Abstract Introduction/Aims There is a dearth of knowledge regarding the status of infralesional lower motor neurons (LMNs) in individuals with traumatic cervical spinal cord injury (SCI), yet there is a growing need to understand how the spinal lesion impacts LMNs caudal to the lesion epicenter, especially in the context of nerve transfer surgery to restore several key upper limb functions. Our objective was to determine the frequency of pathological spontaneous activity (PSA) at, and below, the level of spinal injury, to gain an understanding of LMN health below the spinal lesion. Methods Ninety‐one limbs in 57 individuals (53 males, mean age = 44.4 ± 16.9 years, mean duration from injury = 3.4 ± 1.4 months, 32 with motor complete injuries), were analyzed. Analysis was stratified by injury level as (1) C4 and above, (2) C5, and (3) C6‐7. Needle electromyography was performed on representative muscles innervated by the C5‐6, C6‐7, C7‐8, and C8‐T1 nerve roots. PSA was dichotomized as present or absent. Data were pooled for the most caudal infralesional segment (C8‐T1). Results A high frequency of PSA was seen in all infralesional segments. The pooled frequency of PSA for all injury levels at C8‐T1 was 68.7% of the limbs tested. There was also evidence of PSA at the rostral border of the neurological level of injury, with 58.3% of C5‐6 muscles in those with C5‐level injuries. Discussion These data support a high prevalence of infralesional LMN abnormalities following SCI, which has implications to nerve transfer candidacy, timing of the intervention, and donor nerve options.
Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.
Pattern-recognition-based control using surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for providing control of multiple prosthetic functions. However, it is not clear how these systems will perform when the user possesses a functional wrist; an attribute unique to the population of partial-hand amputees. Fortunately, partial-hand amputees may have remaining intrinsic hand muscles, from which additional information-rich EMG data may be extracted and used for prosthetic control. We investigated the effect of statically and dynamically varying wrist position on a pattern recognition system's ability to classify hand grasp patterns in able-bodied individuals. We found that varying wrist position significantly degraded the system's performance (p<;0.001). The system performed worse when trained only with EMG data from the extrinsic hand muscles than when trained with EMG data from the intrinsic hand muscles. The system's performance significantly improved when trained in all static wrist positions (p<;0.001) and with all dynamic wrist motions (p<;0.001).
Cervical spinal cord injury (SCI) significantly impairs upper limb function, affecting patients' quality of life. Nerve transfer surgery can restore arm and hand function, but its success depends on the health of infralesional lower motor neurons (LMNs). LMN abnormalities are prevalent in muscles targeted for nerve transfer, particularly those innervated by the posterior interosseous nerve (PIN) and radial nerve, essential for wrist extension and hand opening. This study evaluates the health of infralesional LMNs in cervical SCI using multipoint stimulation motor unit number estimation (MPS-MUNE). We assessed motor unit counts in the C7-innervated anconeus and the predominantly C8-innervated extensor indicis (EI) to determine their viability as targets for nerve transfer surgery. We conducted a prospective, two-center cohort study using MPS-MUNE to evaluate 15 individuals with cervical SCI (26 limbs) and 17 healthy controls. Compound muscle action potential (CMAP) and MUNE values were significantly lower in SCI patients compared to controls (EI CMAP: 2.0 mV ± 1.57, EI MUNE: 33 ± 30.5; anconeus CMAP: 2.7 mV ± 1.9, anconeus MUNE: 39 ± 50.6 versus controls: EI CMAP: 6.6 mV ± 1.0, EI MUNE: 137 ± 33.9; anconeus CMAP: 6.6 mV ± 1.3, anconeus MUNE: 146 ± 42.3). Test-retest reliability for EI and anconeus were 0.84 (CI: 0.45-0.95) and 0.78 (CI: 0.36-0.93), respectively. Significant LMN loss was observed after cervical SCI. MPS-MUNE shows potential for evaluating LMN health, highlighting its importance for assessing nerve transfer targets and planning interventions.
Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (p<;0.001); and that training in only variable wrist positions, with only dynamic wrist movements, or with both variable wrist positions and movements results in lower error rates than training in only the neutral wrist position (p<;0.001). Finally, our results show that both an increase in window length and a decrease in the number of grasps available to the classifier significantly decrease classification error (p<;0.001). These results remained consistent whether the classifier selected or maintained a hand-grasp.
The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study's objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function.EMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions. The performance of 4 classification schemes using EMG data alone and EMG data combined with wrist positional information was evaluated. Using recorded wrist positional data, the relationship between EMG features and wrist position was modeled and used to develop a wrist position-independent classification scheme.A multi-layer perceptron artificial neural network classifier was better able to discriminate four hand motion classes in 13 wrist positions than a linear discriminant analysis classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to EMG data significantly improved performance (p < 0.001). Training the classifier with the combination of extrinsic and intrinsic muscle EMG data performed significantly better than using intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001). The same trends were observed for amputees, except training with intrinsic muscle EMG data, on average, performed worse than the extrinsic muscle EMG data. We propose a wrist position-independent controller that simulates data from multiple wrist positions and is able to significantly improve performance by 48-74% (p < 0.05) for non-amputees and by 45-66% for partial-hand amputees, compared to a classifier trained only with data from a neutral wrist position and tested with data from multiple positions.Sensor fusion (using EMG and wrist position information), non-linear artificial neural networks, combining EMG data across multiple muscle sources, and simulating data from different wrist positions are effective strategies for mitigating the wrist position effect and improving classification performance.
