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Neuroimage. 2018 May 1;171:135-147. doi: 10.1016/j.neuroimage.2017.12.093. Epub 2018 Jan 6.

Brain state flexibility accompanies motor-skill acquisition.

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
2
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
4
Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD 21218, USA.
5
Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA.
6
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
7
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: dsb@seas.upenn.edu.

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.

KEYWORDS:

Brain state flexibility; Discrete sequence production; Graph theory; Motor sequence learning

PMID:
29309897
PMCID:
PMC5857429
[Available on 2019-05-01]
DOI:
10.1016/j.neuroimage.2017.12.093
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