神经退行性变
脑电图
生物标志物
神经心理学
医学
疾病
队列
肿瘤科
阿尔茨海默病
内科学
预测值
神经科学
病理
听力学
心理学
生物
认知
生物化学
作者
Sinead Gaubert,Marion Houot,Federico Raimondo,Manon Ansart,Marie‐Constance Corsi,Lionel Naccache,Jacobo Sitt,Marie‐Odile Habert,Bruno Dubois,Fabrizio De Vico Fallani,Stanley Durrleman,Stéphane Epelbaum
标识
DOI:10.1016/j.neurobiolaging.2021.04.024
摘要
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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