期刊
MAGNETIC RESONANCE IMAGING
卷 29, 期 4, 页码 507-515出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2010.10.005
关键词
Two-tensor tractography; Kernel methods; First-episode schizophrenia
资金
- Department of Veteran Affairs
- VA Schizophrenia Center
- NIH [1P50MH080272-01, P41 RR13218, R01 MH 52807, R01 MH 50740, R01MH074794]
- NA-MIC (NIH) [U54 GM072977-01]
This work proposes a new method to detect abnormalities in fiber bundles of first-episode (FE) schizophrenia patients. Existing methods have either examined a particular region of interest or used voxel-based morphometry or used tracts generated using the single tensor model for locating statistically different fiber bundles. Further, a two-sample t test, which assumes a Gaussian distribution for each population, is the most widely used statistical hypothesis testing algorithm. In this study, we use the unscented Kalman filter based two-tensor tractography algorithm for tracing neural fiber bundles of the brain that connect 105 different cortical and subcortical regions. Next, fiber bundles with significant connectivity across the entire population were determined. Several diffusion measures derived from the two-tensor model were computed and used as features in the subsequent analysis. For each fiber bundle, an affine-invariant descriptor was computed, thus obviating the need for precise registration of patients to an atlas. A kernel-based statistical hypothesis testing algorithm, which makes no assumption regarding the distribution of the underlying population, was then used to determine the abnormal diffusion properties of all fiber bundles for 20 FE patients and 20 age-matched healthy controls. Of the 1254 fiber bundles with significant connectivity, 740 fiber bundles were found to be significantly different in at least one diffusion measure after correcting for multiple comparisons. Thus, the changes affecting first-episode patients seem to be global in nature (spread throughout the brain). (c) 2011 Elsevier Inc. All rights reserved.
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