Scholarlink

<p>Peripheral nerve stimulation can help restore limb movement after paralysis and enable advanced rehabilitation technologies; however, current extraneural interfaces are typically limited by low fascicle selectivity and laborious functional evaluation. This study has developed an extraneural peripheral nerve interface with high fascicle selectivity, and an AI-facilitated video analysis pipeline for assessing limb movement during neuromodulation. This was achieved by deploying temporal interference stimulation (TIS) in a high-density nerve cuff electrode and by using machine learning algorithms for automated pose estimation and unsupervised behavioral analysis to evaluate movement selectivity and diversity. Using this unbiased semi-automated analysis revealed that TIS elicited more selective motor responses than standard biphasic stimulation, as evidenced by the formation of 1.75 times more distinct movement clusters and behavioral syllables. Furthermore, logit link beta regression modeling showed that TIS had a significantly higher positive effect on movement selectivity (β = 2.75, <i>p</i> &lt; 0.005) compared to biphasic stimulation. Our statistical and machine learning-based analysis provides a computational and objective pipeline for quantifying complex motor outcomes in neuromodulation research. The results suggest that extraneural TIS can be used to generate individually targeted and functionally diverse limb movement patterns and offers a promising approach for neurorehabilitation applications, including restoring movement to individuals living with spinal cord injury.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Temporal interference stimulation of peripheral nerves induces functionally diverse limb movements revealed by automated pose estimation and unsupervised behavioral analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

查看全文
  • Joshua Philippe Olorocisimo,
  • Sudip Nag,
  • Hengjia Zhang,
  • Songyu Yang,
  • Matvii Prytula,
  • Serena Liu,
  • Mustafa Kanchwala,
  • Yinghe Sun,
  • Jose Zariffa,
  • Roman Genov

摘要

Peripheral nerve stimulation can help restore limb movement after paralysis and enable advanced rehabilitation technologies; however, current extraneural interfaces are typically limited by low fascicle selectivity and laborious functional evaluation. This study has developed an extraneural peripheral nerve interface with high fascicle selectivity, and an AI-facilitated video analysis pipeline for assessing limb movement during neuromodulation. This was achieved by deploying temporal interference stimulation (TIS) in a high-density nerve cuff electrode and by using machine learning algorithms for automated pose estimation and unsupervised behavioral analysis to evaluate movement selectivity and diversity. Using this unbiased semi-automated analysis revealed that TIS elicited more selective motor responses than standard biphasic stimulation, as evidenced by the formation of 1.75 times more distinct movement clusters and behavioral syllables. Furthermore, logit link beta regression modeling showed that TIS had a significantly higher positive effect on movement selectivity (β = 2.75, p < 0.005) compared to biphasic stimulation. Our statistical and machine learning-based analysis provides a computational and objective pipeline for quantifying complex motor outcomes in neuromodulation research. The results suggest that extraneural TIS can be used to generate individually targeted and functionally diverse limb movement patterns and offers a promising approach for neurorehabilitation applications, including restoring movement to individuals living with spinal cord injury.