Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

Mathematical methods for modeling the microcirculation

The microcirculation plays a major role in maintaining homeostasis in the body. Alterations or dysfunctions of the microcirculation can lead to several types of serious diseases. It is not surprising, then, that the microcirculation has been an object of intense theoretical and experimental study over the past few decades. Mathematical approaches offer a valuable method for quantifying the relationships between various mechanical, hemodynamic, and regulatory factors of the microcirculation and the pathophysiology of numerous diseases. This work provides an overview of several mathematical models that describe and investigate the many different aspects of the microcirculation, including geometry of the vascular bed, blood flow in the vascular networks, solute transport and delivery to the surrounding tissue, and vessel wall mechanics under passive and active stimuli. Representing relevant phenomena across multiple spatial scales remains a major challenge in modeling the microcirculation. Nevertheless, the depth and breadth of mathematical modeling with applications in the microcirculation is demonstrated in this work. A special emphasis is placed on models of the retinal circulation, including models that predict the influence of ocular hemodynamic alterations with the progression of ocular diseases such as glaucoma

Cell anatomy and network input explain differences within but not between leech touch cells at two different locations

Mechanosensory cells in the leech share several common features with mechanoreceptors in the human glabrous skin. Previous studies showed that the six T (touch) cells in each body segment of the leech are highly variable in their responses to somatic current injection and change their excitability over time. Here, we investigate three potential reasons for this variability in excitability by comparing the responses of T cells at two soma locations (T2 and T3): (1) Differential effects of time-dependent changes in excitability, (2) divergent synaptic input from the network, and (3) different anatomical structures. These hypotheses were explored with a combination of electrophysiological double recordings, 3D reconstruction of neurobiotin-filled cells, and compartmental model simulations. Current injection triggered significantly more spikes with shorter latency and larger amplitudes in cells at soma location T2 than at T3. During longer recordings, cells at both locations increased their excitability over time in the same way. T2 and T3 cells received the same amount of synaptic input from the unstimulated network, and the polysynaptic connections between both T cells were mutually symmetric. However, we found a striking anatomical difference: While in our data set all T2 cells innervated two roots connecting the ganglion with the skin, 50% of the T3 cells had only one root process. The sub-sample of T3 cells with one root process was significantly less excitable than the T3 cells with two root processes and the T2 cells. To test if the additional root process causes higher excitability, we simulated the responses of 3D reconstructed cells of both anatomies with detailed multi-compartment models. The anatomical subtypes do not differ in excitability when identical biophysical parameters and a homogeneous channel distribution are assumed. Hence, all three hypotheses may contribute to the highly variable T cell responses, but none of them is the only factor accounting for the observed systematic difference in excitability between cells at T2 vs. T3 soma location. Therefore, future patch clamp and modeling studies are needed to analyze how biophysical properties and spatial distribution of ion channels on the cell surface contribute to the variability and systematic differences of electrophysiological phenotypes

Analysis of Factors Affecting the Performance of Retinal Prostheses Using Finite Element Modelling of Electric Field Distribution in the Retina

This dissertation proposes a computational framework targeted at improving the design of currently employed retinal prostheses. The framework was used for analysing factors impacting the performance of prostheses in terms of electrical stimulation for retinal neurons, which might lead to a perception of pixelated vision. Despite their demonstrated effectiveness, the chronic and safe usage of these retinal prostheses in human and animal trials is jeopardised due to high stimulation thresholds. This is related to the distance between the stimulating electrodes and the retinal neurons resulting from the implantation procedure. The major goal of this dissertation was to evaluate the stimulation efficacy in current implantable planar microelectrode-based retinal prostheses and consequently demonstrate their weakness, thereby providing scope for the development of future implants. The effect of geometrical factors i.e., electrode-retina distance and electrode size on stimulation applied to the retina by retinal prostheses was studied. To this end, a finite element method based simulation framework to compute electric field distribution in the retina was constructed. An electrical model of the retina was an integral part of the framework, essentially represented by a resistivity profile of the multi-layered retina. The elements of a retinal prosthesis were modelled by incorporating realistic electrode sizes, an anatomical and electrical model of the retina, a precise positioning of stimulation and return electrodes and the location of the implant with respect to the retina representing the epiretinal and subretinal stimulation schemes. The simulations were carried out both in quasi-static and direct current (DC) modes. It was observed that electrode-electrolyte interface and tissue capacitance could be safely neglected in our model based on the magnitude of the applied voltage stimulus and frequencies under consideration. Therefore, all simulations were conducted in DC mode. Thresholds and lateral extents of the stimulation were computed for electrode sizes corresponding to existing and self-fabricated implants. The values and trends obtained were in agreement with experiments from literature and our collaborators at the les Hôpitaux Universitaires de Genève (HUG). In the subretinal stimulation scheme, the computed variation of impedance with electrode-retina distance correlated well with time varying in vivo impedance measurements in rats conducted in collaboration with the Institut de la Vision, INSERM, Paris. Finally, it was also reiterated that the currently employed retinal prostheses are not very efficient due to a significant distance between the stimulation electrode and the retinal cells. In addition, I present a new experimental technique for measuring the absolute and local resistivity profile in high-resolution along the retinal depth, based on impedance spectroscopy using a bipolar microprobe. This experiment was devised to extract the resistivity profile of an embryonic chick retina to construct an electrical model for the simulation framework to simulate in vitro retinal stimulation experiments conducted by HUG collaborators. We validated the capability of the technique in rat and embryonic chick retinas. In conclusion, the computational framework presented in this dissertation is more realistic than those found in literature, but represents only a preliminary step towards an accurate model of a real implantation scenario in vivo. The simulation results are in agreement with results from clinical trials in humans for epiretinal configuration (literature) and with in vitro results for epiretinal and subretinal stimulation applied to chick retinas (HUG). The developed simulation framework computes quantities that can form a reference for quality control during surgery while inserting implants in the eye and functionality checks by electrophysiologists. Furthermore, this framework is useful in deciding the specifications of stimulation electrodes such as optimal size, shape, material, array density, and the position of the reference electrode to name a few. The work presented here offers to aid in optimising retinal prostheses and implantation procedures for patients and eventually contributes towards improving their quality of life

Advancing treatment of retinal disease through in silico trials

Abstract Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy.&amp;#xD;&amp;#xD;In recent years, the concept of in silico clinical trials has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. In silico clinical trials rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to&amp;#xD;optimise the use of existing therapeutics.&amp;#xD;&amp;#xD;In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing in silico clinical trials. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of in silico clinical trials and identify challenges to developing in silico clinical trials of retinal diseases.</jats:p

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)