Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Abstract

Background Electroencephalogram (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 assessment of AD and its preclinical stage, amnestic 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. Methods A 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 models by combining participants’ EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual’s cognitive function. Results The 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. In the cross-validation 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. Conclusions Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

“…The new biomarkers introduced here reflect how functional connectivity is changing in both the time and frequency domains across the EEG‐based regions of interest (ROIs) in HC and MCI. Our analysis indicates that these biomarkers are closely related to the resting‐state EEG biomarkers for AD as identified in most consistent findings 10–14 …”

“…As can be seen, RP, MP, LT, and Occ were the ROI regions that appeared most frequently in the features, indicating that these regions play significant roles in identifying the differences in resting‐state brain connectivity between HC and MCI. These brain regions are exactly those most often cited as involved in MCI and AD deterioration 11,14 . It also should be noted that EEG electrodes do reflect surrounding area activities to some degree.…”

“…Our analysis indicates that these biomarkers are closely related to the resting-state EEG biomarkers for AD as identified in most consistent findings. [10][11][12][13][14] Second, unlike existing work which generally relies only on one detection approach, [19][20][21]23,24 our soft discrimination of HC and MCI is obtained through weighted majority voting of a selected group of reliable discrimination approaches. This combination of diversified approaches takes the discrepancies between HC and MCI from different perspectives into consideration, and greatly improves the accuracy, stability, and reliability of the proposed model.…”

“…Developing economically viable assessment tools and biomarkers that are highly sensitive to cognitive decline and neural dysfunction, before frank Alzheimer's disease (AD) pathology, is critical for the of AD and mild cognitive impairment (MCI) has attracted increased research attention. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] The results in the existing literature, however, are mixed and call for newer analytic methods. Notably, in several recent papers, [20][21][22] it was reported that very high accuracy (>96%) may be achieved over modest sample sizes (22-34 participants).…”

“…Due to its high time resolution, accessibility, affordability, and patient acceptance, electroencephalography (EEG)‐based detection of AD and mild cognitive impairment (MCI) has attracted increased research attention 10–25 . The results in the existing literature, however, are mixed and call for newer analytic methods.…”