IEEE Int Conf Rehabil Robot. 2022 Jul;2022:1-6. doi: 10.1109/ICORR55369.2022.9896517.
The efficacy of trans-spinal direct current stimulation (tsDCS) as neurorehabilitation technology remains sub-optimal, partly due to the variability introduced by subject-specific neurophysiological features and stimulation conditions (e.g. electrode placement, stimulating amplitude, polarity, etc.) Hence, current therapies apply tsDCS in an open-loop fashion, resulting in a lack of standardized protocols for controlling elicited neuronal adaptations in closed-loop. Through the combination of high-density electromyogram (HD-EMG) decomposition, biophysical neuronal modelling and metaheuristic optimization, this work presents a novel neural data-driven framework for estimating subject-specific features and quantifying acute neuronal adaptations elicited by tsDCS on incomplete spinal cord injury subjects. This approach consists of calibrating the anatomical parameters (e.g. soma diameter) of in silico $\alpha-$motoneuron (MN) models for firing similarly to in vivo MNs decoded from HD-EMG. Assuming that cathodal-tsDCS elicits excitability changes in the MN pool, while preserving their anatomical parameters, optimization of an excitability gain common to the entire pool was performed to minimize discrepancies in firing rate and recruitment time between in vivo and in silico MNs after cathodal-tsDCS. This quantification of excitability changes on MN models calibrated in a person specific way enables closing the loop with neuro-modulation devices to tailor neurorehabilitation therapies. Clinical Relevance - This framework addresses a key limitation in non-invasive neuro-modulative technologies via a novel model-assisted framework that enables quantifying acute excitability changes induced on a person-specific in silico MN pool calibrated using in vivo neural data. This will enable the development of advanced controllers for modulating targeted neuronal adaptations in closed-loop.
经颅直流电刺激(trans-spinal direct current stimulation, tsDCS)作为神经康复技术的疗效仍然不尽如人意，部分原因是由于个体神经生理特征和刺激条件（例如电极放置、刺激幅度、极性等）的可变性。因此，目前的治疗方法以开环方式应用 tsDCS，导致缺乏用于闭环控制诱发电导神经元适应性的标准化方案。通过高密度肌电图 (high-density electromyogram, HD-EMG) 分解、生物物理神经元建模和元启发式优化的结合，这项工作提出了一种新的神经数据驱动框架，用于估计个体特征并量化不完全性脊髓损伤受试者中 tsDCS 诱发的急性神经元适应性。该方法包括校准计算机模型中α运动神经元（motor neuron, MN）的解剖参数（例如胞体直径），使其放电方式与从 HD-EMG 解码的体内 MN 相似。假设阴极 tsDCS 诱发电导神经元池的兴奋性变化，同时保持其解剖参数，对整个池的兴奋性增益进行优化，以最小化阴极 tsDCS 后体内和计算机 MN 之间的放电率和募集时间差异。这种对个体特异性校准的 MN 模型的兴奋性变化的量化，使得能够用神经调节设备闭环，以定制神经康复治疗。临床意义 - 该框架通过一种新的模型辅助框架解决了非侵入性神经调节技术的一个关键限制，该框架能够量化使用体内神经数据校准的个体特异性计算机 MN 池上诱导的急性兴奋性变化。这将能够开发用于在闭环中调节靶向神经元适应性的先进控制器。
