant segmentations. Anatomical knowledge can be potentially useful as a constraint in deep learning segmentation methods. We propose a loss function based on projected pooling to introduce soft topological constraints. Our main application is the segmentation of the red nucleus from quantitative susceptibility mapping (QSM) which is of interest in parkinsonian syndromes., Approach: This new loss function introduces soft constraints on the topology by magnifying small parts of the structure to segment to avoid that they are discarded in the segmentation process. To that purpose, we use projection of the structure onto the three planes and then use a series of MaxPooling operations with increasing kernel sizes. These operations are performed both for the ground truth and the prediction and the difference is computed to obtain the loss function. As a result, it can reduce topological errors as well as defects in the structure boundary. The approach is easy to implement and computationally efficient., Results: When applied to the segmentation of the red nucleus from QSM data, the approach led to a very high accuracy (Dice 89.9%) and no topological errors. Moreover, the proposed loss function improved the Dice accuracy over the baseline when the training set was small. We also studied three tasks from the medical segmentation decathlon challenge (MSD) (heart, spleen, and hippocampus). For the MSD tasks, the Dice accuracies were similar for both approaches but the topological errors were reduced., Conclusions: We propose an effective method to automatically segment the red nucleus which is based on a new loss for introducing topology constraints in deep learning segmentation., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Published
2024
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39. Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty.

Author

Chougar L, Faucher A, Faouzi J, Lejeune FX, Gama Lobo G, Jovanovic C, Cormier F, Dupont G, Vidailhet M, Corvol JC, Colliot O, Lehéricy S, Grabli D, and Degos B

Subjects
Humans, Female, Male, Aged, Middle Aged, Machine Learning, Uncertainty, Diagnosis, Differential, Sensitivity and Specificity, Magnetic Resonance Imaging methods, Magnetic Resonance Imaging standards, Supranuclear Palsy, Progressive diagnostic imaging, Supranuclear Palsy, Progressive diagnosis, Parkinsonian Disorders diagnostic imaging, Parkinsonian Disorders diagnosis, Parkinson Disease diagnostic imaging, Parkinson Disease diagnosis, Multiple System Atrophy diagnostic imaging, Multiple System Atrophy diagnosis, Early Diagnosis
Abstract

Background: International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages., Objectives: To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism., Materials: Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up., Results: We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08)., Conclusion: Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Published
2024
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40. Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data.

Author

Loizillon S, Bottani S, Maire A, Ströer S, Dormont D, Colliot O, and Burgos N

Subjects
Humans, Motion, Brain diagnostic imaging, Magnetic Resonance Imaging, Artifacts, Data Warehousing
Abstract

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Competing financial interests related to the present article: none to disclose for all authors. Competing financial interests unrelated to the present article: OC reports having received consulting fees from AskBio and Therapanacea and having received fees for writing a lay audience short paper from Expression Santé. O.C. holds a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices). OC is in the editorial board of Medical Image Analysis., (Copyright © 2023. Published by Elsevier B.V.)

Published
2024
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41. Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.

Author

Bottani S, Thibeau-Sutre E, Maire A, Ströer S, Dormont D, Colliot O, and Burgos N

Subjects
Humans, Brain diagnostic imaging, Magnetic Resonance Imaging methods, Neuroimaging methods, Image Processing, Computer-Assisted methods, Data Warehousing, Gadolinium
Abstract

Background: Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse., Methods: We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area., Results: Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images., Conclusion: We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse., (© 2024. The Author(s).)

Published
2024
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42. Proxy-analysis of the genetics of cognitive decline in Parkinson's disease through polygenic scores.

