Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis

Area of Science:

• Neuroscience
• Neuroimaging
• Cognitive Science

Background:

• Alzheimer's disease (AD) is a progressive neurodegenerative disorder.
• Mild cognitive impairment (MCI) represents a transitional phase from normal aging to dementia.
• Early detection of MCI is crucial for potentially slowing AD progression.

Purpose of the Study:

• To investigate abnormal dynamic brain activity in AD and MCI using resting-state functional magnetic resonance imaging (rs-fMRI).
• To identify differences in brain network dynamics between AD, MCI, and cognitively normal (CN) individuals.
• To correlate dynamic brain activity patterns with cognitive function.

Main Methods:

• Leading Eigenvector Dynamics Analysis (LEiDA) applied to rs-fMRI data.
• Identification of repetitive brain activity states based on phase coherence.
• Analysis of intergroup differences in dynamic activity indicators (occurrence probability, lifetime).
• Correlation analysis with neurobehavioral scale scores.

Main Results:

• Decreased global synchronization in the globally synchronized state for AD and MCI groups.
• Significant differences in limbic region activity states between AD and other groups.
• Increased default and visual region activity states in AD and MCI compared to CN.
• Poorer cognitive function correlated with reduced global synchronization and increased limbic/visual activity.

Conclusions:

• AD and MCI exhibit distinct abnormal dynamic activity patterns in resting-state brain networks.
• LEiDA provides novel insights into the spatiotemporal dynamics of brain networks in neurodegenerative diseases.
• Findings contribute to a deeper understanding of abnormal brain network activity in AD and MCI.