Altered dynamic functional network connectivity patterns in Alzheimer's disease: insights into neural dysfunction.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Dynamic functional network connectivity (dFNC) tracks temporal changes in brain activity during MRI scans.
• Understanding dFNC in Alzheimer's disease (AD) is crucial for deciphering its complex clinical features.
• Limited research currently exists on dFNC alterations in AD patients.

Purpose of the Study:

• To comprehensively characterize dFNC patterns in individuals with Alzheimer's disease (AD).
• To investigate the relationship between dFNC alterations and clinical symptoms in AD.
• To explore the potential of dFNC for classifying AD.

Main Methods:

• Analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data from 100 AD patients and 69 healthy controls (HC).
• Application of a sliding-window approach and k-means clustering with independent component analysis to identify dFNC states.
• Correlation analyses and multivariate pattern analysis to link dFNC with clinical scores and classify AD.

Main Results:

• Four distinct recurrent connectivity states were identified.
• AD patients showed altered dwell and fractional times in specific states (e.g., longer in state III, shorter in state IV).
• State II demonstrated the highest accuracy in classifying AD, characterized by widespread network dysfunction.

Conclusions:

• Significant dFNC alterations are present in Alzheimer's disease.
• dFNC patterns are associated with clinical symptoms and cognitive scores.
• These findings enhance our understanding of AD's underlying pathophysiology.