Preprocessing of the diffusion data was performed using fsl-5.0.9 (FSL; Data were corrected for magnetic susceptibility distortions, motion, and eddy currents using the fugue and eddy packets in FSL. FSL’s brain extraction tool was used to skull-strip each participant’s diffusion-weighted and field map magnitude images. The B0 image was stripped from the diffusion-weighted image, and the field map was prepared using FSL’s field map preparation tool. Smoothing and regularization were performed using FSL’s fugue tool (, and three-dimensional (3D) Gaussian smoothing was applied using sigma = 4 mm (25). The magnitude image was warped on the basis of this smoothing, with y as the warp direction, following a previous study (26). Eddy current correction was performed using FSL’s eddy_correct tool, which takes each volume of the diffusion-weighted image and registers it to the B0 image to correct for both eddy currents and motion. Next, the deformed magnitude image was registered to the B0 image using FSL’s linear image registration tool. The resulting transformation matrix was then applied to the prepared field map. Last, distortions were removed from the diffusion-weighted image using the registered field map, with FSL’s fugue tool. Intensity correction was applied to this unwarping. Upon completion of preprocessing, FSL’s DTIFIT tool was used to reconstruct the diffusion tensors. DTIFIT uses linear regression to fit a diffusion tensor model at each voxel of the preprocessed diffusion image; this results in FA, mean diffusivity, radial diffusivity, and axial diffusivity maps for each participant. Group analyses were carried out in specific ROIs (see below) by nonlinearly registering each participant’s native-space FA map to Montreal Neurological Institute (MNI) space ( Following prior studies in the literature (21), we used FA values for hypothesis testing.

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