Document Type


Date of Award


Degree Name

Doctor of Philosophy in Biomedical Engineering - (Ph.D.)


Biomedical Engineering

First Advisor

Mesut Sahin

Second Advisor

Farzan Nadim

Third Advisor

Bryan J. Pfister

Fourth Advisor

Sergei Adamovich

Fifth Advisor

Vijayalakshmi Santhakumar


Efforts on finding the principle mechanism for selective neural stimulation have concentrated on segregating the neurons based on their size and other geometric factors. However, neuronal subtypes found in different parts of the nervous system also differ in their electrophysiological properties. The primary objective of this study is to investigate the feasibility of selective activation of neurons by leveraging the diversity seen in passive and active membrane properties.

Using both a local membrane model and an axon model based on the CRRSS, the diversity of electrophysiological properties is simulated by varying four model parameters (membrane leakage-Gleak and capacitance-Cm, temperature coefficient-Ktemp, and maximum sodium conductance-GNamax) by ±25% around their default value. Temperature coefficient is used as a means to alter the opening rate of the sodium channel. Three different stimulus waveforms are implemented to test the effects of hyperpolarizing pre-pulsing (HPP) and depolarizing pre-pulsing (DPP) on selectivity in comparison to monophasic (Mono) waveform.

The default value of Cm is found to play a critical role in amplifying or attenuating the sensitivity of the chronaxie time (Chr) and rheobase (Rhe) to variations in all the membrane parameters. The HPP waveform is able to selectively activate neurons diversified in Gleak only. Maximum selectivity indices are obtained when passive parameters (Cm & Gleak) are allowed to vary. The impact of dynamic parameter (Ktemp and GNamax) diversity increased slightly for the smallest value of Cm. In all cases, the HPP waveforms (with zero inter-phase gap) produce higher selectivity than the other two stimulus waveforms.

These results reveal a novel mechanism of selectivity based on electrophysiological diversity, and it is particularly pronounced with the hyperpolarizing pre-pulsing stimulation waveform. The proposed method of selectivity may lead to a paradigm-shifting approach if the electrophysiological diversity can also segregate neurons into functional subtypes, as evidence suggests in reports from numerous sites in the central nervous systems. This basic concept of selectivity should generalize to more complex neural models, though probably to different extents, that include a voltage-gated fast sodium channel and a leakage current, as in the CRRSS model.

Furthermore, this study expanded the investigation of neural selectivity to include stimulus waveform. Historically, rectangular stimulus pulse has been used in various neural stimulation application, however several limitations reside when using the tradition rectangular pulse to achieve selectivity. Hence, the study investigated using seven different non-rectangular waveforms as the stimulus pulse proceeded with hyperpolarizing pre-pulsing stimulus as a method to improve selectivity. The seven non-rectangular pulses are Charge-discharge curve (Chr-Dis), increasing and decreasing exponential (ExpInc and ExpDec) respectively, Gaussian (Gauss), KT2, Linear (Lin), and sinewave (Sine). Results revealed that Kt^2 maximized selectivity, followed by Gauss, ExpInc, and ExpDec stimulus, when proceeded by hyperpolarizing pre-puls. Furthermore, results showed with higher diversity in neural cells, specifically in GLeak & Ktemp or GLeak & GNamax using Kt^2 allows higher stimulation selectivity between neural cells.

Additionally, to get more realistic results that represents the behavior of neural cell in the human body we expanded the investigation to include a compartmental axon mode. We used a 10 µm myelinated axon that incorporated the CRRSS local model at the nodes of Ranvier that had widths of 1 µm and an inter-nodal distance of 1 mm. A monopolar point electrode was placed 1 mm away from the axon and aligned with its central node. Using all eight stimulus waveforms the SD curves and SIs were found with the same passive and active parameter ranges tested in the local membrane model

The Axon model further confirmed the results obtained from the local model revealing that diversification in the membrane parameters leads to selective neural stimulation. Both model results indicate that the most selective stimulus waveform changes depending on the membrane parameter combination that is allowed to vary, and no single stimulus waveform is the best for all combinations. These simulation results warrant further investigation of the concept of “selectivity based on electrophysiological diversity” using experimental data from real neurons.



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