Abstract Introduction/Aims Phrenic Neuropathy (PhN) impairs diaphragm muscle function, causing a spectrum of breathing disability. PhN etiologies and their natural history are ill defined. This knowledge gap hinders informed prognosis and management decisions. This study aims to help fill this knowledge gap on PhN etiologies, outcomes, and recovery patterns, especially in the context of non-surgical clinical practice. Methods This is a retrospective study from two interdisciplinary clinics, physiatry and neurology based. Patients were included if PhN was identified, and other causes of hemi-diaphragm muscle dysfunction excluded. Patients were followed serially per the discretion of the neuromuscular trained neurologist or physiatrist. Recovery was assessed using pulmonary function tests (PFTs), diaphragm muscle US thickening ratio, and patient-reported outcomes in patients presenting within two years of PhN onset. Results We identified 151 patients with PhN. The most common etiologies included idiopathic (27%), associated with cardiothoracic procedure (24%), and intensive care unit (17%). Of these patients, 117 (77%) were evaluated within two years of PhN onset. Of patients included in outcome analyses, 69% saw improvement on serial US, 50% on serial PFTs and 79% reported symptomatic improvement at an average of 13, 16, and 17 months respectively. Conclusion This study maps PhN etiologies and recovery. A clear majority of PhN patients show improvement in diaphragm muscle function, but on average improvements took 13-17 months depending on the assessment type. These insights are vital for developing tailored treatments and can guide physicians in prognosis and decision-making, especially if more invasive interventions are being considered.
Abstract Study Design: Two-centre retrospective cohort study. Objectives: We examined the health of the infralesional LMN in individuals with traumatic cervical SCI, using data derived from the clinical electrodiagnostic examination performed early after SCI. Setting: Academic hospitals. Methods: We assessed 66 limbs in 42 individuals (40 males, mean age = 43.6±17.2, mean duration from injury = 3.3±1.5 months, 25 with motor complete injuries). Analysis was stratified by injury level as 1) C4 and above, 2) C5 and 3) C6-7. EMG performed on representative muscles from C5-6, C6-7, C7-8 and C8-T1, were included in analysis. LMN abnormality was dichotomized as present (abnormal spontaneous activity) or absent. Data were pooled for the most caudal infralesional segment (C8-T1). Results: A high frequency of denervation potentials was seen in all infralesional segments for all injury levels. The pooled frequency of denervation potentials at C8-T1 was 74.6% of limbs tested. There was also evidence of denervation potentials at the rostral border of the neurological level of injury, as high as 64.3% of C5-6 muscles for C5 injuries. Conclusion: These data support a high prevalence of infralesional LMN abnormality following SCI, which has implications to candidacy, timing of the intervention and donor nerve options.
ABSTRACT Introduction The health of infralesional lower motor neurons (LMNs) after a cervical spinal cord injury is frequently overlooked, despite its critical role in mediating effective clinical interventions for improving arm and hand function. Prior studies suggest high frequencies of infralesional lower motor abnormalities in muscles that are potential targets for nerve transfer surgery, a procedure that has the potential to restore upper limb function. Methods In this prospective, two-center cohort study, we used multipoint stimulation motor unit number estimation (MPS-MUNE) to evaluate the number of motor units in clinically relevant infralesional muscles, including the predominantly C7-innervated anconeus and the predominantly C8-innervated extensor indicis (EI) in 15 individuals with cervical spinal cord injury (26 limbs) and 17 healthy controls. Results Both CMAP and MUNE values were significantly lower (p < 0.05) for those with cervical spinal cord injury (EI CMAP: 2.0 mV±1.57, EI MUNE: 33±30.5, Anconeus CMAP:2.7 mV±1.9, Anconeus MUNE: 39±50.6) versus controls (EI CMAP: 6.6mV±1.0, EI MUNE:137±33.9, Anconeus CMAP:6.6 mV±1.3, Anconeus MUNE: 146 ±42.3). The test-retest reliability as measured by intraclass correlation coefficient and confidence interval (CI) for the EI and anconeus were 0.84 (CI: 0.45-0.95) and 0.78 (CI: 0.36-0.93), respectively. Discussion This study shows significant loss of infralesional motor units after cervical spinal cord injury. We demonstrate the potential utility of MPS-MUNE for evaluating the health of LMNs. The LMN abnormalities observed underscore the significance of this approach to evaluating potential targets for nerve transfer surgery for the restoration of upper limb function.
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