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Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or monitor conditions by recording from the peripheral nerves. The recent growth of Machine Learning (ML) has led to the application of a wide variety of ML techniques to PNIs, especially in circumstances where the goal is classification or regression. However, the extent to which ML has been applied to PNIs or the range of suitable ML techniques has not been documented. Therefore, a scoping review was conducted to determine and understand the state of ML in the PNI field. The review searched five databases and included 63 studies after full-text review. Most studies incorporated a supervised learning approach to classify activity, with the most common algorithms being some form of neural network (artificial neural network, convolutional neural network or recurrent neural network). Unsupervised, semi-supervised and reinforcement learning (RL) approaches are currently underutilized and could be better leveraged to improve performance in this domain.

Abstract Background It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be effective at discriminating neural sources in rat sciatic nerves. ESCAPE-NET was designed to operate using data from multi-channel nerve cuff arrays, and use the resulting spatiotemporal signatures to classify individual naturally evoked compound action potentials (nCAPs) based on differing source fascicles. The applicability of this approach to larger and more complex nerves is not well understood. To support future translation to humans, the objective of this study was to characterize the performance of this approach in a computational model of the human median nerve. Methods Using a cross-sectional immunohistochemistry image of a human median nerve, a finite-element model was generated and used to simulate extraneural recordings. ESCAPE-NET was used to classify nCAPs based on source location, for varying numbers of sources and noise levels. The performance of ESCAPE-NET was also compared to ResNet-50 and MobileNet-V2 in the context of classifying human nerve cuff data. Results Classification accuracy was found to be inversely related to the number of nCAP sources in ESCAPE-NET (3-class: 97.8% ± 0.1%; 10-class: 89.3% ± 5.4% in low-noise conditions, 3-class: 70.3% ± 0.1%; 10-class: 52.5% ± 0.3% in high-noise conditions). ESCAPE-NET overall outperformed both MobileNet-V2 (3-class: 96.5% ± 1.1%; 10-class: 84.9% ± 1.7% in low-noise conditions, 3-class: 86.0% ± 0.6%; 10-class: 41.4% ± 0.9% in high-noise conditions) and ResNet-50 (3-class: 71.2% ± 18.6%; 10-class: 40.1% ± 22.5% in low-noise conditions, 3-class: 81.3% ± 4.4%; 10-class: 31.9% ± 4.4% in high-noise conditions). Conclusion All three networks were found to learn to differentiate nCAPs from different sources, as evidenced by performance levels well above chance in all cases. ESCAPE-NET was found to have the most robust performance, despite decreasing performance as the number of classes increased, and as noise was varied. These results provide valuable translational guidelines for designing neural interfaces for human use.

Machine learning (ML) is a technique that learns to detect patterns and trends in data. However, the quality of reporting ML in research is often suboptimal, leading to inaccurate conclusions and hindering progress in the field, especially if disseminated in literature reviews that provide researchers with an overview of a field, current knowledge gaps, and future directions. While various tools are available to assess the quality and risk-of-bias of studies, there is currently no generalized tool for assessing the reporting quality of ML in the literature. To address this, this study presents a new screening tool called STAR-ML (Screening Tool for Assessing Reporting of Machine Learning), accompanied by a guide to using it. A pilot scoping review looking at ML in chronic pain was used to investigate the tool. The time it took to screen papers and how the selection of the threshold affected the papers included were explored. The tool provides researchers with a reliable and systematic way to evaluate the quality of reporting of ML studies and to make informed decisions about the inclusion of studies in scoping or systematic reviews. In addition, this study provides recommendations for authors on how to choose the threshold for inclusion and use the tool proficiently. Lastly, the STAR-ML tool can serve as a checklist for researchers seeking to develop or implement ML techniques effectively.

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs.

Abstract Central sensitization is a condition that represents a cascade of neurological adaptations, resulting in an amplification of nociceptive responses from noxious and non-noxious stimuli. However, whether this abnormality translates into motor output and more specifically, ventral horn abnormalities, needs to be further explored. Twenty healthy participants aged 20–70 were randomly allocated to topical capsaicin or a placebo topical cream which was applied onto their left upper back to induce a transient state of sensitization. Visual analogue scale (VAS) ratings of pain intensity and brush allodynia score (BAS) were used to determine the presence of pain and secondary allodynia. Surface electromyography (sEMG) and intramuscular electromyography (iEMG) were used to record motor unit activity from the upper trapezius and infraspinatus muscles before and twenty minutes after application of capsaicin/placebo. Motor unit recruitment and variability were analyzed in the sEMG and iEMG, respectively. An independent t-test and Kruskal–Wallis H test were performed on the data. The sEMG results demonstrated a shift in the motor unit recruitment pattern in the upper trapezius muscle, while the iEMG showed a change in motor unit variability after application of capsaicin. These results suggest that capsaicin-induced central sensitization may cause changes in ventral horn excitability outside of the targeted spinal cord segment, affecting efferent pathway outputs. This preclinical evidence may provide some explanation for the influence of central sensitization on changes in movement patterns that occur in patients who have pain encouraging of further clinical investigation. Clinical Trials registration number: NCT04361149; date of registration: 24-Apr-2020.

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.