Performance reserves in brain-imaging-based phenotype prediction.

ABSTRACT: This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype predictio

SUBMITTER: Schulz MA 

PROVIDER: S-EPMC11215805 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature