TY  - JOUR
AU  - He, Tong
AU  - An, Lijun
AU  - Chen, Pansheng
AU  - Chen, Jianzhong
AU  - Feng, Jiashi
AU  - Bzdok, Danilo
AU  - Holmes, Avram J.
AU  - Eickhoff, Simon
AU  - Yeo, B. T. Thomas
TI  - Meta-matching as a simple framework to translate phenotypic predictive models from big to small data
JO  - Nature neuroscience
VL  - 25
IS  - 1
SN  - 1097-6256
CY  - New York, NY
PB  - Nature America
M1  - FZJ-2022-02235
SP  - 795-804
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:35578132
UR  - <Go to ISI:>//WOS:000799439200001
DO  - DOI:10.1038/s41593-022-01059-9
UR  - https://juser.fz-juelich.de/record/907824
ER  -