Abstract
Abstract Massive whole-brain blood-oxygen-level dependent (BOLD) signal modulation (up to 95% of brain voxels) in response to task stimuli has recently been reported in functional MRI investigations. These findings have two implications. First, they highlight inability of a conventional ‘top-down’ general linear model approach to capture all forms of task-driven brain activity. Second, as opposed to a static ‘active’ or ‘non-active’ localization theory of the neural implementation of cognitive processes, functional neuroimaging should develop and pursue dynamical theories of cognition involving the dynamic interactions of all brain networks, in line with psychological constructionist theories of cognition. In this study, we describe a novel exploratory, bottom-up approach that directly estimates task-driven brain activity regardless of whether it follows an a priori reference function. Leveraging the property that task-driven brain activity is associated with reductions in BOLD signal variability, we combine the tools of instantaneous phase synchronization and independent component analysis to characterize whole-brain task-driven activity in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this novel framework to task fMRI data from a motor, theory of mind and working memory task provided through the Human Connectome Project. We discovered a large number of brain networks that dynamically synchronized to various features of the task scan, some overlapping with areas identified as ‘active’ in the top-down GLM approach. Using the results provided through this novel approach, we provide a more comprehensive description of cognitive processes whereby task-related brain activity is not restricted to dichotomous ‘active’ or ‘non-active’ inferences, but is characterized by the temporal dynamics of brain networks across time. Significance Statement This study describes the results of a novel exploratory methodological approach that allows for direct estimation of task-driven brain activity in terms of group-wise similarity in temporal signal dynamics, as opposed to the conventional approach of identifying task-driven brain activity with a hypothesized temporal pattern. This approach applied to three different task paradigms yielded novel insights into the brain activity associated with these tasks in terms of time-varying, low-frequency dynamics of replicable synchronization networks. We suggest that this exploratory methodological approach provides a framework in which the complexity and dynamics of the neural mechanisms underlying cognitive processes can be captured more comprehensively.