Whole-brain modular dynamics at rest predict sensorimotor learning performance

Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatiotemporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

The prediction of cognitive performance by measures derived from neuroimaging signals is informative about the neural processes underlying those signals, with the potential for use in the diagnosis of cognitive abnormalities, such as those associated with aging and Alzheimer’s disease. These neural markers include statistics of functional networks, characterizing the cooperation of distributed brain regions in large-scale networks. We investigated dynamics (changes over time) of functional networks prior to a task on which participants differ in their use of strategies. We found that performance on the upcoming task was predicted by several measures of whole-brain network dynamics.