Brain dysfunction assessment in Alzheimer's disease: A phase-space projection and interactive signal decomposition...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Neurodegenerative diseases like Alzheimer's Disease (AD) present significant diagnostic challenges.
• Resting-state electroencephalography (EEG) offers a non-invasive method to study brain activity.
• Identifying reliable electrophysiological biomarkers is crucial for early diagnosis and monitoring disease progression.

Purpose of the Study:

• To introduce and evaluate a novel signal processing framework for identifying electrophysiological biomarkers of neurodegenerative diseases using resting-state EEG.
• To quantify distinct patterns of brain dysfunction in Alzheimer's Disease (AD) by analyzing nonlinear signal dynamics and functional connectivity.
• To establish a quantitative measure of local nonlinear signal complexity and its relationship with cognitive impairment in AD.

Main Methods:

• A two-stage analytical method was applied to resting-state EEG data from 65 participants (AD, Frontotemporal Dementia, Healthy Controls).
• Phase-Space Projection (PSP) transformed EEG time-series into deviation signals.
• Interactive Signal Decomposition (ISD) separated deviation signals into oscillatory components and nonlinear residuals.
• Functional connectivity was assessed using the directed Phase Lag Index (dPLI).

Main Results:

• The AD group showed significantly lower functional connectivity compared to the Healthy Control (HC) group.
• Mean residual energy, a measure of signal complexity from ISD, was significantly lower in AD patients.
• A positive correlation was observed between residual energy and Mini-Mental State Examination (MMSE) scores, indicating reduced complexity with greater cognitive impairment.

Conclusions:

• The combined PSP-ISD framework provides a quantitative measure of local nonlinear signal complexity, which is significantly reduced in Alzheimer's Disease.
• This approach, when combined with functional connectivity analysis, characterizes AD neuropathology through network degradation and reduced local signal complexity.
• The framework offers a novel analytical tool for clinical neuroscience, aiding in the characterization of AD pathology.