Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke
Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of mu...
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Published in | Scientific reports Vol. 12; no. 1; pp. 13029 - 14 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
29.07.2022
Nature Publishing Group Nature Portfolio |
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Abstract | Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. |
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AbstractList | Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. Abstract Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions. |
ArticleNumber | 13029 |
Author | Bighamian, Ramin Raghavan, Preeti O’Keeffe, Rory Shirazi, Seyed Yahya Bilaloglu, Seda Jahed, Shayan Atashzar, S. Farokh |
Author_xml | – sequence: 1 givenname: Rory surname: O’Keeffe fullname: O’Keeffe, Rory organization: Department of Electrical and Computer Engineering, New York University – sequence: 2 givenname: Seyed Yahya surname: Shirazi fullname: Shirazi, Seyed Yahya organization: Department of Electrical and Computer Engineering, New York University – sequence: 3 givenname: Seda surname: Bilaloglu fullname: Bilaloglu, Seda organization: Department of Medicine, New York University Langone Health – sequence: 4 givenname: Shayan surname: Jahed fullname: Jahed, Shayan organization: Department of Electrical and Computer Engineering, New York University – sequence: 5 givenname: Ramin surname: Bighamian fullname: Bighamian, Ramin organization: Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration – sequence: 6 givenname: Preeti surname: Raghavan fullname: Raghavan, Preeti email: praghav3@jhmi.edu organization: Departments of Physical Medicine and Rehabilitation and Neurology, Johns Hopkins University School of Medicine – sequence: 7 givenname: S. Farokh surname: Atashzar fullname: Atashzar, S. Farokh email: f.atashzar@nyu.edu organization: Department of Electrical and Computer Engineering, New York University, Department of Mechanical and Aerospace Engineering, New York University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35906239$$D View this record in MEDLINE/PubMed |
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Snippet | Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with... Abstract Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with... |
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SubjectTerms | 631/378/116 639/166/985 Arm Biomarkers Brain research Central nervous system Coordination Electromyography Hand Humanities and Social Sciences Humans Information Theory Motor ability Motor task performance multidisciplinary Muscle function Muscles Nervous system Neurological disorders Paresis Rehabilitation Science Science (multidisciplinary) Sensorimotor integration Sensory integration Stroke Stroke Rehabilitation - methods Upper Extremity |
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Title | Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke |
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