ArticleNature communications2024
Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
25 citing papers in PubMed.
- Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.iScience · 2026Review
- Accelerating the development of bioelectronic medicine: outcomes and innovations from the NIH SPARC initiative.Bioelectronic medicine · 2026Review
- Bioinspired hierarchical fibre bundles from microfluidics for nerve regeneration.Bioactive materials · 2026Article
- AI-driven tripartite classification for optimizing wearable bioelectronics in depression management.Science advances · 2026Article
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results.Communications medicine · 2026Article
- Hypoglossal nerve stimulation in a pig model for evaluation of genioglossus muscle force with different stimulation parameters.Scientific reports · 2026Article
- An Efficient Multi-Channel Electrotactile Parameter Configuration Method for Personalized Teleoperation.Biomimetics (Basel, Switzerland) · 2026Article
- The optimization of neuroprosthetic interfaces relying on biophysical and surrogate digital twins.npj biomedical innovations · 2026Review
- Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology.APL bioengineering · 2026Article
- Beyond Transplantation: Engineering Neural Cell Therapies and Combination Strategies for Spinal Cord Repair.Brain sciences · 2026Review
- Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.Journal of neural engineering · 2026Article
- Identification of low threshold off-target activation pathways during stimulation of carotid baroreceptor afferents in swine.bioRxiv : the preprint server for biology · 2026Article
- NEURONpyxl: fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses.Frontiers in computational neuroscience · 2026Article
- Temporal properties of direct current sensory block of the rat sciatic nerve using the C-fiber reflex.Journal of neural engineering · 2025Article
- Mitigating muscle contractions during pulsed field ablation by utilizing intersegment delays.Bioelectrochemistry (Amsterdam, Netherlands) · 2025Article
- Correlation of Electrical Impedance and Evoked Potentials With Properties of the Electrode Interface Using in Situ Block-Face Imaging of the Rat Pelvic Nerve.Neuromodulation : journal of the International Neuromodulation Society · 2025Article
- The human vagal complex: from gross anatomy to single neurons, from brainstem to abdomen.Research square · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- In situ electric field dosimetry analysis for powerline frequency peripheral nerve magnetic stimulation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.