Data Processing

The DTI data were preprocessed using PANDA¹ (25) in Matlab including the following steps: converting DICOM files into NIfTI images, estimating the brain mask, cropping the raw images, correcting for the eddy current effect, correcting for head motions, estimating the diffusion tensor models by using the linear least-squares fitting method on each voxel, tracking whole-brain fiber in the native diffusion space via Fiber Assignment by using the Continuous Tracking algorithm, and averaging multiple acquisitions and calculating diffusion tensor metrics.

The fMRI data were preprocessed using SPM8² and the GRETNA toolbox³ (26). The preprocessing steps included removal of volumes, slice timing correction, realignment, spatial normalization, and temporal filtering as follows. The first 10 volumes of each subject were removed to ensure magnetization equilibrium. The remaining volumes were then executed for slice timing correction based on the middle slice and then realigned for head motion correction. Two patients were excluded from further calculations due to head motion >2 mm or head rotations <2°. For group average and group comparison purposes, the data were spatially normalized to the standard Montreal Neurological Institute space and resampled with a resolution of 3 mm × 3 mm × 3 mm. Subsequently, signals were typically band-pass (0.01–0.08 Hz) filtered to reduce the effects of low-frequency drift and high-frequency physiological noise (27). Finally, confounding variables, including six head motion parameters, averaged global and white matter signals, and cerebrospinal fluid regressed out.