| pubmed-article:18584043 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C0002395 | lld:lifeskim |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C0006104 | lld:lifeskim |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C0205102 | lld:lifeskim |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C0205245 | lld:lifeskim |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C0868995 | lld:lifeskim |
| pubmed-article:18584043 | lifeskim:mentions | umls-concept:C1707489 | lld:lifeskim |
| pubmed-article:18584043 | pubmed:issue | 6 | lld:pubmed |
| pubmed-article:18584043 | pubmed:dateCreated | 2008-6-27 | lld:pubmed |
| pubmed-article:18584043 | 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. | lld:pubmed |
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| pubmed-article:18584043 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18584043 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18584043 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18584043 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18584043 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:18584043 | pubmed:issn | 1553-7358 | lld:pubmed |
| pubmed-article:18584043 | pubmed:author | pubmed-author:MenonVinodV | lld:pubmed |
| pubmed-article:18584043 | pubmed:author | pubmed-author:GreiciusMicha... | lld:pubmed |
| pubmed-article:18584043 | pubmed:author | pubmed-author:RubinDanielD | lld:pubmed |
| pubmed-article:18584043 | pubmed:author | pubmed-author:SupekarKaustu... | lld:pubmed |
| pubmed-article:18584043 | pubmed:author | pubmed-author:MusenMarkM | lld:pubmed |
| pubmed-article:18584043 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:18584043 | pubmed:volume | 4 | lld:pubmed |
| pubmed-article:18584043 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18584043 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18584043 | pubmed:pagination | e1000100 | lld:pubmed |
| pubmed-article:18584043 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
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| pubmed-article:18584043 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18584043 | pubmed:articleTitle | Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. | lld:pubmed |
| pubmed-article:18584043 | pubmed:affiliation | Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA. | lld:pubmed |
| pubmed-article:18584043 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18584043 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:18584043 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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