دانلود رایگان مقاله انگلیسی همراهی حالت مغز در یادگیری حرکتی - الزویر 2018

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.

Conclusion

In summary, in this study we seek to better understand the changes in brain state that accompany the acquisition of a new motor skill over the course of extended practice. We treat the brain as a dynamical system whose states are characterized by a recognizable pattern of activation across anatomicaly defined cortical and subcortical regions. We apply tools from graph theory to study the temporal transitions (network edges) between brain states (network nodes). Our data suggest that the emergence of automaticity is accompanied by an increase in brain state flexibility, or the frequency with which the brain switches between activity states. Broadly, our work offers a unique perspective on brain variability, noise, and dynamics (Deco et al., 2009; Breakspear and McIntosh, 2011; Garrett et al., 2013, 2014), and its role in human learning.