The objective of this study is to develop Neuro-Intermuscular Coordination Enhancement (NICE) rehabilitation, a novel neuromuscular control signal-guided strategy that visually guides stroke patients to individually activate groups of synergistic muscles through human-machine interaction. Ultimately, the development will lead to better clinical motor recovery, better quality of life, and lowered healthcare costs associated with the impairment.
Stroke is the leading cause of severe long-term disability, affecting 9.4 million Americans. Each year around 800,000 people suffer a stroke even in the USA. Chronic upper extremity motor impairment is a major contributing factor to disability; functional use of the affected UE in daily life is a key factor for increased independence, return to work, and overall quality of life. Thus, effective and innovative treatment to address long-term disability is both a major public health need and an economic necessity. The study will develop an innovative human-machine interaction platform to target and improve inter-joint coordination and motor function by enhancing muscular coordination in the UE. This study, in total, 38 chronic stroke survivors will be randomly assigned into two rehabilitation strategies either neuromuscular-coordination guided exercise (NICE; therapy group) or force-guided exercise (control group). The inclusion criteria primarily consist of: (1) having experienced an ischemic or hemorrhagic stroke at least 6 months prior (chronic stroke); (2) being between 21 and 80 years of age; (3) not having received botulinum toxin treatment in the affected arm within the past 3 months; and (4) having no cognitive impairments that would affect task comprehension or the ability to provide informed consent. This study will evaluate the effects of both rehabilitation exercises on muscle coordination, standardized clinical scores, kinetics, and electroencephalogram.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
TRIPLE
Enrollment
48
During training exercise, post-stroke participants will be asked to match the targets on the screen. The experimental group will match them by activating a specific set of muscle. During assessment trials, a physical therapist or occupational therapist will rate the functional level of arm impairment using FMA and ARAT.
During training exercise, post-stroke participants will be asked to match the targets on the screen. The active comparator group will match them by generating isometric force in a desired target direction. During assessment trials, a physical therapist or occupational therapist will rate the functional level of arm impairment using FMA and ARAT.
University of Houston
Houston, Texas, United States
RECRUITINGChange in Fugl-Meyer Assessment (FMA) score
To measure severity of motor impairment after stroke, FMA will be performed in the human upper extremity. FMA is commonly used to assess severity of motor impairment and motor recovery. The maximum FMA upper extremity motor score is 66 (i.e., 0: complete motor impairment; 66: normal motor performance). Each item is scored on a 3-point scale (0 = cannot perform, 1 = performs partially, 2 = performs fully).
Time frame: Pre-Training (baseline), post-training (6-week follow-up), 10-week and 18-week follow ups
Change in similarity score of intermuscular coordination patterns
EMGs will be recorded from 8 muscles. To assess whether muscle-synergy guided and/or force-guided exercise induce changes in the composition of intermuscular coordination patterns (ICoPs), non-negative matrix factorization will be applied to EMGs to identify and compare ICoPs.
Time frame: Pre-Training (baseline), post-training (6-week follow-up), 10-week and 18-week follow ups
Change in kinematic synergy
Kinematic synergies are a representation of multi-joint coordination. It will be identified using NNMF applied to the joint kinematic data obtained from 3D dynamic point-to-point reaching and drinking tasks.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in pairwise joint angle-to-angle correlation
Pairwise joint angle-to-angle correlation is a way to see the joint coupling using kinematic data. It will be calculated using Pearson's correlation coefficient between joint angles during the point-to-point reaching task.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in similarity score of intermuscular coordination patterns in dynamic task
Surface EMGs will be recorded from 8 key arm muscles during a 3D dynamic task and drinking task. Non-negative matrix factorization will be applied to EMGs to identify and compare intermuscular coordination patterns.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in active range of motion
The active range of motion will be calculated from full active range tasks for shoulder flexion/extension, internal/external rotation, abduction/adduction, elbow flexion/extension, and wrist pronation/supination.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Changes in EEG-derived spectral powers
EEG-derived spectral powers will be calculated, in resting and task conditions, across different frequency bands and different event-related spectral potentials across four different directions of target match.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in Revised Brain Symmetry Index
The revised brain symmetry index with EEG signals will be computed in the resting state during eyes open and closed conditions.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in cortico-muscular connectivity
Functional connectivity using a directed transfer function will be computed to identify the information flow and coherence among EEG and EMG signals in the desired brain region and muscle activation associated with directional (SE, SF, EE, EF) force generation.
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
Change in cortico-cortical connectivity
Functional connectivity using a directed transfer function will be computed to identify the information flow and coherenceamong EEG signals from different regions of interest (sources, e.g., ipsi and contralesional fronto-parietal regions, primary motor cortex and somatosensory cortices).
Time frame: Pre-Training (baseline), post-training (6-week follow-up)
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