Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed
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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.00715/full |
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| author | Macauley Smith Breault Zachary B. Fitzgerald Pierre Sacré John T. Gale Sridevi V. Sarma Jorge A. González-Martínez |
| author_facet | Macauley Smith Breault Zachary B. Fitzgerald Pierre Sacré John T. Gale Sridevi V. Sarma Jorge A. González-Martínez |
| author_sort | Macauley Smith Breault |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:36:41Z |
| format | Article |
| id | doaj.art-3519eb3049724c278199a1421162f6a0 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T08:36:41Z |
| publishDate | 2019-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-3519eb3049724c278199a1421162f6a02022-12-22T01:55:56ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-07-011310.3389/fnins.2019.00715417883Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement SpeedMacauley Smith Breault0Zachary B. Fitzgerald1Pierre Sacré2John T. Gale3Sridevi V. Sarma4Jorge A. González-Martínez5Neuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Neurosurgery, Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United StatesNeuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United StatesGale Neurotechnologies Inc., Smoke Rise, GA, United StatesNeuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Neurosurgery, Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United StatesSensorimotor 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.https://www.frontiersin.org/article/10.3389/fnins.2019.00715/fullmovement speedStereoelEctroencEphalography (SEEG)Local Field Potential (LFP)generalized linear model (GLM)regressionnon-dominant hemisphere |
| spellingShingle | Macauley Smith Breault Zachary B. Fitzgerald Pierre Sacré John T. Gale Sridevi V. Sarma Jorge A. González-Martínez Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed Frontiers in Neuroscience movement speed StereoelEctroencEphalography (SEEG) Local Field Potential (LFP) generalized linear model (GLM) regression non-dominant hemisphere |
| title | Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed |
| title_full | Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed |
| title_fullStr | Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed |
| title_full_unstemmed | Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed |
| title_short | Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed |
| title_sort | non motor brain regions in non dominant hemisphere are influential in decoding movement speed |
| topic | movement speed StereoelEctroencEphalography (SEEG) Local Field Potential (LFP) generalized linear model (GLM) regression non-dominant hemisphere |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.00715/full |
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