Impact Factor 3.648
2018 JCR, Web of Science Group 2019

Frontiers journals are at the top of citation and impact metrics

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00715

Non-motor brain regions in non-dominant hemisphere are influential in decoding movement speed

  • 1School of Medicine, Johns Hopkins University, United States
  • 2Cleveland Clinic, United States
  • 3Emory University School of Medicine, United States
  • 4The Johns Hopkins Hospital, Johns Hopkins Medicine, United States

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 88 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related movement speed to spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of $0.38 \pm 0.03$~(mean $\pm$ standard deviation) and mean squared error of $1.07 \pm 0.09$. Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of \SI{70\pm2.75}{\percent} across subjects. These models included features from regions such as the right hippocampus, middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

Keywords: movement speed, Stereoelectroencephalography (SEEG), local field potential (LFP), Generalized linear model (GLM), Non-dominant hemisphere, non-motor brain regions, Frequency bands, Lasso algorithm

Received: 03 Aug 2018; Accepted: 25 Jun 2019.

Edited by:

Jonathan Miller, University Hospitals Cleveland Medical Center, United States

Reviewed by:

Rolando Grave De Peralta Menendez, Electrical Neuroimaging Group, Switzerland
Giuseppe Pellizzer, Medical School, University of Minnesota, United States  

Copyright: © 2019 Breault, Fitzgerald, Sacré, Gale, Sarma and Gonzalez-Martinez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ms. Macauley S. Breault, School of Medicine, Johns Hopkins University, Baltimore, United States, mbreaul1@jhu.edu