Temporal structure of task engagement organizes infra-slow BOLD dynamics
Task-based fMRI has traditionally characterized cognitive engagement through spatial patterns of BOLD activation, leaving open whether tasks also organize activity along the frequency dimension. Here we tested whether temporally structured task engagement reshapes infra-slow BOLD dynamics, asking whether task responses are simply superimposed onto ongoing activity or instead redistribute power across the infra-slow spectrum. Across three independent fMRI datasets, task timing systematically reorganized the BOLD power spectrum. In the main visual attention dataset, periodic stimulation produced sharp stimulus-locked peaks at 0.083 and 0.125 Hz; critically, these gains were matched by a reduction concentrated within the dominant intrinsic infra-slow range (0.01-0.04 Hz), indicating redistribution rather than a simple local increase. Independent auditory and visual-motion datasets showed analogous task-frequency responses at their respective timescales, indicating that this reorganization generalizes across modalities and stimulation frequencies. The peaks were spatially related to GLM-derived activation but persisted after removal of the GLM-modeled response, indicating that this frequency-specific organization is not fully reducible to conventional task-evoked activation. These features were functionally informative: task-imposed frequencies distinguished cognitive states, predicted individual reaction time, and preserved subject-specific signatures across runs more effectively than broader spectral ranges or GLM-derived features. Finally, Jansen-Rit simulations coupled to a Balloon-Windkessel model reproduced the peaks, showing that their expression depends on balanced excitatory, inhibitory, and filter-gain regimes rather than simple amplification of input. Together, these findings indicate that task-fMRI reveals not only where cognition is localized, but how temporally structured engagement redistributes infra-slow dynamics across timescales.