Author

Faouzi J, Tan M, Casse F, Lesage S, Tesson C, Brice A, Mangone G, Mariani LL, Iwaki H, Colliot O, Pihlstrøm L, and Corvol JC

Abstract

Cognitive decline is common in Parkinson's disease (PD) and its genetic risk factors are not well known to date, besides variants in the GBA and APOE genes. However, variation in complex traits is caused by numerous variants and is usually studied with genome-wide association studies (GWAS), requiring a large sample size, which is difficult to achieve for outcome measures in PD. Taking an alternative approach, we computed 100 polygenic scores (PGS) related to cognitive, dementia, stroke, and brain anatomical phenotypes and investigated their association with cognitive decline in six longitudinal cohorts. The analysis was adjusted for age, sex, genetic ancestry, follow-up duration, GBA and APOE status. Then, we meta-analyzed five of these cohorts, comprising a total of 1702 PD participants with 6156 visits, using the Montreal Cognitive Assessment as a cognitive outcome measure. After correction for multiple comparisons, we found four PGS significantly associated with cognitive decline: intelligence (p = 5.26e-13), cognitive performance (p = 1.46e-12), educational attainment (p = 8.52e-10), and reasoning (p = 3.58e-5). Survival analyses highlighted an offset of several years between the first and last quartiles of PGS, with significant differences for the PGS of cognitive performance (5 years) and educational attainment (7 years). In conclusion, we found four PGS associated with cognitive decline in PD, all associated with general cognitive phenotypes. This study highlights the common genetic factors between cognitive decline in PD and the general population, and the importance of the participant's cognitive reserve for cognitive outcome in PD., (© 2024. The Author(s).)

Published
2024
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43. Genomic loci influence patterns of structural covariance in the human brain.

Author

Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, and Davatzikos C

Subjects
Humans, Brain pathology, Brain Mapping methods, Genomics, Magnetic Resonance Imaging methods, Brain Neoplasms pathology
Abstract

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain., Competing Interests: Competing interests statement:D.H.W. served as Site PI for studies by Biogen, Merck, and Eli Lilly/Avid. He has received consulting fees from GE Healthcare and Neuronix. He is on the DSMB for a trial sponsored by Functional Neuromodulation. A.J.S. receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in-kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions, Inc. (Dementia Advisory Board); and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging, and Behavior). O.C. reports having received consulting fees from AskBio (2020) and Therapanacea (2022), having received payments for writing a lay audience short paper from Expression Santé (2019), and that his laboratory has received grants (paid to the institution) from Qynapse (2017 to present). Members of his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016 to 2019) and with Qynapse (2017 to present). O.C.’s spouse is an employee and holds stock options of myBrainTechnologies (2015 to present). O.C. has a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, J.-B. Schiratti, S. Allassonniere, O.C., S. Durrleman, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). M.E. receives support from multiple NIH grants, the Alzheimer’s Association, and the Alzheimer’s Therapeutic Research Institute. M.V.Z. serves as a consultant and/or speaker for the following pharmaceutical companies: Eurofarma, Lundbeck, Abbott, Greencare, Myralis, and Elleven Healthcare.

Published
2023
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45. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse.

Author

Bottani S, Burgos N, Maire A, Saracino D, Ströer S, Dormont D, and Colliot O

Subjects
Humans, Data Warehousing, Brain diagnostic imaging, Magnetic Resonance Imaging methods, Machine Learning, Computers, Contrast Media, Alzheimer Disease diagnostic imaging
Abstract

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Competing financial interests unrelated to the present article: OC reports having received consulting fees from AskBio and Therapanacea and having received fees for writing a lay audience short paper from Expression Santé. Members from his laboratory have co-supervised a PhD thesis with myBrainTechnologies and with Qynapse. OC’s spouse is an employee and holds stock-options of myBrainTechnologies. O.C. holds a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices)., (Copyright © 2023. Published by Elsevier B.V.)

Published
2023
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46. Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability.

