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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Autores principales:	Kaustubh Supekar, Vinod Menon, Daniel Rubin, Mark Musen, Michael D Greicius
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f575cb835a4c486babf27c943a4dedb4
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spelling	oai:doaj.org-article:f575cb835a4c486babf27c943a4dedb42021-11-25T05:41:15ZNetwork analysis of intrinsic functional brain connectivity in Alzheimer's disease.1553-734X1553-735810.1371/journal.pcbi.1000100https://doaj.org/article/f575cb835a4c486babf27c943a4dedb42008-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18584043/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Functional 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.Kaustubh SupekarVinod MenonDaniel RubinMark MusenMichael D GreiciusPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 6, p e1000100 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kaustubh Supekar Vinod Menon Daniel Rubin Mark Musen Michael D Greicius Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
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.
format	article
author	Kaustubh Supekar Vinod Menon Daniel Rubin Mark Musen Michael D Greicius
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)
publishDate	2008
url	https://doaj.org/article/f575cb835a4c486babf27c943a4dedb4
work_keys_str_mv	AT kaustubhsupekar networkanalysisofintrinsicfunctionalbrainconnectivityinalzheimersdisease AT vinodmenon networkanalysisofintrinsicfunctionalbrainconnectivityinalzheimersdisease AT danielrubin networkanalysisofintrinsicfunctionalbrainconnectivityinalzheimersdisease AT markmusen networkanalysisofintrinsicfunctionalbrainconnectivityinalzheimersdisease AT michaeldgreicius networkanalysisofintrinsicfunctionalbrainconnectivityinalzheimersdisease
_version_	1718414554401079296

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

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