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Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta‐synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient‐specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation. [ABSTRACT FROM AUTHOR]

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease. [ABSTRACT FROM AUTHOR]

Existing prosthetic technologies for people with upper limb amputation are being adopted at moderate rates. Once fitted for these devices, many upper limb amputees report not using them regularly or at all. The primary aim of this study was to solicit feedback about prosthetic technology and important device design criteria from amputees, clinicians, and device regulators. We compare these perspectives to identify common or divergent priorities. Twenty-one adults with upper limb loss, 35 clinicians, and 3 regulators completed a survey on existing prosthetic technologies and a conceptual sensorimotor prosthesis driven by implanted myoelectric electrodes with sensory feedback via spinal root stimulation. The survey included questions from the Trinity Amputation and Prosthesis Experience Scale, the Disabilities of the Arm, Shoulder, and Hand, and novel questions about technology acceptance and neuroprosthetic design. User and clinician ratings of satisfaction with existing devices were similar. Amputees were most accepting of the proposed sensorimotor prosthesis (75.5% vs clinicians (68.8%), regulators (67.8%)). Stakeholders valued user-centered outcomes like individualized task goals, improved quality of life, device reliability, and user safety; regulators emphasized these last two. The results of this study provide insight into amputee, clinician, and regulator priorities to inform future upper-limb prosthetic design and clinical trial protocol development. [ABSTRACT FROM AUTHOR]