CaltechAUTHORS
  A Caltech Library Service

No strong evidence that social network index is associated with gray matter volume from a data-driven investigation

Lin, Chujun and Keleş, Ümit and Tyszka, J. Michael and Gallo, Marcos and Paul, Lynn and Adolphs, Ralph (2020) No strong evidence that social network index is associated with gray matter volume from a data-driven investigation. Cortex, 125 . pp. 307-317. ISSN 0010-9452. PMCID PMC7774327. https://resolver.caltech.edu/CaltechAUTHORS:20200102-093452970

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.

2MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200102-093452970

Abstract

Recent studies in adult humans have reported correlations between individual differences in people’s Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values ≤ 0.1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.cortex.2020.01.021DOIArticle
https://doi.org/10.1101/2019.12.19.883173DOIDiscussion Paper
https://osf.io/zumwt/?view_only=4f11ca10ed5947c1be1ecdea57cfdff3Related ItemData
ORCID:
AuthorORCID
Lin, Chujun0000-0002-7605-6508
Tyszka, J. Michael0000-0001-9342-9014
Gallo, Marcos0000-0002-8227-2661
Paul, Lynn0000-0002-3128-8313
Adolphs, Ralph0000-0002-8053-9692
Additional Information:© 2020 Elsevier Ltd. Received 1 August 2019, Revised 18 December 2019, Accepted 28 January 2020, Available online 12 February 2020. We thank Tim Armstrong for helping with data collection, and Dorit Kliemann and Julien Dubois for helpful discussion. Funding: This work was supported by a Silvio O. Conte Center from the National Institute of Mental Health (2P50MH094258). Data statement: All data are available at https://osf.io/zumwt/?view_only=4f11ca10ed5947c1be1ecdea57cfdff3. Author contributions: CL carried out preregistration, most data processing except for aspects of the MRI data, and drafted the paper; CL and UK performed all data analysis; JMT carried out MRI data collection, MRI data processing, helped with data analysis, and helped drafting parts of the Methods; MG helped with assembling data, preregistration, and carring out a literature review for the introduction and discussion sections of the paper; LP expanded the Social Network Index to assess modes of communication and types of social support, supervised behavioral data collection, and helped with processing of behavioral data; RA initially conceived of project and helped draft the paper; All authors contributed to intellectual discussions on the project, and all authors participated in revisions to finalize the manuscript.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NIH2P50MH094258
Subject Keywords:social network index, gray matter volume, predictive modeling, cross-validation
PubMed Central ID:PMC7774327
Record Number:CaltechAUTHORS:20200102-093452970
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200102-093452970
Official Citation:Chujun Lin, Umit Keles, J. Michael Tyszka, Marcos Gallo, Lynn Paul, Ralph Adolphs, No strong evidence that social network index is associated with gray matter volume from a data-driven investigation, Cortex, Volume 125, 2020, Pages 307-317, ISSN 0010-9452, https://doi.org/10.1016/j.cortex.2020.01.021. (http://www.sciencedirect.com/science/article/pii/S0010945220300460)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100446
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Jan 2020 19:39
Last Modified:24 Feb 2021 18:28

Repository Staff Only: item control page