Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract

Predictive biomarkers of cognitive performance are informative about the neural mechanisms underlying cognitive phenomena, and have tremendous potential for the diagnosis and treatment of neuropathologies with cognitive symptoms. Among such biomarkers, 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 spatio-temporal 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 strength of dynamic modularity scores (clarity of subnetwork composition), 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 biomarkers of cognition, with relevance to basic and clinical neuroscience.