Effective workflow from multimodal MRI data to model-based prediction.

ABSTRACT: Predicting human behavior from neuroimaging data remains a complex challenge in neuroscience. To address this, we propose a systematic and multi-faceted framework that incorporates a model-based workflow using dynamical brain models. This approach utilizes multi-modal MRI data for brain modeling and applies the optimized modeling outcome to machine learning. We demonstrate the performance of such an approach through several examples such as sex classification and prediction of cognition or personality traits. We in particular show that incorporating the simulated data into machine learning can significantly improve the prediction performance compared to using empirical features alone. These results suggest considering the output of the dynamical brain models as an additional neuroimaging d

SUBMITTER: Jung K 

PROVIDER: S-EPMC12181387 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature