3 years ago

Electroencephalographic connectivity measures predict learning of a motor sequencing task.

Steven C Cramer, Franziska Knapp, Ramesh Srinivasan, Jennifer Wu
Individuals vary significantly with respect to rate and degree of improvement with motor practice. While the regions that underlie motor learning have been well described, neurophysiological factors underlying differences in response to motor practice are less well understood. The present study examined both resting state and event-related EEG coherence measures of connectivity as predictors of response to motor practice on a motor sequencing task using the dominant hand. Thirty-two healthy, young, right-handed participants underwent resting EEG prior to motor practice. Response to practice was evaluated both across the single session of motor practice, and 24-hours later at a retention test of short-term motor learning. Behaviorally, the group demonstrated statistically significant gains both in single-session motor improvement and across-session motor learning. A resting-state measure of whole brain coherence with primary motor cortex (M1) at baseline robustly predicted subsequent motor improvement (validated R(2)=0.55) and motor learning (validated R(2)=0.68) in separate partial least squares regression models. Specifically, greater M1 coherence with left frontal-premotor cortex (PMC) at baseline was characteristic of individuals likely to demonstrate greater gains in both motor improvement and motor learning. Analysis of event-related coherence with respect to movement found the largest changes occurring in areas implicated in planning and preparation of movement, including PMC and frontal cortices. While event-related coherence provided a stronger prediction of practice-induced motor improvement (validated R(2)=0.73) it did not predict the degree of motor learning (validated R(2)=0.16). These results indicate that connectivity in the resting-state is a better predictor of consolidated learning of motor skills.

Publisher URL: http://doi.org/10.1152/jn.00580.2017

DOI: 10.1152/jn.00580.2017

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.