Individual-level Functional Connectivity Predicts Cognitive Control Efficiency

Abstract

AbstractCognitive control (CC) is a vital component of cognition associated with problem-solving in everyday life. Many neurological and neuropsychiatric conditions have deficits associated with CC. CC is composed of multiple behaviors including switching, inhibiting, and updating. The fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON) and the dorsal default-mode network (dorsal-DMN) have been associated with switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about CC behaviors is still unclear. In the current study, participants performed two in-scanner tasks that required switching and inhibiting. We then used a series of support vector regression (SVR) models containing individually-estimated functional connectivity between the networks of interest derived during tasks and at rest to predict inhibition and switching behaviors in individual subjects. We observed that the combination of between-network connectivity from these individually estimated functional networks predicted accurate and timely inhibition and switching behaviors in individuals. We also observed that the relationships between canonical task-positive and task-negative networks predicted inhibiting and switching behaviors. Finally, we observed a functional dissociation between the FPCN-A and FPCNB during rest, and task performance predicted inhibiting and switching behaviors. These results suggest that individually estimated networks can predict individual CC behaviors, that between-network functional connectivity estimated within individuals is vital to understanding how CC arises, and that the fractionation of the FPCN and the DMN may be associated with different behaviors than their canonically accepted behaviors.