A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

Abstract The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain’s functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain’s functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual’s task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.