DOI: 10.5061/dryad.7m70n
title: Data from: Social brain volume is associated with in-degree social network size among older adults

description: 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.
description: KSHAP MRI
 dataMRI 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.zipkshap0108Demographic, subject screening data in 3rd wave Korean Social Health Aging Project (KSHAP)