The subcortical structure borders were plotted by freeview visualization tools (part of FreeSurfer package) and compared against the brain regions. In case of discrepancy, they were corrected manually. A separate mask was generated for each and every segmented subcortical region and parcellated cortical region. These masks were transferred to the T1 native space
using nearest-neighbor interpolation. The transformation matrix was obtained by registering Inhibitors,research,lifescience,medical the subject’s head from FreeSurfer space to native space by FMRIB software library (FSL) linear registration tool (http://fsl.fmrib.ox.ac.uk/fsl/flirt/) with 6 degree of freedom (df), rigid-body, 256 bins normalized mutual information cost function, and trilinear interpolation. Quality check was inhibitor ARQ197 performed by overlaying the masks on top of the T1 image in the subject’s native space. No discrepancy was found at this stage. Resting BOLD fMRI preprocessing Inhibitors,research,lifescience,medical The 6:1 slice interleaving of Philips scanner was corrected using Sinc interpolation using SPM8 software package. Our MRI protocol did not include bias field map acquisition, thus we could not correct for B0 field inhomogeneity correction. However, correlations in temporal selleck inhibitor signals are not altered with the mean of the signals, therefore the effect of B0 field inhomogeneity Inhibitors,research,lifescience,medical in the Inhibitors,research,lifescience,medical absence of spatial smoothing is negligible.
It should be emphasized that spatial smoothing is not required in fMRI data analysis in native space. However, this does not rule out the effect of B0 field inhomogeneity in the intermodal registration of fMRI and T1 scans. This will be discussed next in the next section. There have been many reports of motion-induced correlation between ROIs in resting-state BOLD fMRI data (Birn et al. 2006; Power et al. 2012; Van Dijk et al. 2012; Carp 2013), so extra caution was taken in this study to deal with this issue (see Fig. 3). We used mcflirt
(motion correction tools Inhibitors,research,lifescience,medical in the FSL package [Jenkinson et al. 2012]) to Brefeldin_A register all the volumes to a reference image (Jenkinson et al. 2002). The reference image was generated by registering (6 df, 256 bins mutual information, and Sinc interpolation) all volumes to the middle volume and averaging them. We made sure that the selected middle volume was free of artifacts and motion by examining the derivative of the transformation parameters around that volume. We then used the method described in Power et al. (2012) to calculate frame-wise displacement (FD) from the six motion parameters and root mean square difference (RMSD) of the bold percentage signal in the consecutive volumes for every subject. To be more conservative, we lowered the threshold of our RMSD to 0.3%. (It was originally suggested to be 0.5%.