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

Medientyp: E-Artikel

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

Beteiligte: He, Tong [Verfasser:in]; An, Lijun [Verfasser:in]; Chen, Pansheng [Verfasser:in]; Chen, Jianzhong [Verfasser:in]; Feng, Jiashi [Verfasser:in]; Bzdok, Danilo [Verfasser:in]; Holmes, Avram J. [Verfasser:in]; Eickhoff, Simon [Verfasser:in]; Yeo, B. T. Thomas [Verfasser:in]

Erschienen: Nature America, 2022

Sprache: Englisch

DOI: https://doi.org/10.1038/s41593-022-01059-9

ISSN: 1097-6256; 1546-1726

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Beschreibung: 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.

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