Population Modeling with Machine Learning can Enhance Measures of Mental Health - Open-Data Replication

Authors

Ty Easley, Washington University School of Medicine in St. Louis
Ruiqi Chen, Washington University in St. Louis
Kayla Hannon, Washington University School of Medicine in St. Louis
Rosie Dutt, Washington University School of Medicine in St. LouisFollow
Janine Bijsterbosch, Washington University School of Medicine in St. Louis

Document Type

Article

Publication Date

6-1-2023

Publication Title

Neuroimage: Reports

Publication Title

Neuroimage: Reports

Volume

3

Issue

2

Abstract

Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotype's distribution through undersampling of majority scores, and identified data-driven subtypes to investigate the impact of between-participant heterogeneity. Our results replicated prior results from Dadi et al. (2021) in a larger sample. Each data manipulation further led to small but consistent improvements in prediction accuracy, which were largely additive when combining multiple data manipulations. Combining data manipulations (i.e., extended fMRI features, averaged target phenotype, balanced target phenotype distribution) led to a three-fold increase in prediction accuracy for fluid intelligence compared to prior work. These findings highlight the benefit of several relatively easy and low-cost data manipulations, which may positively impact future work.

Comments

Archived as published.

Recommended Citation

Easley, Ty; Chen, Ruiqi; Hannon, Kayla; Dutt, Rosie; and Bijsterbosch, Janine, "Population Modeling with Machine Learning can Enhance Measures of Mental Health - Open-Data Replication" (2023). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/48

Digital Object Identifier (DOI)

10.1016/j.ynirp.2023.100163

Rights

© 2023 Published by Elsevier Inc.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.