A machine learning approach to screen for preclinical Alzheimer's disease

[en] Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers. © 2021 Elsevier Inc.

Disciplines :

Neurologie

Auteur, co-auteur :

Gaubert, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Université de Paris, Lariboisière Fernand-Widal Hospital, Cognitive Neurology Center, Paris, France

Houot, M.; AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Center for Clinical Investigation (CIC) Neurosciences, Institut du cerveau et de la Moelle épinière (ICM), Paris, F75013, France, Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'Hôpital, Paris, F-75013, France

Raimondo, Federico ; Université de Liège - ULg

Ansart, M.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Corsi, M.-C.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Naccache, L.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, F-75013, France

Sitt, J. D.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, U992, Gif/Yvette, F-91191, France, NeuroSpin Centre, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, F-91191, France

Habert, M.-O.; Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U1146, CNRS UMR 7371, Paris, France, AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, F-75013, France, Centre d'Acquisition et de Traitement des Images, CATI neuroimaging platform, France

Dubois, B.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France

De Vico Fallani, F.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Durrleman, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Epelbaum, S.; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, F75013, France, AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN) and National Reference Centre for Rare or Early Dementias, Department of Neurology, Paris, F75013, France, Inria, Aramis project-team, Paris, F-75013, France

Langue du document :

Anglais

Titre :

A machine learning approach to screen for preclinical Alzheimer's disease

Date de publication/diffusion :

2021

Titre du périodique :

Neurobiology of Aging

ISSN :

0197-4580

eISSN :

1558-1497

Maison d'édition :

Elsevier, Pays-Bas

Volume/Tome :

105

Fascicule/Saison :

Septembre

Pagination :

205-216

Peer reviewed :

Peer reviewed vérifié par ORBi

Disponible sur ORBi :

depuis le 17 juin 2021

Statistiques

Nombre de vues

111 (dont 1 ULiège)

Nombre de téléchargements

1 (dont 1 ULiège)

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