Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms, the relative importance, and the interaction between spontaneous coupling and task-induced activations. To do so, we recorded two groups of healthy participants (male and female) during rest and while they performed either a visual perception or a motor sequence task. We demonstrate that, for both tasks, stronger activations during the task as well as greater network coupling through spontaneous a rhythms at rest predict performance. However, high performers present an absence of classical task-induced activations and, instead, stronger spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.