Distributed networks of brain areas interact with one another in a time-varying fashion to enable complex cognitive and sensorimotor functions. Here we used new network-analysis algorithms to test the recruitment and integration of large-scale functional neural circuitry during learning. Using functional magnetic resonance imaging data acquired from healthy human participants, we investigated changes in the architecture of functional connectivity patterns that promote learning from initial training through mastery of a simple motor skill. Our results show that learning induces an autonomy of sensorimotor systems and that the release of cognitive control hubs in frontal and cingulate cortices predicts individual differences in the rate of learning on other days of practice. Our general statistical approach is applicable across other cognitive domains and provides a key to understanding time-resolved interactions between distributed neural circuits that enable task performance.

Adult human cognition is supported by systems of brain regions, or modules, that are functionally coherent at rest and collectively activated by distinct task requirements. However, an understanding of how the formation of these modules supports evolving cognitive capabilities has not been delineated. Here, we quantify the formation of network modules in a sample of 780 youth (aged 8-22 y) who were studied as part of the Philadelphia Neurodevelopmental Cohort. We demonstrate that the brain's functional network organization changes in youth through a process of modular evolution that is governed by the specific cognitive roles of each system, as defined by the balance of within-vs. between-module connectivity. Moreover, individual variability in these roles is correlated with cognitive performance. Collectively, these results suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition.neurodevelopment | graph theory | network science | modularity | brain network T he human brain is composed of large-scale functional networks that are coherent at rest, forming identifiable modules that support specific cognitive functions (1-3). These modules include well-known subsystems, such as the default-mode, visual, motor, auditory, attention, salience, and cognitive control systems. Prior research has shown that this modular structure evolves considerably during development in youth (4, 5) and across the life span (6, 7). Network modularity, a measure of the segregation between modules, is high during young adulthood and decreases across the latter life span (6, 7). Other features of network reorganization accompany development (8), including a growing preference for interactions between hubs and nonhubs (9), and between regions separated by large physical distances (10).Although prior research has explored such changes in gross network features, it remains unknown how the relationships between specific types of cognitive systems evolve during adolescent development. Ongoing developmental changes in connectivity between cognitive systems are suggested by known differences in how these systems are organized in the adult brain: Primary motor and sensory systems display a high degree of segregation with limited connections to other modules, whereas higher order cognitive systems have more between-module connectivity (1). Moreover, the disparate connectivity profiles of such systems may be critical for optimal cognitive functioning (11). Differentiation of specific network modules may thus support the burgeoning cognitive, emotional, and motor capabilities seen during adolescence (12). Furthermore, abnormalities in functional network organization are a ubiquitous finding in major neuropsychiatric conditions (11), which are increasingly considered disorders of neurodevelopment (13). Thus, a quantitative characterization of the modular maturation of functional networks in youth is critical to understanding the development of both normal and abnormal brain function.Here, w...

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.