Journal
BRAIN RESEARCH
Volume 1669, Issue -, Pages 131-140Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.brainres.2017.06.013
Keywords
Diffusion tensor imaging; Major depressive disorder; TBSS; ANTs; DTI fitting algorithms
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Funding
- Warren Foundation
- Swedish Research Council
- Region Ostergotland
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Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxelwise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTS) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTS TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxelbased correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used. (C) 2017 Elsevier B.V. All rights reserved.
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