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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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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