Author

de Matos K, Cury C, Chougar L, Strike LT, Rolland T, Riche M, Hemforth L, Martin A, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Papadopoulos Orfanos D, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Frouin V, Bach Cuadra M, Colliot O, and Couvy-Duchesne B

Subjects
Male, Female, Humans, Magnetic Resonance Imaging, Hippocampus, Functional Laterality genetics, Sex Characteristics, Temporal Lobe diagnostic imaging
Abstract

The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35-40% versus 20-25% in males) and an RS-CS connection more common in males (approximately 40-45% versus 25-30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28-0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2023
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47. Comparison of mean diffusivity, R2* relaxation rate and morphometric biomarkers for the clinical differentiation of parkinsonism.

Author

Chougar L, Lejeune FX, Faouzi J, Morino B, Faucher A, Hoyek N, Grabli D, Cormier F, Vidailhet M, Corvol JC, Colliot O, Degos B, and Lehéricy S

Subjects
Humans, Diffusion Magnetic Resonance Imaging, Brain Stem pathology, Magnetic Resonance Imaging methods, Diagnosis, Differential, Parkinsonian Disorders pathology, Parkinson Disease, Supranuclear Palsy, Progressive pathology, Multiple System Atrophy pathology
Abstract

Introduction: Quantitative biomarkers for clinical differentiation of parkinsonian syndromes are still lacking. Our aim was to evaluate the value of combining clinically feasible manual measurements of R2* relaxation rates and mean diffusivity (MD) in subcortical regions and brainstem morphometric measurements to improve the discrimination of parkinsonian syndromes., Methods: Twenty-two healthy controls (HC), 25 patients with Parkinson's disease (PD), 19 with progressive supranuclear palsy (PSP) and 27 with multiple system atrophy (MSA, 21 with the parkinsonian variant -MSAp, 6 with the cerebellar variant -MSAc) were recruited. R2*, MD measurements and morphometric biomarkers including the midbrain to pons area ratio and the Magnetic Resonance Parkinsonism Index (MRPI) were compared between groups and their diagnostic performances were assessed., Results: Morphometric biomarkers discriminated better patients with PSP (ratio: AUC 0.89, MRPI: AUC 0.89) and MSAc (ratio: AUC 0.82, MRPI: AUC 0.75) from other groups. R2* and MD measurements in the posterior putamen performed better in separating patients with MSAp from PD (R2*: AUC 0.89; MD: AUC 0.89). For the three-class classification "MSA vs PD vs PSP", the combination of MD and R2* measurements in the posterior putamen with morphometric biomarkers (AUC: 0.841) outperformed each marker separately. At the individual-level, there were seven discordances between imaging-based prediction and clinical diagnosis involving MSA. Using the new Movement Disorder Society criteria for the diagnosis of MSA, three of these seven patients were clinically reclassified as predicted by quantitative imaging., Conclusion: Combining R2* and MD measurements in the posterior putamen with morphometric biomarkers improves the discrimination of parkinsonism., Competing Interests: Declaration of competing interest No conflicts of interest related to the present study. Competing financial interests unrelated to the present work: Jean-Christophe Corvol has served in scientific advisory boards for Biogen, Denali, Ever Pharma, Isdorsia, Prevail Therapeutics, and UCB; and has received grants from Sanofi, the Michael J Fox Foundation, ANR, France Parkinson, the French Ministry of Health. Olivier Colliot received consulting fees from AskBio (2020), received fees for writing a lay audience short paper from Expression Santé (2019), received speaker fees for a lay audience presentation from Palais de la découverte (2017). His laboratory received grants (paid to the institution) from Air Liquide Medical Systems (2011–2016) and Qynapse (2017–present). Members from his laboratory have cosupervised a PhD thesis with myBrainTechnologies (2016–present). Colliot's spouse is an employee of myBrainTechnologies (2015–present). Colliot has submitted a patent to the International Bureau of the World Intellectual Property Organization (PCT/IB2016/052699 3, J.-B. Schiratti, S. Allassonniere, O. Colliot, S. Durrleman, “A method for determining the temporal progression of a biological phenomenon and associated methods and devices,” 2016). Bertrand Degos received research support grants from Fondation de France, Inserm, and ANR; speech honoraria from Ipsen, Merz Pharma, and Orkyn; and received travel funding from Merz Pharma, Elivie, and Orkyn. Stéphane Lehéricy received grants from ‘Investissements d'avenir’ (gran t numbers ANR-10-IAIHU -06 and AN R-11-INBS-0006) and Biogen Inc., (Copyright © 2023. Published by Elsevier Ltd.)

