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

Abstract : 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.
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https://hal.sorbonne-universite.fr/hal-03261206
Contributor : Hal Sorbonne Université Gestionnaire <>
Submitted on : Tuesday, June 15, 2021 - 3:06:52 PM
Last modification on : Tuesday, July 13, 2021 - 3:28:04 AM

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Sinead Gaubert, Marion Houot, Federico Raimondo, Manon Ansart, Marie-Constance Corsi, et al.. A machine learning approach to screen for preclinical Alzheimer's disease Authors. Neurobiology of Aging, Elsevier, 2021, 105, pp.205-216. ⟨10.1016/j.neurobiolaging.2021.04.024⟩. ⟨hal-03261206⟩

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