Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

ABSTRACT: In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demograp

SUBMITTER: Omidvarnia A 

PROVIDER: S-EPMC11208538 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature