Data from: Social brain volume is associated with in-degree social network size among older adults

Kwak, Seyul, Seoul National University

Joo, Won-tak, University of Wisconsin-Madison

Youm, Yoosik, Yonsei University

Chey, Jeanyung, Seoul National University

Published Jan 08, 2018 on Dryad. https://doi.org/10.5061/dryad.7m70n

Cite this dataset

Kwak, Seyul; Joo, Won-tak; Youm, Yoosik; Chey, Jeanyung (2018). Data from: Social brain volume is associated with in-degree social network size among older adults [Dataset]. Dryad. https://doi.org/10.5061/dryad.7m70n

Abstract

The social brain hypothesis proposes that large neocortex size evolved to support cognitively demanding social interactions. Accordingly, previous studies have observed that larger orbitofrontal and amygdala structures predict the size of an individual's social network. However, it remains uncertain how an individual's social connectedness reported by other people is associated with the social brain volume. In this study, we found that a greater in-degree network size, a measure of social ties identified by a subject's social connections rather than by the subject, significantly correlated with a larger regional volume of the orbitofrontal cortex, dorsomedial prefrontal cortex and lingual gyrus. By contrast, out-degree size, which is based on an individual's self-perceived connectedness, showed no associations. Meta-analytic reverse inference further revealed that regional volume pattern of in-degree size was specifically involved in social inference ability. These findings were possible because our dataset contained the social networks of an entire village, i.e. a global network. The results suggest that the in-degree aspect of social network size not only confirms the previously reported brain correlates of the social network but also shows an association in brain regions involved in the ability to infer other people's minds. This study provides insight into understanding how the social brain is uniquely associated with sociocentric measures derived from a global network.

Usage notes

KSHAP MRI data

MRI images were acquired using a 3-Tesla MAGNETOM Trio 32 channel coil. Whole-brain T1-weighted images were reconstructed from 224 sagittal slices of 1 mm thickness using an MPRAGE sequence with the following parameters: TR = 2.3 s, TE = 2.3 ms, FOV = 256 × 256 mm2, and FA = 9°. The time between social network measurement and MRI acquisition was 16–21 months. Image preprocessing was carried out using tools implemented in Statistical Parametric Mapping software (SPM12; Wellcome Department of Imaging Neuroscience, London, UK) and executed in Matlab (MathWorks, Natick, Massachusetts). We used the New Segmentation algorithm implemented in SPM12 [43]. T1 images were bias-corrected and segmented into five tissue classes based on non-linearly registered tissue probability maps. The East Asian International Consortium for Brain Mapping template was used for local optimization affine regularization. In order to spatially normalize gray matter images into a standard space with enhanced accuracy of inter-subject registration [44,45], we used Diffeomorphic Anatomical Registration Exponentiated Lie algebra (DARTEL). A customized template was created from imported versions of the gray matter tissue images. Then, the deformation field was applied to previously segmented gray matter images to implement non-linear transformation into standardized Montreal Neurological Institute (MNI) space. During these non-linear transformations, the total volume of gray matter was preserved with modulated images. All images were smoothed using an 8-mm full-width at half-maximum Gaussian kernel.

kshapmri.zip

kshap0108

Demographic, subject screening data in 3rd wave Korean Social Health Aging Project (KSHAP)

Location

South Korea