Learning-induced autonomy of sensorimotor systems

By D. Bassett, Muzhi Yang, Nicholas F. Wymbs, Scott T. Grafton. Nature Neuroscience, 2014

570 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/34a2c9661bdd7f2dfc4f25084847c297751af32a

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Citations in the corpus

Consolidation alters motor sequence-specific distributed representations (2018, background): “, 2002; Doyon and Benali, 2005) are known to occur within the initial training session, as well as during the offline consolidation phase, the latter being characterized by a functional ‘reorganization’ of the nervous system structures supporting this type of procedural memory function (Rasch and Bo”
Network neuroscience (2017, methodology): “Multilayer networks are networks whose nodes may be connected by different types of edges, with each type being encoded in a different layer 90 .”
Communication dynamics in complex brain networks (2017, background): “Individual network elements fluctuate in their modular affiliation 25,154 and their functional connectivity with other parts of the brain 155,156 , and extended functional networks reconfigure with changing task demands 156–159 .”
Modular Brain Networks (2015, background): “A number of studies have begun to investigate multislice representations of brain networks (Bassett et al. 2011, 2013, 2015; Cole et al. 2014) in which multiple instances of a single network (a slice) form a multislice stack.”
Two’s company, three (or more) is a simplex (2016, unknown): “5c) (Bassett et al, 2015).”
The modular and integrative functional architecture of the human brain (2015, background): “Given that activity can dissociate from connectivity (28) and no previous analysis has quantified connectivity changes in relation to the number of modules engaged in a task, this type of evidence is not present in previous analyses of connectivity data.”
Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015, methodology): “We estimated the transience of module–module interaction by averaging all elements of the module allegiance matrix T that link nodes in two different modules; we refer to this quantity as “integration” (24).”
Modern temporal network theory: a colloquium (2015, background): “ of the oxygen levels as measured by fMRI scanning. Even though fMRI has a temporal resolution of the shortest time scale of neuronal activities, it has proven fruitful to study as a temporal network [20,21,176]. 4 Petter Holme: Modern temporal network theory: A colloquium 2.8 Travel and transportat”
The Neuroscience of Intelligence (2016, background): “On one hand, efficiency remains a popular concept for thinking about neural circuit activity and how it relates to complex cognition (Bassett et al., 2015).”
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition (2016, background): “greater reduction in the involvement of other networks, a frontocingulate network in particular, led to increased learning (Bassett et al., 2015).”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “A wealth of whole brain connectivity analyses have similarly shown that the interaction between systems is an important component of skill learning specifically, and other domains of learning more generally [2, 8].”