Association between resting-state functional connectivity and empathizing/systemizing

Abstract

Empathizing is the drive to identify the mental status of other individuals and respond to it with an appropriate emotion; systemizing is the drive to analyze a system. Previously, we have shown that structures associated with the default mode network (DMN) and external attention system (EAS) are associated with empathizing and systemizing, respectively. Here we investigated the association between resting-state functional connectivity (RSFC) and empathizing/systemizing in 248 healthy young adults. We considered the medial prefrontal cortex (mPFC) and bilateral dorsolateral prefrontal cortices (DLPFCs), which are key nodes of DMN and EAS, as seed regions, and investigated correlations across subjects between individual empathizing/systemizing and RSFC between each seed region and other brain regions. We found that higher empathizing was associated with larger RSFC between the mPFC and areas in (a) the dorsal anterior cingulate cortex (dACC), (b) precuneus, and (c) left superior temporal sulcus (STS). Furthermore, there was an interaction effect between sex and systemizing on RSFC between the left DLPFC and dACC: males showed positive correlations between this RSFC and systemizing, whereas females showed the opposite tendency. Thus, empathizing was associated with increased positive functional coupling with the key node and other nodes of DMN, as well as the area associated with feeling another's pain. Systemizing was associated with increased positive functional coupling between the key nodes of EAS in males. These findings provide further support for the concept of an association between DMN/EAS and empathizing/systemizing.

Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference

“…This skin-skull-stripping procedure was performed so that these parts were not treated as the outer edge of the brain parenchyma in the preprocessing procedures. Furthermore, the skull-stripped BOLD image was coregistered to a previously created custom made skull-stripped EPI template (Takeuchi et al, 2014b). The series of BOLD images for each session for each subject were slice timing corrected and realigned using DPARSF.…”

“…The series of BOLD images for each session for each subject were slice timing corrected and realigned using DPARSF. The series of BOLD images for each subject were segmented and normalized using the previously described method (Takeuchi et al, 2014b) that modified the diffeomorphic anatomical Table 1 The average, range, and SD of age, Raven's Advanced Progressive Matrix score, reverse Stroop interference rate, Stroop interference rate, volume level framewise displacement, and maximum movement from the origin in each direction among the subjects of this study. registration through exponentiated lie algebra (DARTEL) (Ashburner, 2007) to give images with 3.75 × 3.75 × 3.75 mm 3 voxels.…”

“…In this process, a custom template was also created (Takeuchi et al, 2014b), and the whole brain mask, which consists of voxels that show gray matter tissue probability + white matter tissue probability + cerebrospinal fluid (CSF) tissue probability, N0.1, the mask of the areas that are likely to be white matter (white matter tissue probability N0.99, to avoid contamination of signals from white matter), the mask of the areas that are strongly likely to be CSF (CSF tissue probability N 0.99, to avoid the contamination of signals from other tissues) were created from the custom template. The basis for not using T1-weighted structural images for the abovementioned normalization procedure has been previously described (Takeuchi et al, 2014b). This is merely because the shape of these images is apparently different from that of BOLD images (see extensive explanations for this problem, Takeuchi et al, 2011a).…”

“…The analyses were performed using this software. Analyses were performed with sex, age, Raven's Advanced Progressive Matrix score, volume-level mean framewise displacement that is calculated according to the previous study (Power et al, 2012) and performance of the control task for each interference (number of correct answers on the Word-Color task for the analysis of reverse Stroop interference and the number of correct answers on the Color-Word task for the analysis of Stroop interference) the signal intensities of preprocessed regional gray matter density (rGMD) at each voxel (for details of the inducement of this measure, see Takeuchi et al, 2014b), as additional covariates. The movement related parameter was used due to the concern for the effect of movement during the scan on resting-state fMRI measures (Fair et al, 2012).…”

“…However, for the interest of readers, we tested the interaction effects between Stroop interference and sex on fALFF/DC and found no significant interactions. We followed the method from a previous study for the construction of the models and contrast (for details, see Takeuchi et al, 2014b). In brief, in these test of interaction effects, we used the function of ANCOVA in SPM8, since BPM cannot be used to model the interaction effects in ANCOVA.…”