Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

We propose a simple framework-meta-matching-to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = -0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching. Individual-level prediction is critical for precision medicine, but many neuroimaging prediction studies are underpowered. Here the authors present a simple yet powerful approach that effectively translates predictive models from big to small data.

Automated summary

This paper presents a simple yet powerful approach to translate predictive models from large-scale datasets to small-scale studies, improving the prediction capability. The results demonstrate that meta-matching can greatly enhance predictions of new phenotypes in small independent datasets in various scenarios.