10.5061/DRYAD.7M70N
Kwak, Seyul
Seoul National University
Joo, Won-tak
University of Wisconsin-Madison
Youm, Yoosik
Yonsei University
Chey, Jeanyung
Seoul National University
Data from: Social brain volume is associated with in-degree social network
size among older adults
Dryad
dataset
2018
VBM
2017
2015
2014
Social brain hypothesis
gray matter volume
in-degree size
2018-01-08T20:42:14Z
2018-01-08T20:42:14Z
en
https://doi.org/10.1098/rspb.2017.2708
399580508 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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)
South Korea