Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Alzheimer's disease (AD) is a leading cause of age-related neurodegeneration.
• Early AD detection is critical due to a rapidly aging global population.
• Assessing brain functional connectivity (FC) via novel techniques offers a potential diagnostic pathway.

Purpose of the Study:

• To evaluate static and dynamic FC using various analytical approaches.
• To investigate FC differences across healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD groups.
• To explore network segregation and integration using graph theory.

Main Methods:

• Utilized a resting-state fMRI dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (n=128).
• Analyzed blood-oxygen-level-dependent (BOLD) signals from 116 brain regions across four participant groups.
• Employed Pearson's correlation, sliding windows analysis (SWA), and point process analysis (PPA) for FC and dynamic FC extraction.

Main Results:

• EMCI group exhibited a longer characteristic path length and decreased degree compared to other groups.
• Increased FC was observed in several regions for LMCI and AD groups relative to HC and EMCI.
• These findings suggest a compensatory mechanism to sustain cognitive function.

Conclusions:

• An elevated FC pattern in LMCI and AD is consistently observed across analyses.
• Point process analysis (PPA) effectively reduced computational load and provided novel dynamic FC findings.
• PPA enhances the identification of subtle dynamic FC alterations in neurodegenerative diseases.