No strong evidence that social network index is associated with gray matter volume from a data-driven investigation
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.
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.Attached Files
Submitted - 2019.12.19.883173v1.full.pdf
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Additional details
- PMCID
- PMC7774327
- Eprint ID
- 100446
- Resolver ID
- CaltechAUTHORS:20200102-093452970
- NIH
- 2P50MH094258
- Created
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2020-01-02Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field
- Caltech groups
- Tianqiao and Chrissy Chen Institute for Neuroscience, Division of Biology and Biological Engineering