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'...

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Main Authors: Kaustubh Supekar, Vinod Menon, Daniel Rubin, Mark Musen, Michael D Greicius
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2008-06-01
Series:PLoS Computational Biology
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18584043/?tool=EBI
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spelling doaj-f575cb835a4c486babf27c943a4dedb42021-04-21T15:20:31ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582008-06-0146e100010010.1371/journal.pcbi.1000100Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.Kaustubh SupekarVinod MenonDaniel RubinMark MusenMichael D GreiciusFunctional 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18584043/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Kaustubh Supekar
Vinod Menon
Daniel Rubin
Mark Musen
Michael D Greicius
spellingShingle Kaustubh Supekar
Vinod Menon
Daniel Rubin
Mark Musen
Michael D Greicius
Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
PLoS Computational Biology
author_facet Kaustubh Supekar
Vinod Menon
Daniel Rubin
Mark Musen
Michael D Greicius
author_sort Kaustubh Supekar
title Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
title_short Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
title_full Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
title_fullStr Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
title_full_unstemmed Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
title_sort network analysis of intrinsic functional brain connectivity in alzheimer's disease.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2008-06-01
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.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18584043/?tool=EBI
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