| Title: | Meta-matching as a simple framework to translate phenotypic predictive models from big to small data |
| Journal: | Nature Neuroscience |
| Published: | 16 May 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35578132/ |
| DOI: | https://doi.org/10.1038/s41593-022-01059-9 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202200 |
| Citations: | 50 (40 in last 2 years) as of 8 Aug 2024 |
Publication 12372
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Abstract
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.</p>
5 Keywords
- Brain
- Connectome
- Humans
- Magnetic Resonance Imaging
- Phenotype
9 Authors
- Tong He
- Lijun An
- Pansheng Chen
- Jianzhong Chen
- Jiashi Feng
- Danilo Bzdok
- Avram J. Holmes
- Simon B. Eickhoff
- B. T. Thomas Yeo
1 Application
| Application ID | Title |
|---|---|
| 25163 | Structural genetic contributions brain function and behavior |
Enabling scientific discoveries that improve human health