Statistical analysis of fiber bundles using multi-tensor tractography: application to first-episode schizophrenia

Abstract

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 (ROI) or used voxel based morphometry (VBM) 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, that 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 firstepisode patients seem to be global in nature (spread throughout the brain).