Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching.

ABSTRACT: Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI

SUBMITTER: Wulan N 

PROVIDER: S-EPMC10802307 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature