Brain–phenotype models fail for individuals who defy sample stereotypes

Abigail S. Greene (), Xilin Shen, Stephanie Noble, Corey Horien, C. Alice Hahn, Jagriti Arora, Fuyuze Tokoglu, Marisa N. Spann, Carmen I. Carrión, Daniel S. Barron, Gerard Sanacora, Vinod H. Srihari, Scott W. Woods, Dustin Scheinost and R. Todd Constable ()
Additional contact information
Abigail S. Greene: Yale School of Medicine
Xilin Shen: Yale School of Medicine
Stephanie Noble: Yale School of Medicine
Corey Horien: Yale School of Medicine
C. Alice Hahn: Yale School of Medicine
Jagriti Arora: Yale School of Medicine
Fuyuze Tokoglu: Yale School of Medicine
Marisa N. Spann: Columbia University Irving Medical Center
Carmen I. Carrión: Yale School of Medicine
Daniel S. Barron: University of Washington
Gerard Sanacora: Yale School of Medicine
Vinod H. Srihari: Yale School of Medicine
Scott W. Woods: Yale School of Medicine
Dustin Scheinost: Yale School of Medicine
R. Todd Constable: Yale School of Medicine

Nature, 2022, vol. 609, issue 7925, 109-118

Abstract: Abstract Individual differences in brain functional organization track a range of traits, symptoms and behaviours1–12. So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain–phenotype relationships. To this end, here we related brain activity to phenotype using predictive models—trained and tested on independent data to ensure generalizability15—and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18–20 on the interpretation and utility of resulting brain–phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Date: 2022
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DOI: 10.1038/s41586-022-05118-w

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