This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.1101/2023.12.05.569848 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-05653 in citations.
Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data
Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data
Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a nec...
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Personal Name(s): | Chen, Pansheng |
---|---|
An, Lijun / Wulan, Naren / Zhang, Chen / Zhang, Shaoshi / Ooi, Leon Qi Rong / Kong, Ru / Chen, Jianzhong / Wu, Jianxiao / Chopra, Sidhant / Bzdok, Danilo / Eickhoff, Simon B / Holmes, Avram J / Yeo, B. T. Thomas (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Imprint: |
2024
|
DOI: |
10.1101/2023.12.05.569848 |
DOI: |
10.34734/FZJ-2023-05653 |
Document Type: |
Preprint |
Research Program: |
Multilevel Brain Organization and Variability |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-05653 in citations.
Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK. |