Computational protocol: Complex regional pain syndrome: The matter of white matter?

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Protocol publication

[…] The preprocessing of DTI data included motion and eddy‐current correction, and brain extraction using FSL toolkit (; Smith et al., ). The B‐matrices were also rotated (Leemans & Jones, ). A brain mask was created from the diffusion‐weighted image with b‐value of zero, using the FSL's brain extraction tool (Smith, ).Tensors were estimated on the corrected data within the brain mask using FSL's dtifit and the resulting images were converted into DTI‐TK ( format for tensor‐based spatial normalization (Zhang et al., ). First, population‐specific tensor template was bootstrapped using the IXI aging DTI template (Zhang, Yushkevich, Rueckert, & Gee, ). Individual tensor image was then registered to the population‐specific template that was mapped to the IIT (Illinois Institute of Technology) DTI human template (v3; Zhang, Peng, Dawe, & Arfanakis, ) with rigid, affine and finally diffeomorphic registrations. Finally, aligned individual tensor images were wrapped to the population‐specific template in standard space. This tensor‐based normalization has been shown to be superior in detecting white‐matter differences compared with low‐dimensional registration using scalar values, such as FA (Wang et al., ; Zhang et al., ).The FA, MD, AD, and RD maps were reconstructed from the spatially normalized tensor images for each participant. These DTI parameters were compared voxel‐by‐voxel with TBSS (Smith et al., ), as part of FSL. Briefly, the mean FA skeleton image that represents the center of tracts, consistent across subjects, was obtained by thinning the across‐subjects averaged FA image (threshold of 0.2). Then, each subject's aligned FA, MD, AD, and RD images were projected onto the mean FA skeleton.Between‐group comparisons of the resulting skeletonized data were conducted using permutation test (FSL's Randomise v2.1; 10,000 permutations), with multiple comparisons corrected using threshold‐free cluster enhancement method (Smith & Nichols, ). We considered a difference to be statistically significant at a corrected p < .05. Confounding factors of age and scanner were included as covariates. Statistically significant tracts were labeled according to the white‐matter atlas from Johns Hopkins University (JHU ICBM‐DTI‐81 white‐matter labels).Whole‐brain skeletal FA, MD, AD, and RD were calculated by averaging over the FA skeleton. Between‐group comparisons in these values were performed using unpaired two‐tailed two‐sample t tests. Age and scanner were regressed out using multiple linear regression before the comparison analysis.To evaluate the bias caused by head motion, the motion‐correction parameters (absolute displacement and mean absolute translations) were also compared between the two groups using unpaired two‐tailed two‐sample t test. The mean translation values were calculated across all three axes. […]

Pipeline specifications

Software tools BET, DTI-TK
Applications Magnetic resonance imaging, Diffusion magnetic resonance imaging analysis
Organisms Pieris rapae, Homo sapiens