%0 Journal Article
%A He, Tong
%A An, Lijun
%A Chen, Pansheng
%A Chen, Jianzhong
%A Feng, Jiashi
%A Bzdok, Danilo
%A Holmes, Avram J.
%A Eickhoff, Simon
%A Yeo, B. T. Thomas
%T Meta-matching as a simple framework to translate phenotypic predictive models from big to small data
%J Nature neuroscience
%V 25
%N 1
%@ 1097-6256
%C New York, NY
%I Nature America
%M FZJ-2022-02235
%P 795-804
%D 2022
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:35578132
%U <Go to ISI:>//WOS:000799439200001
%R 10.1038/s41593-022-01059-9
%U https://juser.fz-juelich.de/record/907824