Structural Brain Imaging Predicts Individual-Level Task Activation Maps Using Deep Learning.

ABSTRACT: Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 591 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test sub

SUBMITTER: Ellis DG 

PROVIDER: S-EPMC10406267 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature