Dynamic rewiring of electrophysiological brain networks during learning

Abstract

Human learning is an active and complex process. However, the brain mechanisms underlying human skill learning and the effect of learning on the communication between brain regions, at different frequency bands, are still largely unknown. Here, we tracked changes in large-scale electrophysiological networks over a 6-week training period during which participants practiced a series of motor sequences during 30 home training sessions. Our findings showed that brain networks become more flexible with learning in all the frequency bands from theta to gamma ranges. We found consistent increase of flexibility in the prefrontal and limbic areas in the theta and alpha band, and over somatomotor and visual areas in the alpha band. Specific to the beta rhythm, we revealed that higher flexibility of prefrontal regions during the early stage of learning strongly correlated with better performance measured during home training sessions. Our findings provide novel evidence that prolonged motor skill practice results in higher, frequency-specific, temporal variability in brain network structure.

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

“…Our analysis shows that while performance of the beta band in classification accuracy is not as high as alpha or theta bands, it still plays a crucial role in the flow of information during WM tasks [84]. Higher activations in central regions are observed in beta band, which is closely intertwined with cognitive processing [85]. During WM tasks, especially those requiring a motor response, the beta band may facilitate the translation of cognitive decisions into motor actions [86].…”

A distributed geometric rewiring model

“…According to f v, (8,12) (Fig. 3c), the most visited node is (10,9), which is at a Euclidean distance 3.6 from (8,12). • Case ii: Consider the rewiring rule R2 and ℓ max = L/4 , i.e., ℓ max ≈ 4.9 .…”

“…Node (8,12) rewires its dynamic link ((8, 12), (4, 13)) to a randomly selected node from f v, (8,12) , which is at a geodesic distance 2 from (8,12), and at a Euclidean distance of at most 4.9 from (8,12). The nodes satisfying such conditions are: (10, 9), (11,12), and (9,13). Although node (3, 1) is at a geodesic distance of 2 from (8, 12), it cannot be selected because its Euclidean distance from (8, 12) is 11.7, which is longer than 4.9.…”

“…In neuroscience, for example, rewiring can represent changes in neural connections. Understanding how the brain reconnects itself in response to experiences such as learning, injuries, or diseases is a critical research area 8 , 9 . In social networks, rewiring can represent changes in relationships or interactions between individuals and communities.…”

A distributed geometric rewiring model

“…In neuroscience, for example, rewiring can represent changes in neural connections. Understanding how the brain reconnects itself in response to experiences such as learning, injuries, or diseases is a critical research area 6,7 . In social networks, rewiring can represent changes in relationships or interactions between individuals.…”