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Selective neural stimulation by leveraging electrophysiological differentiation and using pre-pulsing and non-rectangular waveforms

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Abstract

Efforts on selective neural stimulation have concentrated on segregating axons based on their size and geometry. Nonetheless, axons of the white matter or peripheral nerves may also differ in their electrophysiological properties. The primary objective of this study was to investigate the possibility of selective activation of axons by leveraging an assumed level of diversity in passive (Cm & Gleak) and active membrane properties (Ktemp & Gnamax). First, the stimulus waveforms with hyperpolarizing (HPP) and depolarizing pre-pulsing (DPP) were tested on selectivity in a local membrane model. The default value of membrane capacitance (Cm) was found to play a critical role in sensitivity of the chronaxie time (Chr) and rheobase (Rhe) to variations of all the four membrane parameters. Decreasing the default value of Cm, and thus the passive time constant of the membrane, amplified the sensitivity to the active parameters, Ktemp and GNamax, on Chr. The HPP waveform could selectively activate neurons even if they were diversified by membrane leakage (Gleak) only, and produced higher selectivity than DPP when parameters are varied in pairs. Selectivity measures were larger when the passive parameters (Cm & Gleak) were varied together, compared to the active parameters. Second, this novel mechanism of selectivity was investigated with non-rectangular waveforms for the stimulating phase (and HPP) in the same local membrane model. Simulation results suggest that Kt2 is the most selective waveform followed by Linear and Gaussian waveforms. Traditional rectangular pulse was among the least selective of all. Finally, a compartmental axon model confirmed the main findings of the local model that Kt2 is the most selective, but rank ordered the other waveforms differently. These results suggest a potentially novel mechanism of stimulation selectivity, leveraging electrophysiological variations in membrane properties, that can lead to various neural prosthetic applications.

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Authors and Affiliations

  1. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Bemin Ghobreal & Mesut Sahin

  2. Department of Biology, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Farzan Nadim

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  1. Bemin Ghobreal
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  2. Farzan Nadim
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Ghobreal, B., Nadim, F. & Sahin, M. Selective neural stimulation by leveraging electrophysiological differentiation and using pre-pulsing and non-rectangular waveforms. J Comput Neurosci 50, 313–330 (2022). https://doi.org/10.1007/s10827-022-00818-8

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  • DOI: https://doi.org/10.1007/s10827-022-00818-8

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