|Neuromuscular networking connectivity in sensorimotor impairments after stroke
|Hu, Xiaoling (BME)
Zheng, Yong-ping (BME)
|Cerebrovascular disease -- Patients -- Rehabilitation
Hong Kong Polytechnic University -- Dissertations
|Department of Biomedical Engineering
|xix, 160 pages : color illustrations
|More than half of stroke survivors experience both sensory and motor impairments in the upper extremity (UE), limiting their independence in daily living tasks. Neuroplastic processes of neuromuscular networking connectivity, including connectivity among cortical areas (i.e., cortico-cortical connectivity) and that between the sensorimotor cortex and muscle effectors (i.e., cortico-muscular connectivity), in central-and-peripheral nerves systems, is the basis of sensorimotor rehabilitation after stroke. However, little has been done on an effective neurological evaluation of sensorimotor impairments after stroke. This was primarily caused by the absence of systemic assessments on the altered neuromuscular networking connectivity in sensorimotor impairments and its evolution in the recovery process after stroke. Current sensorimotor evaluation post-stroke relied on traditional clinical assessments through manual operation and visual observation, with disadvantages of subjectiveness, low accuracy, and low repeatability. Therefore, the purpose of this project was to investigate the neuromuscular networking connectivity in post-stroke sensorimotor impairments and recovery in rehabilitation, including 1) the functional connectivity (FC) among cortical areas in fine tactile sensation after stroke, 2) the pathway-specific cortico-muscular coherence (CMC) in the motion compensation from the proximal upper limb to the fine motor control of distal fingers, 3) integrated sensorimotor evaluation of post-stroke cortical rearrangement in sensory-/motor-level neuromuscular electrical stimulation (NMES), and 4) the closed-loop neurorehabilitation effects of a CMC-Electromyogram (EMG)-driven NMES-robot after stroke, as in the following four studies:
In the first study, the whole-brain 64-channel electroencephalogram (EEG) was recorded in both stroke (n=8) and unimpaired participants (n=8) during the fabric stimulation. The FC among cortical areas and the networking structure in the brain were then estimated using EEG coherence and graph theory analyses. Results suggested that the tactile impairments post-stroke had increased inter-hemispheric connectivity and cortical activities mainly in the unaffected hemisphere and attentional areas for compensation to the ipsilesional somatosensory areas.
In the second study, synchronous EEG and electromyography (EMG) recordings were conducted from the sensorimotor cortical areas and both distal and proximal UE muscles in stroke (n=14) and unimpaired (n=11) participants during fine motor control of distal fingers. The directed CMC (dCMC) in descending, i.e., from EEG to EMG, and ascending pathways i.e., from EMG to EEG, and the corticomuscular conduction delay were analyzed. Results suggested that the post-stroke compensatory motions from the proximal elbow-shoulder to the precise control of distal fingers had the shifted descending predominance from the fingers towards the proximal elbow-shoulder joints, excessive sensory feedbacks in distal finger, and extended conduction delay for descending control in target muscles.
In the third study, sensory- and motor-level NMES to the hand-wrist extensors with synchronized whole-brain 64-channel EEG recordings were conducted in stroke (n=15) and unimpaired (n=20) participants. The NMES cortical neuromodulation was analyzed by event-related desynchronization/synchronization (ERD/ERS) and FC analyses based on EEG signals. Results suggested that sensory-/motor-level NMES and EEG effectively captured the cortical rearrangement in post-stroke sensorimotor impairments, presenting altered hemispheric dominance in cortical activation, over inhibition in cortical recovery, reduced cortical interaction with the ipsilesional hemisphere, and cortical compensation from neighboring regions.
In the fourth study, a randomized control trial was performed to compare the rehabilitation effectiveness of the CMC-EMG-triggered NMES-robot (n=16) and CMC-EMG-triggered Robot (n=11) for hand-wrist recovery after stroke. We assessed the rehabilitation effectiveness of these systems with both behavioral, i.e., clinical scores, and neurological measures, i.e., CMC, dCMC, and the levels of EMG activation. Results suggested that the CMC-EMG-triggered NMES-robot exhibited better motor outcomes than the CMC-EMG-triggered robot for the precise hand-wrist motor restoration after stroke. The additional NMES assistance in the system could enhance improvements on voluntary motor functions in target muscles and the re-distribution of central-and-peripheral voluntary motor efforts (CAP-VME) among UE muscles for motor relearning, contributing to the cortical sensorimotor integration for the closed-loop neurorehabilitation on target muscles.
In conclusion, the neuromuscular networking connectivity could be effective to evaluate the systemic neurological changes in sensorimotor impairments and recovery post-stroke.
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