Kyle Loizos

A multi-scale computational model for the study of retinal prosthetic stimulation

An implantable retinal prosthesis has been developed to restore vision to patients who have been blinded by degenerative diseases that destroy photoreceptors. By electrically stimulating the surviving retinal cells, the damaged photoreceptors may be bypassed and limited vision can be restored. While this has been shown to restore partial vision, the understanding of how cells react to this systematic electrical stimulation is largely unknown. Better predictive models and a deeper understanding of neural responses to electrical stimulation is necessary for designing a successful prosthesis. In this work, a computational model of an epi-retinal implant was built and simulated, spanning multiple spatial scales, including a large-scale model of the retina and implant electronics, as well as underlying neuronal networks.

A large-scale detailed neuronal model of electrical stimulation of the dentate gyrus and perforant path as a platform for electrode design and optimization

Owing to the dramatic rise in treatment of neurological disorders with electrical micro-stimulation it has become apparent that the major technological limitation in deploying effective devices lies in the process of designing efficient, safe, and outcome specific electrode arrays. The time-consuming and low-fidelity nature of gathering test data using experimental means and the immense control and flexibility of computational models, has prompted us and others to build models of electrical stimulation of neural networks that can be simulated in a computer. Because prior work has been focused on single cells, very small networks, or non-biological models of neural tissue, it was expedient that we take advantage of our, 4,040 processor, computing cluster to construct a large-scale 3-dimensional emulation of hippocampal tissue using detailed neuronal models with explicit and unique morphologies. This model, when paired with an equivalent circuit method of estimating voltage signal attenuation throughout anisotropic resistive tissue, can be used to predict tissue response to an exhaustive set of stimulation and tissue conditions: electrode geometry, array geometry, static dielectric properties of tissue, stimulation pulse features, etc. Preliminary experiments demonstrate that this system is capable of yielding neuronal responses with striking similarities to experimental results. This work provides an avenue to qualitative evaluation of electrode arrays, and more meaningful modeling of local field potentials in terms of their contributing sources and sinks.

Increasing Electrical Stimulation Efficacy in Degenerated Retina: Stimulus Waveform Design in a Multiscale Computational Model

A computational model of electrical stimulation of the retina is proposed for investigating current waveforms used in prosthetic devices for restoring partial vision lost to retinal degenerative diseases. The model framework combines a connectome-based neural network model characterized by accurate morphological and synaptic properties with an admittance method model of bulk tissue and prosthetic electronics. In this model, the retina was computationally “degenerated,” considering cellular death and anatomical changes that occur early in disease, as well as altered neural behavior that develops throughout the neurodegeneration and is likely interfering with current attempts at restoring vision. A resulting analysis of stimulation range and threshold of ON ganglion cells within the retina that are either healthy or in beginning stages of degeneration is presented for currently used stimulation waveforms, and an asymmetric biphasic current stimulation for subduing spontaneous firing to allow increased control over ganglion cell firing patterns in degenerated retina is proposed. Results show that stimulation thresholds of retinal ganglion cells do not notably vary after beginning stages of retina degeneration. In addition, simulation of proposed asymmetric waveforms showed the ability to enhance the control of ganglion cell firing via electrical stimulation.

Model-Based Analysis of Electrode Placement and Pulse Amplitude for Hippocampal Stimulation

Objective: The ideal form of a neural-interfacing device is highly dependent upon the anatomy of the region with which it is meant to interface. Multiple-electrode arrays provide a system that can be adapted to various neural geometries. Computational models of stimulating systems have proven useful for evaluating electrode placement and stimulation protocols, but have yet to be adequately adapted to the unique features of the hippocampus. Methods: As an approach to understanding potential memory restorative devices, an admittance method-NEURON model was constructed to predict the direct and synaptic response of a region of the rat dentate gyrus to electrical stimulation of the perforant path. Results: A validation of estimated local field potentials against experimental recordings is performed and results of a bilinear electrode placement and stimulation amplitude parameter search are presented. Conclusion: The parametric analysis presented herein suggests that stimulating electrodes placed between the lateral and medial perforant path, near the crest of the dentate gyrus, yield a larger relative population response to given stimuli. Significance: Beyond deepening understanding of the hippocampal tissue system, establishment of this model provides a method to evaluate candidate stimulating devices and protocols.

On the computation of a retina resistivity profile for applications in multi-scale modeling of electrical stimulation and absorption.

