Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

• Published in: Frontiers in neuroscience Vol. 13; p. 715
• Main Authors: Breault, Macauley Smith, Fitzgerald, Zachary B., Sacré, Pierre, Gale, John T., Sarma, Sridevi V., González-Martínez, Jorge A.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 16.07.2019; Frontiers Media S.A
• Subjects: Brain research; Clinical trials; generalized linear model (GLM); Generalized linear models; Intraparietal sulcus; Local Field Potential (LFP); Motor task performance; movement speed; Neuroscience; non-dominant hemisphere; regression; Roles; Sensorimotor integration; Sensorimotor system; Sensory integration; StereoelEctroencEphalography (SEEG); Temporal gyrus; United States > US; generalized linear model (GLM); Least Absolute Shrinkage and Selection Operator (LASSO); regression; non-dominant hemisphere; Local Field Potential (LFP); StereoelEctroencEphalography (SEEG); non-motor brain regions; movement speed
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.00715

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 87 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 spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. 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 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right 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.