EEG‐based neural biomarker for diagnosis and assessment of mild cognitive impairment and Alzheimer’s disease

Abstract

AbstractBackgroundElectroencephalogram (EEG) has emerged as a non‐invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer’s disease (AD). However, the effectiveness of EEG in the precise diagnosis and prediction of AD and its preclinical stage, mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD.MethodA total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting‐state EEG recordings for a three‐level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of predictive models by combining participants’ EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for predicting the disease progression and individual’s cognitive function.ResultThe identified EEG biomarkers achieved over 70% accuracy in the three‐level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD‐linked neurodegeneration on EEG metrics were localized at parieto‐occipital regions. Noteworthy, the absolute theta power of channel O2 was negatively correlated with Aβ42 (r = ‐0.358, p < 0.001) and positively correlated with p‐tau (r = 0.442, p < 0.001). In terms of cognitive assessment, the Hjorth mobility of channel O1, O2 and P4 were found to be positively associated with MMSE and MoCA scores (r = 0.416‐0.464, all p < 0.001). In the predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction.ConclusionOur study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.