期刊
NEUROIMAGE
卷 45, 期 3, 页码 778-787出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.12.049
关键词
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资金
- NCATS NIH HHS [UL1 TR000454] Funding Source: Medline
- NIBIB NIH HHS [R01 EB002009, R01 EB002009-13] Funding Source: Medline
- NIMH NIH HHS [P50 MH077083-03, P50 MH077083, K23 MH086690, R01 MH073719] Funding Source: Medline
The extension of group-level connectivity methods to individual subjects remains a hurdle for statistical analyses of neuroimaging data. Previous group analyses of positron emission tomography data in clinically depressed patients, for example, have shown that resting-state connectivity prior to therapy predicts how patients eventually respond to pharmacological and cognitive-behavioral therapy. Such applications would be considerably more informative for clinical decision making if these connectivity methods could be extended into the individual subject domain. To test such an extension, 46 treatment-nave depressed patients were enrolled in an fMRI study to model baseline resting-state functional connectivity. Resting-state fMRI scans were acquired and submitted to exploratory structural equation modeling (SEM) to derive the optimal group connectivity model. Jackknife and split sample tests confirm that group model was highly reproducible, and path weights were consistent across the best five group models. When this model was applied to data from individual subjects, 85% of patients fit the group model. Histogram analysis of individual subjects' paths indicate that some paths are better representative of group membership. These results suggest that exploratory SEM is a viable technique for neuroimaging connectivity analyses of individual subjects' resting-state fMRI data. (C) 2008 Elsevier Inc. All rights reserved.
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