Published
2023
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48. Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis.

Author

Yazdan-Panah A, Schmidt-Mengin M, Ricigliano VAG, Soulier T, Stankoff B, and Colliot O

Subjects
Humans, Magnetic Resonance Imaging methods, Choroid pathology, Image Processing, Computer-Assisted methods, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology, Parkinson Disease pathology
Abstract

Choroid Plexuses (ChP) are structures located in the ventricles that produce the cerebrospinal fluid (CSF) in the central nervous system. They are also a key component of the blood-CSF barrier. Recent studies have described clinically relevant ChP volumetric changes in several neurological diseases including Alzheimer's, Parkinson's disease, and multiple sclerosis (MS). Therefore, a reliable and automated tool for ChP segmentation on images derived from magnetic resonance imaging (MRI) is a crucial need for large studies attempting to elucidate their role in neurological disorders. Here, we propose a novel automatic method for ChP segmentation in large imaging datasets. The approach is based on a 2-step 3D U-Net to keep preprocessing steps to a minimum for ease of use and to lower memory requirements. The models are trained and validated on a first research cohort including people with MS and healthy subjects. A second validation is also performed on a cohort of pre-symptomatic MS patients having acquired MRIs in routine clinical practice. Our method reaches an average Dice coefficient of 0.72 ± 0.01 with the ground truth and a volume correlation of 0.86 on the first cohort while outperforming FreeSurfer and FastSurfer-based ChP segmentations. On the dataset originating from clinical practice, the method reaches a Dice coefficient of 0.67 ± 0.01 (being close to the inter-rater agreement of 0.64 ± 0.02) and a volume correlation of 0.84. These results demonstrate that this is a suitable and robust method for the segmentation of the ChP both on research and clinical datasets., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2023
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49. The impact of aging on human brain network target controllability.

Author

Bassignana G, Lacidogna G, Bartolomeo P, Colliot O, and De Vico Fallani F

Subjects
Humans, Aged, Brain, Aging, Parietal Lobe, Magnetic Resonance Imaging methods, Connectome, Stroke
Abstract

Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease. Recent endeavors based on network controllability provided fresh insights into the potential ability of single regions to influence whole brain dynamics through the underlying structural connectome. However, controlling the entire brain activity is often unfeasible and might not always be necessary. The question whether single areas can control specific target subsystems remains crucial, albeit still poorly explored. Furthermore, the structure of the brain network exhibits progressive changes across the lifespan, but little is known about the possible consequences in the controllability properties. To address these questions, we adopted a novel target controllability approach that quantifies the centrality of brain nodes in controlling specific target anatomo-functional systems. We then studied such target control centrality in human connectomes obtained from healthy individuals aged from 5 to 85. Main results showed that the sensorimotor system has a high influencing capacity, but it is difficult for other areas to influence it. Furthermore, we reported that target control centrality varies with age and that temporal-parietal regions, whose cortical thinning is crucial in dementia-related diseases, exhibit lower values in older people. By simulating targeted attacks, such as those occurring in focal stroke, we showed that the ipsilesional hemisphere is the most affected one regardless of the damaged area. Notably, such degradation in target control centrality was more evident in younger people, thus supporting early-vulnerability hypotheses after stroke., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2022
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50. Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised Variational Autoencoders.

Author

Kmetzsch V, Becker E, Saracino D, Rinaldi D, Camuzat A, Le Ber I, and Colliot O

Subjects
Humans, Cross-Sectional Studies, Multimodal Imaging, Biomarkers, Disease Progression, MicroRNAs genetics
Abstract

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with state-of-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

Published
2022
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