Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
Functional brain networks detected in task-free ("resting-state") functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.
Supekar
Kaustubh
K
Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA.
Menon
Vinod
V
Rubin
Daniel
D
Musen
Mark
M
Greicius
Michael D
MD
D000328:Adult; D000368:Aged; D000544:Alzheimer Disease / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D009434:Neural Pathways / Q000503:physiopathology*