MRI preprocessing

Diffusion-weighted data were analyzed with FMRIB’s Diffusion Toolbox of FMRIB Software Library (FSL) Version 5.0.10 [30]. FSL’s Eddy tool was used to correct for head motion, eddy currents, and inter-slice intensity outliers [31, 32]. Further analysis of FA data was based on tract-based spatial statistics [33]. First, individual FA images were created by fitting a tensor model to the raw diffusion data. After brain extraction [34], all participants’ FA images were aligned to a 1 × 1 × 1 mm target FA image (FMRIB58_FA; FMRIB Software Library) using nonlinear registration [35, 36]. Next, the aligned FA images were transformed into the Montreal Neurologic Institute 152 template using affine registrations. A mean FA image across all participants was generated and thinned to create a mean FA skeleton which represents the centers of all tracts common to the sample. We set an FA threshold of 0.3 to form the skeleton, a spatial representation of the majority of major white matter fiber bundles. In the final step, the aligned FA data of all participants were projected onto this skeleton by searching for the local center of the relevant fiber tract. For the region of interest analysis, we calculated a separate mean FA value for the left and right anterior limb of the internal capsule based on the skeletonized FA images (Figure 1). Both regions were defined by masks based on the JHU ICBM-DTI-81 White Matter labels atlas [37].

Study-specific FA skeleton used for analysis. Green represents the mean FA skeleton across all participants. Red depicts the left and right mask of the anterior limb of the internal capsule which was used to calculate the mean FA value of the structure. Underlying gray-scale images are the Montreal Neurologic Institute 152-T1 1 mm template.