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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
6
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pubmed:dateCreated |
2008-6-27
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pubmed:abstractText |
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.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/18584043-10371535,
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http://linkedlifedata.com/resource/pubmed/commentcorrection/18584043-9623998
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:month |
Jun
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pubmed:issn |
1553-7358
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
4
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
e1000100
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pubmed:dateRevised |
2009-11-18
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pubmed:meshHeading |
pubmed-meshheading:18584043-Adult,
pubmed-meshheading:18584043-Aged,
pubmed-meshheading:18584043-Alzheimer Disease,
pubmed-meshheading:18584043-Brain,
pubmed-meshheading:18584043-Brain Mapping,
pubmed-meshheading:18584043-Computer Simulation,
pubmed-meshheading:18584043-Female,
pubmed-meshheading:18584043-Humans,
pubmed-meshheading:18584043-Magnetic Resonance Imaging,
pubmed-meshheading:18584043-Male,
pubmed-meshheading:18584043-Middle Aged,
pubmed-meshheading:18584043-Models, Neurological,
pubmed-meshheading:18584043-Nerve Net,
pubmed-meshheading:18584043-Neural Pathways
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pubmed:year |
2008
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pubmed:articleTitle |
Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.
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pubmed:affiliation |
Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA.
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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