Optimization of multi-electrode implant configurations and programming for the delivery of non-ablative electric fields in intratumoral modulation therapy.

PURPOSE: Application of low intensity electric fields to interfere with tumor growth is being increasingly recognized as a promising new cancer treatment modality. Intratumoral modulation therapy (IMT) is a developing technology that uses multiple electrodes implanted within or adjacent tumor regions to deliver electric fields to treat cancer. In this study, the determination of optimal IMT parameters was cast as a mathematical optimization problem, and electrode configurations, programming, optimization, and maximum treatable tumor size were evaluated in the simplest and easiest to understand spherical tumor model. The establishment of electrode placement and programming rules to maximize electric field tumor coverage designed specifically for IMT is the first step in developing an effective IMT treatment planning system. METHODS: Finite element method electric field computer simulations for tumor models with 2 to 7 implanted electrodes were performed to quantify the electric field over time with various parameters, including number of electrodes (2 to 7), number of contacts per electrode (1 to 3), location within tumor volume, and input waveform with relative phase shift between 0 and 2π radians. Homogeneous tissue specific conductivity and dielectric values were assigned to the spherical tumor and surrounding tissue volume. In order to achieve the goal of covering the tumor volume with a uniform threshold of 1 V/cm electric field, a custom least square objective function was used to maximize the tumor volume covered by 1 V/cm time averaged field, while maximizing the electric field in voxels receiving less than this threshold. An additional term in the objective function was investigated with a weighted tissue sparing term, to minimize the field to surrounding tissues. The positions of the electrodes were also optimized to maximize target coverage with the fewest number of electrodes. The complexity of this optimization problem including its non-convexity, the presence of many local minima, and the computational load associated with these stochastic based optimizations led to the use of a custom pattern search algorithm. Optimization parameters were bounded between 0 and 2π radians for phase shift, and anywhere within the tumor volume for location. The robustness of the pattern search method was then evaluated with 50 random initial parameter values. RESULTS: The optimization algorithm was successfully implemented, and for 2 to 4 electrodes, equally spaced relative phase shifts and electrodes placed equidistant from each other was optimal. For 5 electrodes, up to 2.5 cm diameter tumors with 2.0 V, and 4.1 cm with 4.0 V could be treated with the optimal configuration of a centrally placed electrode and 4 surrounding electrodes. The use of 7 electrodes allow for 3.4 cm diameter coverage at 2.0 V and 5.5 cm at 4.0 V. The evaluation of the optimization method using 50 random initial parameter values found the method to be robust in finding the optimal solution. CONCLUSIONS: This study has established a robust optimization method for temporally optimizing electric field tumor coverage for IMT, with the adaptability to optimize a variety of parameters including geometrical and relative phase shift configurations

Impact of brain tissue filtering on neurostimulation fields: A modeling study

Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson's disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods.United States. Defense Advanced Research Projects Agency (Contract W31P4Q-09-C-0117)National Institute of Neurological Disorders and Stroke (U.S.) (Award R43NS062530)National Institute of Neurological Disorders and Stroke (U.S.) (Award 1R44NS080632

A population model of deep brain stimulation in movement disorders from circuits to cells

Copyright © 2020 Yousif, Bain, Nandi and Borisyuk.For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson's disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient's brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.Peer reviewe

Computational modelling of neural mechanisms underlying natural speech perception

Humans are highly skilled at the analysis of complex auditory scenes. In particular, the human auditory system is characterized by incredible robustness to noise and can nearly effortlessly isolate the voice of a specific talker from even the busiest of mixtures. However, neural mechanisms underlying these remarkable properties remain poorly understood. This is mainly due to the inherent complexity of speech signals and multi-stage, intricate processing performed in the human auditory system. Understanding these neural mechanisms underlying speech perception is of interest for clinical practice, brain-computer interfacing and automatic speech processing systems. In this thesis, we developed computational models characterizing neural speech processing across different stages of the human auditory pathways. In particular, we studied the active role of slow cortical oscillations in speech-in-noise comprehension through a spiking neural network model for encoding spoken sentences. The neural dynamics of the model during noisy speech encoding reflected speech comprehension of young, normal-hearing adults. The proposed theoretical model was validated by predicting the effects of non-invasive brain stimulation on speech comprehension in an experimental study involving a cohort of volunteers. Moreover, we developed a modelling framework for detecting the early, high-frequency neural response to the uninterrupted speech in non-invasive neural recordings. We applied the method to investigate top-down modulation of this response by the listener's selective attention and linguistic properties of different words from a spoken narrative. We found that in both cases, the detected responses of predominantly subcortical origin were significantly modulated, which supports the functional role of feedback, between higher- and lower levels stages of the auditory pathways, in speech perception. The proposed computational models shed light on some of the poorly understood neural mechanisms underlying speech perception. The developed methods can be readily employed in future studies involving a range of experimental paradigms beyond these considered in this thesis.Open Acces

Using Phase Response Curves to Optimize Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. April 2016. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); vii, 190 pages.Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time