Research Objectives
To
investigate the use of high-density surface electromyography (EMG) and
complex network analysis to quantify upper extremity motor impairment in
chronic stroke survivors with hemiparesis. EMG can provide insights
into the residual neuro-muscular activity during attempted functional
movement. Using network analysis, we can determine how different muscle
regions interact to create complex motion and the associated deficits
after stroke.
Design
In
three two-hour sessions, we led participants through attempted
functional movements of the hand, fingers, and wrist while recording EMG
activity using the NeuroLife® Sleeve, a 150-electrode wearable forearm
garment.
Setting
Studies were conducted within Battelle's research facilities in Columbus, Ohio.
Participants
Six chronic stroke survivors (>6 months post-stroke) with moderate UE impairment (UEFM: 7-38).
Interventions
Not applicable.
Main Outcome Measures
Global
efficiency and local clustering of the functional EMG networks. Global
efficiency measures the ease of traversal of the network topology, with
many regions in close coordination with each other showing increased
efficiency. Local clustering measures the amount of electrode triads
that appear in the network architecture, a measure of local
microstructure.
Results
We
find that network graphs from participants show differing topological
structure that relates to the level of impairment of the participant.
Specifically, participants with moderate to severe hand impairment had
lower global network efficiency and local clustering when compared to
mild-impairment participants or able-bodied controls. These results show
that differences in network topology are sensitive to the
pathophysiology that follows stroke.
Conclusions
Complex
network analysis of surface EMG can provide a novel, quantifiable
assessment of the extent of deficit in subjects with chronic stroke.
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