This study proposes a methodology for computationally estimating resistive properties of tissue in multi-scale computational models, used for studying the interaction of electromagnetic fields with neural tissue, with applications to both dosimetry and neuroprosthetics. Traditionally, models at bulk tissue- and cellular-level scales are solved independently, linking resulting voltage from existing resistive tissue-scale models as extracellular sources to cellular models. This allows for solving the effects that external electric fields have on cellular activity. There are two major limitations to this approach: first, the resistive properties of the tissue need to be chosen, of which there are contradicting measurements in literature; second, the measurements of resistivity themselves may be inaccurate, leading to the mentioned contradicting results found across different studies. Our proposed methodology allows for constructing computed resistivity profiles using knowledge of only the neural morphology within the multi-scale model, resulting in a practical implementation of the effective medium theory; this bypasses concerns regarding the choice of resistive properties and accuracy of measurement setups. A multi-scale model of retina is constructed with an external electrode to serve as a test bench for analyzing existing and resulting resistivity profiles, and validation is presented through the reconstruction of a published resistivity profile of retina tissue. Results include a computed resistivity profile of retina tissue for use with a retina multi-scale model used to analyze effects of external electric fields on neural activity.

Optimizing electrode placement using a multiscale model of the hippocampus

The hippocampus is associated with consolidating short-term memory into long-term memory. Therefore, damage to the hippocampus can result in neurological conditions such as Alzheimers, dementia, and other diseases that affect memory. One way of helping patients affected by these conditions is to create a neural prosthesis that replicates the function of the damaged section of the hippocampus. This prosthetic device is built by a) creating an input-output model of the transformation between the still-intact portions of the hippocampus, and b) “instantiating” that model into custom VLSI hardware thats attached to upstream recording electrodes and downstream stimulating electrodes. However, a phenomenon thats still not well understood is the neural response to the electrical stimulation: thus, a multi-scale computational model has been developed to study the response to biphasic stimulation in a rat hippocampus.

Estimation of initiated local field potential by neurons in heterogeneous tissue environment using admittance method

In order to decode the relationship between the activity of neuronal networks in the brain and the physiological response, multiple recording methods are available to monitor the spatial-temporal response of the neurons. Compared to single neuron recording that is useful for finding neuronal response, local field potential (LFP) recording allows for analysis of a larger neuronal network response. LFP is a combinational effect of the asynchronous action potentials from multiple neurons and can be recorded using implanted microelectrodes. Accurately calculating the LFP of a simulated neuronal network allows for optimal electrode placement for electrical stimulation and recording. The simulated LFP on a proximal recording electrode due to the neuronal firings of a multineuron network can be compared with the recorded LFP from a relevant experiment. In this work we compare two different methods for solving multineuron network field potentials.

Simulation study for estimating effective resistivity in heterogenenous neural tissues

Electromagnetic numerical methods, such as the finite element method (FEM), admittance method (AM), or finite-difference time-domain (FDTD), often use models that are discretized based on the dielectric properties of included materials. When studying neural tissue, the values typically originate from measurements taken across bulk tissue. These are then used to describe the materials in the model as lumped circuital elements, converting each voxel into a homogeneous resistance. While this approach can provide a good estimate of the resistance of the tissue at macro-scales, it may be questionable when voxel resolutions down to 1–5 um are considered. At these voxel resolutions, each voxel may contain different sections of cell bodies, axons, dendritic regions, etc., each having different resistive properties. There is an inherent heterogeneity that is disregarded in previous homogeneous model approximations.

A 3-D admittance-level computational model of a rat hippocampus for improving prosthetic design

Hippocampal prosthetic devices have been developed to bridge the gap between functioning portions of the hippocampus, in order to restore lost memory functionality in those suffering from brain injury or diseases. One approach taken in recent neuroprosthetic design is to use a multi-input, multi-output device that reads data from the CA3 in the hippocampus and electrically stimulates the CA1 in an attempt to mimic the appropriate firing pattern that would occur naturally between the two areas. However, further study needs to be conducted in order to optimize electrode placement, pulse magnitude, and shape for creating the appropriate firing pattern. This paper describes the creation and implementation of an anatomically correct 3D model of the hippocampus to simulate the electric field patterns and axonal activation from electrical stimulation due to an implanted electrode array. The activating function was applied to the voltage results to determine the firing patterns in possible axon locations within the CA1.