Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

• DOI: 10.1371/journal.pcbi.1000100
• Publication Date: 2008-06-26T20:44:30Z
• Authors: Kaustubh Supekar; Vinod Menon; Daniel Rubin; Mark Musen; Michael D. Greicius
• Keywords: Clustering coefficient; Small-world network; Human brain; Power graph analysis

Functional brain networks detected in task-free ("resting-state") functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects robust organization of the brain. Here, we examined whether this is disrupted Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to compute frequency-dependent correlation matrices. Correlation matrices thresholded create 90-node undirected-graphs networks. Small-world metrics (characteristic path length clustering coefficient) computed using graph analytical methods. In low frequency interval 0.01 0.05 Hz, showed activity, characterized by high coefficient characteristic length. contrast, loss properties, significantly lower (p<0.01), indicative local connectivity. Clustering coefficients for left right hippocampus (p<0.01) group compared control group. Furthermore, distinguished participants with sensitivity 72% specificity 78%. Our study provides new evidence there AD. can characterize AD, our findings further suggest these network measures may be useful as an imaging-based biomarker distinguish healthy aging.