Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep Learning.

ABSTRACT: Task-based functional magnetic resonance imaging (tb-fMRI) reveals individual differences in the neural basis of cognitive functions by linking specific tasks to neural responses. However, scaling tb-fMRI to population-level studies is challenging due to its cognitive demands, variations in task design across studies, and the limited scope of tasks in large datasets. To address this, we propose DeepTaskGen, a deep-learning approach that generates non-acquired task-based contrast maps from resting-state fMRI (rs-fMRI) data. Our approach enables generating synthetic task images for non-acquired tasks within the study protocol. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals

SUBMITTER: Serin E 

PROVIDER: S-EPMC12248144 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature