Brain state flexibility accompanies motor-skill acquisition

Abstract

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging - and to assess their dynamics during learning - remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

Understanding diaschisis models of attention dysfunction with rTMS

“…Among the communities observed during visual tracking, some exhibited extreme dynamics compared to the population as a whole, as estimated using a null model. Nodes in the hetermodal parietotemporal cortex, which included the stimulation site, were among the most flexible, tending to coherently integrate with other communities, consistent with previous models of network ablation that reveal higher levels of network flexibility driven by hetermodal cortex 72 .…”

“…For example, extended practice in figure sequences that increases motor automaticity also decreases the modularity of brain systems, while increases the number of transitions between network structures (i.e. the flexibility of the network) 72 . Moreover, those individuals that exhibit more flexibility of the network tend to demonstrate more learning.…”

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks

“…Measures of brain activity based on fMRI have suggested that specific regions of the brain play crucial roles in brain state transitions (Gu et al, 2017(Gu et al, , 2018. Using a graph-based analysis of fMRI signal amplitude, it has been shown that higher flexibility of transitioning between brain states was associated with learning progress (Reddy et al, 2018) and with executive performance differences between children and young adults (Medaglia et al, 2018). Compared to direct measures of fMRI signal amplitudes, brain networks contain a wealth of complex information that may better represent brain states (Bullmore and Sporns, 2009;Ashourvan et al, 2017).…”

Development of Dynamic Functional Architecture during Early Infancy

“…During early development, the increasingly adaptive ability of the brain is to better fit for or response to the moment-to-moment changes in the environment, as well as time-varying attention and task control demands during the conduct of complex cognitive functions, all of which requires a timely response and continuous preparedness at different attention levels. The increasing Vbetween-net of the SMN suggests richer FC patterns underlying feed-back and feed-forward interactions between it and other high-order functional sub-networks , which may allow infants to carry out more and more complex tasks (Reddy et al, 2018). Combing with the heightened stability (decreased Vwithin-net) of FC within the SMN, we speculated that the increased dynamic reconfigurations between SMN and high-order networks may due to the enhanced ability of infants for integrating internal and external sensory and perception input to achieve the rapid development of complex functions.…”