A computational outlook on neurostimulation

Bioelectronic Medicine, 2020 · DOI: https://doi.org/10.1186/s42234-020-00047-3 · Published: April 8, 2020

Simple Explanation

The authors discuss how computational models have influenced the field of neurostimulation for pain and movement recovery, deep brain stimulation, and even device regulations. Computational models serve two purposes: highlighting current knowledge of a specific system and directing experimental research, and providing a virtual testing platform to study interactions between neuromodulation technologies and the nervous system. Computational models can support a wide range of clinical neuromodulation therapies by understanding how inputs to specific neural networks can be altered to modify aberrant behaviors.

Study Duration

Not specified

Participants

Not specified

Evidence Level

Not specified

Key Findings

1
Computational models have improved the understanding of direct neuromodulatory effects of deep brain stimulation (DBS), suggesting that DBS provides symptom relief by creating an informational lesion in the brain.
2
Patient-specific computational models of DBS can select stimulation parameters that provide superior clinical efficacy compared to standard clinical practice.
3
Computational models are increasingly used in software platforms and embedded in medical devices, serving as clinical decision support tools.

Research Summary

Computational models have significantly impacted neurostimulation, aiding in pain and movement recovery, deep brain stimulation, and device regulations. These models serve as virtual testing platforms, helping to understand interactions between neuromodulation technologies and the nervous system and accelerating clinical developments. The FDA is integrating computational modeling into the medical device regulatory approval process, highlighting the importance of these models in advancing and regulating neurostimulation technologies.

Practical Implications

Improved Therapy Design

Computational models can help design more effective and targeted neurostimulation therapies by providing insights into the mechanisms of action and optimizing stimulation parameters.

Accelerated Regulatory Approval

The FDA's integration of computational modeling into the regulatory process can lead to faster and more efficient approvals of medical devices without compromising patient safety.

Personalized Medicine

Patient-specific computational models can enable the development of personalized neurostimulation therapies that account for individual anatomical and physiological variations.

Study Limitations

1
Models are only valid within certain parameter ranges.
2
Model validation is limited by the availability and accuracy of experimental data.
3
Our understanding of specific neural systems limits the definition of constraints.

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