Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

Abstract

Objectives: The future emergence of disease-modifying treatments for dementia highlights the urgent need to identify reliable and easily accessible tools for diagnosing Alzheimer's disease (AD). Electroencephalography (EEG) is a non-invasive and cost-effective technique commonly used in the study of neurodegenerative disorders. However, the specific alterations in EEG biomarkers associated with AD remain unclear when using a limited number of electrodes.

Methods: We studied pathological characteristics of AD using low-density EEG data collected from 26 AD and 29 healthy controls (HC) during both eye closed (EC) and eye opened (EO) resting conditions. The analysis including power spectrum, phase lock value (PLV), and weighted lag phase index (wPLI) and power-to-power frequency coupling (theta/beta) analysis were applied to extract features in the delta, theta, alpha, and beta bands.

Results: During the EC condition, the AD group exhibited decreased alpha power compared to HC. Additionally, both analysis of PLV and wPLI in the theta band indicated that the alterations in the AD brain network predominantly involved in the frontal region with the opposite changes. Moreover, the AD group had increased frequency coupling in the frontal and central regions. Surprisingly, no group difference was found in the EO condition. Notably, decreased theta band functional connectivity within the fronto-central lobe and increased frequency coupling in frontal region were found in AD group from EC to EO. More importantly, the combination of EC and EO quantitative EEG features improved the inter-group classification accuracy when using support vector machine (SVM) in older adults with AD. These findings highlight the complementary nature of EC and EO conditions in assessing and differentiating AD cohorts.

Conclusion: Our results underscore the potential of utilizing low-density EEG data from resting-state paradigms, combined with machine learning techniques, to improve the identification and classification of AD.