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Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke.

Rory O'Keeffe1, Seyed Yahya Shirazi1, Seda Bilaloglu2

  • 1Department of Electrical and Computer Engineering, New York University, New York, NY, USA.

Scientific Reports
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces InfoMuNet, a new biomarker for sensorimotor integration. It quantifies how sensory information improves motor performance in individuals with central nervous system injuries, like stroke.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Sensorimotor integration is vital for motor control.
  • Central nervous system injuries, such as stroke, often impair sensorimotor function.
  • Quantifying the impact of sensory information on motor performance in these individuals is challenging.

Purpose of the Study:

  • To introduce and validate InfoMuNet, a novel functional biomarker for sensorimotor integration.
  • To quantify the role of sensory information in motor performance after stroke.
  • To assess the sensitivity of InfoMuNet to different sensory inputs.

Main Methods:

  • Developed InfoMuNet, a nonlinear network graph of muscle connectivity.
  • Recruited 32 individuals with post-stroke hemiparesis.
  • Measured surface electromyography from 8 arm muscles during a grasp-and-lift task.
  • Assessed changes in muscle connectivity before and after sensory exposure from the less-affected hand.

Main Results:

  • InfoMuNet robustly quantified changes in functional muscle connectivity in the affected hand post-sensory exposure.
  • >90% of subjects showed motor improvement after sensory exposure.
  • InfoMuNet demonstrated high sensitivity to alterations in tactile, kinesthetic, and visual input.

Conclusions:

  • InfoMuNet is a reliable biomarker for assessing sensorimotor integration.
  • Sensory information significantly improves motor performance in individuals with hemiparesis.
  • InfoMuNet shows potential for precision rehabilitation interventions in neurological disorders.