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Network analysis of intrinsic functional brain connectivity in Alzheimer's disease

PLoS computational biology, 2008-06, Vol.4 (6), p.e1000100-e1000100 [Peer Reviewed Journal]

COPYRIGHT 2008 Public Library of Science ;Supekar et al. 2008 ;2008 Supekar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Supekar K, Menon V, Rubin D, Musen M, Greicius MD (2008) Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease. PLoS Comput Biol 4(6): e1000100. doi:10.1371/journal.pcbi.1000100 ;ISSN: 1553-7358 ;ISSN: 1553-734X ;EISSN: 1553-7358 ;DOI: 10.1371/journal.pcbi.1000100 ;PMID: 18584043

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Title:
Network analysis of intrinsic functional brain connectivity in Alzheimer's disease
Author: Supekar, Kaustubh ; Menon, Vinod ; Rubin, Daniel ; Musen, Mark ; Greicius, Michael D
Sporns, Olaf
Subjects: Adult ; Aged ; Alzheimer Disease - physiopathology ; Alzheimer's disease ; Biological markers ; Brain ; Brain - physiopathology ; Brain Mapping ; Computer Science/Systems and Control Theory ; Computer Simulation ; Diagnosis ; Female ; Health aspects ; Humans ; Magnetic Resonance Imaging ; Male ; Mathematics/Statistics ; Middle Aged ; Models, Neurological ; Nerve Net - physiopathology ; Neural networks ; Neural Pathways - physiopathology ; Neurological Disorders/Alzheimer Disease ; Neurology ; NMR ; Nuclear magnetic resonance ; Physiological aspects ; Radiology and Medical Imaging/Magnetic Resonance Imaging
Is Part Of: PLoS computational biology, 2008-06, Vol.4 (6), p.e1000100-e1000100
Description: 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.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1553-7358
ISSN: 1553-734X
EISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1000100
PMID: 18584043
Source: PLoS (Open access)
Geneva Foundation Free Medical Journals at publisher websites
MEDLINE
PubMed Central
ProQuest Central
DOAJ Directory of Open Access Journals

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Network analysis of intrinsic functional brain connectivity in Alzheimer's disease

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