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Date publication

septembre 2021

Journal

Neurobiology of aging

Auteurs

Membres identifiés du Cancéropôle Est :
Dr ANSART Manon


Tous les auteurs :
Gaubert S, Houot M, Raimondo F, Ansart M, Corsi MC, Naccache L, Sitt JD, Habert MO, Dubois B, De Vico Fallani F, Durrleman S, Epelbaum S,

Résumé

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 F-florbetapir and F-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.

Mots clés

EEG, Machine learning, Multimodal, Neurodegeneration, Preclinical Alzheimer's disease

Référence

Neurobiol Aging. 2021 09;105:205-216