Entropy and Complexity Analyses in Alzheimer's Disease: An MEG Study

Area of Science:

• Neuroscience
• Biomedical Engineering
• Medical Diagnostics

Background:

• Alzheimer's disease (AD) is a leading cause of dementia in the elderly, characterized by neural loss, neurofibrillary tangles, and senile plaques.
• Early and accurate diagnosis of AD is crucial for timely intervention and management.

Purpose of the Study:

• To analyze magnetoencephalogram (MEG) background activity in AD patients and elderly controls.
• To evaluate the utility of entropy and complexity measures for differentiating AD patients from healthy individuals.

Main Methods:

• MEG recordings from 36 AD patients and 26 controls were analyzed.
• Six measures were employed: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi's fractal dimension (HFD), Maragos and Sun's fractal dimension (MSFD), and Lempel-Ziv complexity (LZC).
• Statistical significance was assessed using Welch's t-test with Bonferroni correction, and diagnostic accuracy was determined via receiver operating characteristic (ROC) curves.

Main Results:

• MEG recordings from AD patients exhibited significantly lower complexity and higher regularity compared to control subjects across multiple brain regions.
• Shannon spectral entropy (SSE) demonstrated the highest diagnostic accuracy (77.42%) in distinguishing between AD patients and controls.
• Significant differences were observed for all measures except MSFD (p < 0.05).

Conclusions:

• Entropy and complexity analyses of MEG background activity show promise as a non-invasive tool to support Alzheimer's disease diagnosis.
• The findings highlight the potential of quantifying brain signal dynamics for improved AD detection.