Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data

Kathleen Miley, Martin Michalowski, Fang Yu, Ethan Leng, Barbara J. McMorris, Sophia Vinogradov

Research output: Contribution to journalArticlepeer-review

Abstract

Poor social functioning is an emerging public health problem associated with physical and mental health consequences. Developing prognostic tools is critical to identify individuals at risk for poor social functioning and guide interventions. We aimed to inform prediction models of social functioning by evaluating models relying on bio-behavioral data using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22–35, N = 1,101), we built Support Vector Regression models to estimate social functioning from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Predictive accuracy of each model was assessed and the importance of individual variables for model performance was determined. The combined and cognition-behavioral models significantly predicted social functioning, whereas the brain-only and brain-cognition models did not. Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal regions were the most important predictors in the combined model. Results demonstrate that social functioning can be accurately predicted using machine learning methods. Behavioral markers may be more significant predictors of social functioning than brain measures for healthy young adults and may represent important leverage points for preventative intervention.

Original languageEnglish (US)
Pages (from-to)414-427
Number of pages14
JournalSocial neuroscience
Volume17
Issue number5
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported by National Institute of Mental Health Grants 1F31MH124278 (KM), 1R01MH120589-01 and 5P50MH119569-02, and the National Institutes of Health’s National Center for Advancing Translational Sciences, Grants TL1R002493 and UL1TR002494 (KM, EL). Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Funding Information:
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Human Connectome Project
  • artificial intelligence
  • functional outcome
  • multivariate pattern analysis
  • neuroimaging biomakers
  • support vector regression

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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