2.3. Functional connectivity analysis

Processing of rs-fMRI data was performed using the CONN toolbox version 21a (https://web.conn-toolbox.org) (Whitfield-Gabrieli and Nieto-Castanon, 2012) and Statistical Parametric Mapping 12 (SPM12; https://www.fil.ion.ucl.ac.uk/spm).

In the preprocessing steps, T1-weighted images were segmented into grey matter, white matter, and cerebrospinal fluid. The resultant images were then normalized into the Montreal Neurological Institute (MNI) space and resampled to into 1 mm isotropic voxels. The rs-fMRI images were estimated and corrected for subject-level motion and slice timing. The Artifact Detection Tools (ART; https://www.nitrc.org/projects/artifact_detect) was used for detection and scrubbing of outlier volumes, after calculation and z-transformation of the average signal across the entire time series. Outlier volumes were defined when fulfilling either of the following conditions: >5 SD of average intensity deviation from the mean intensity of the session; >0.9 mm framewise displacement. Mean values of head motion were assessed for each subject by CONN. The resultant functional images were then spatially normalized into the MNI space, resampled into 2 mm isotropic voxels, and were finally smoothed with an 8 mm full-width at half-maximum Gaussian kernel.

The anatomical component-based noise correction method (aCompCor) was employed to remove signals from white matter and cerebrospinal fluid as physiological sources of noise (Behzadi et al., 2007). The head motion parameters from motion correction and head motion outlier volumes were regressed out. Then, the residual time series underwent a temporal band pass filtering (0.008–0.09 Hz) to remove other spurious sources of noise.

The seed ROIs for the insular regions were defined as 6 mm radius spheres centered on the anterior and posterior insular regions using the following coordinates based on previous fMRI studies (Cottam et al., 2018, Ichesco et al., 2012, Ichesco et al., 2014, Nicholson et al., 2016, Zhang et al., 2014): left anterior insula (–32, 16, 6), right anterior insula (32, 16, 6), left posterior insula (-39, −15, 1), and right posterior insula (39, −15, 8). The seed ROIs for the cognitive control regions were placed within the dACC and bilateral DLPFC regions centered on the following coordinates based on the previous fMRI study (Dosenbach et al., 2006): dACC (−1, 10, 46), left DLPFC (-43, 22, 34), and right DLPFC (43, 22, 34). Detailed information on the coordinates and locations of the seed ROIs is provided in Fig. 1 and Supplementary Table 1. After averaging the time series of all voxels within each of the ROIs were averaged, and the bivariate correlation coefficients between each pair of ROIs were calculated and converted as z-scores using Fisher’s transformation. Seed-to-seed analysis among all 7 seed ROIs was performed.

Seed regions-of-interest for functional connectivity analysis. The four seed areas are overlaid on the brain template colored as orange for the insular regions (bilateral anterior insulae and bilateral posterior insulae) and green as the cognitive control regions (dorsal anterior cingulate cortex and bilateral dorsolateral prefrontal cortex).

As auxiliary analyses, seed-to-voxel analyses were performed to explore the whole-brain wise functional connectivity of the insular regions. These auxiliary analyses include four seeds including the left anterior insula, right anterior insula, left posterior insula, and right posterior insula.

The four seed areas are overlaid on the brain template colored as orange for the insular regions (bilateral anterior insulae and bilateral posterior insulae) and green as the cognitive control regions (dorsal anterior cingulate cortex and bilateral dorsolateral prefrontal cortex).