Task-based Functional Connectivity is Sufficient to Capture Individual Differences in Functional Selectivity Across Multiple Cortical Systems.

Most studies of brain networks use resting-state fMRI to characterize the brain's intrinsic functional organization. However, recent work has questioned whether we should rely exclusively on resting-state data to define this organization. Using a connectivity fingerprint modeling framework, we test whether functional connectivity estimated from task fMRI data can predict task selectivity in visual, language, and spatial working memory tasks, at the individual subject level. We find that connectivity derived from these task contexts predicts responses comparably to connectivity derived from resting-state data. This pattern held across the three controlled cognitive localizers acquired within the same preprocessing framework. These findings suggest that task fMRI can capture fine-grained individual differences in brain network architecture that are comparable to those captured at rest, broadening the potential utility of task data for investigating functional organization of brain networks.