Abstract
Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.
Highlights
The acquisition of new motor skills requires the brain to flexibly reconfigure neural circuits to master a desired performance level (Bassett & Mattar, 2017)
N-methyl-D-aspartate receptor (NMDA): NMDA receptor is a glutamate receptor in the central nervous system that is very important for synaptic plasticity and memory function
Whereas the initial learning phase engages a widespread network consisting of primary motor area (M1), supplementary motor area (SMA), basal ganglia (BG), dorsolateral prefrontal cortex (DLPFC), premotor cortex, and posterior cerebellum, the following longer term learning phase relies on a smaller set of brain regions including M1, SMA, BG, and the lateral cerebellum (Dayan & Cohen, 2011)
Summary
The acquisition of new motor skills requires the brain to flexibly reconfigure neural circuits to master a desired performance level (Bassett & Mattar, 2017). By combining network analysis and functional magnetic resonance imaging (fMRI), recent studies have shown that brain network features including flexibility (Bassett et al, 2011), connectivity strength, local path length, and nodal efficiency (Heitger et al, 2012; Sami & Miall, 2013) change in response to motor learning and can predict its rate (Bassett et al, 2011). Changes in the brain network architecture cannot only be assessed during the process of motor learning by using task-based fMRI, and during rest. Whereas plasticityrelated effects of motor learning likely shape the intrinsic configuration of brain circuits, the biological mechanisms in humans remain largely unknown
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