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Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

ABSTRACT: Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.

SUBMITTER: Wang H

PROVIDER: S-EPMC7381307 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

Wang Hao H Wang Jiahui J Thow Xin Yuan XY Lee Sanghoon S Peh Wendy Yen Xian WYX Ng Kian Ann KA He Tianyiyi T Thakor Nitish V NV Lee Chengkuo C

Frontiers in computational neuroscience 20200710

Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be the ...[more]

PMID: 32754023

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Project description:Stimulation of muscle for research or clinical interventions is often superimposed on ongoing physiological activity without a quantitative understanding of the impact of the stimulation on the net muscle activity and the physiological response. Experimental studies show that total force during stimulation is less than the sum of the isolated voluntary and stimulated forces, but the occlusion mechanism is not understood.We develop a model of efferent motor activity elicited by superimposing stimulation during a physiologically activated contraction. The model combines action potential interactions due to collision block, source resetting, and refractory periods with previously published models of physiological motor unit recruitment, rate modulation, force production, and EMG generation in human first dorsal interosseous muscle to investigate the mechanisms and effectiveness of stimulation on the net muscle force and EMG.Stimulation during a physiological contraction demonstrates partial occlusion of force and the neural component of the EMG, due to action potential interactions in motor units activated by both sources. Depending on neural and stimulation firing rates as well as on force-frequency properties, individual motor unit forces can be greater, smaller, or unchanged by the stimulation. In contrast, voluntary motor unit EMG potentials in simultaneously stimulated motor units show progressive occlusion with increasing stimulus rate. The simulations predict that occlusion would be decreased by a reverse stimulation recruitment order.The results are consistent with and provide a mechanistic interpretation of previously published experimental evidence of force occlusion. The models also predict two effects that have not been reported previously--voluntary EMG occlusion and the advantages of a proximal stimulation site. This study provides a basis for the rational design of both future experiments and clinical neuroprosthetic interventions involving either motor or sensory stimulation.

Dataset Information

Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

Publications

Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

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