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Statistical Analysis of Fiber Bundles using Multi-tensor Tractography: Application to First Episode Schizophrenia

1Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
3Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4VA Clinical Neuroscience Division, Brockton, MA, USA.
5Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Elsevier Science
Publication Date:
Magn Reson Imaging
Volume Number:
Issue Number:
Magn Reson Imaging. 2011 May;29(4):507-15.
PubMed ID:
Two-tensor tractography, Kernel methods, First Episode Schizophrenia
Appears in Collections:
P50 MH080272/MH/NIMH NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH050740/MH/NIMH NIH HHS/United States
R01 MH052807/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
U54 GM072977/GM/NIGMS NIH HHS/United States
Generated Citation:
Rathi Y., Kubicki M., Bouix S., Westin C-F., Goldstein J., Seidman L.J., Mesholam-Gately R.I., McCarley R.W., Shenton M.E. Statistical Analysis of Fiber Bundles using Multi-tensor Tractography: Application to First Episode Schizophrenia. Magn Reson Imaging. 2011 May;29(4):507-15. PMID: 21277725. PMCID: PMC3078978.
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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).

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