SCI Publications

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the surgical therapy of choice for medically intractable Parkinson's disease. However, quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is limited. Recently, computational models of varying levels of complexity have been used to study the neural response to DBS. The goal of this study was to evaluate the quantitative impact of incrementally incorporating increasing levels of complexity into computer models of STN DBS. Our analysis focused on the direct activation of experimentally measureable fiber pathways within the internal capsule (IC). Our model system was customized to an STN DBS patient and stimulation thresholds for activation of IC axons were calculated with electric field models that ranged from an electrostatic, homogenous, isotropic model to one that explicitly incorporated the voltage-drop and capacitance of the electrode-electrolyte interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to experimental IC activation defined from electromyographic (EMG) recordings from eight different muscle groups in the contralateral arm and leg of the STN DBS patient. Coupled evaluation of the model and experimental data showed that the most realistic predictions of axonal thresholds were achieved with the most detailed model. Furthermore, the more simplistic neurostimulation models substantially overestimated the spatial extent of neural activation.

Keywords: Action Potentials, Action Potentials: physiology, Computer Simulation, Deep Brain Stimulation, Deep Brain Stimulation: instrumentation, Deep Brain Stimulation: methods, Humans, Male, Middle Aged, Models, Neurological, Parkinson Disease, Parkinson Disease: therapy, Subthalamic Nucleus, Subthalamic Nucleus: physiology

OBJECTIVE: Despite the clinical success of deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD), little is known about the electrical spread of the stimulation. The primary goal of this study was to integrate neuroimaging, neurophysiology and neurostimulation data sets from 10 patients with PD, unilaterally implanted with subthalamic nucleus (STN) DBS electrodes, to identify the theoretical volume of tissue activated (VTA) by clinically defined therapeutic stimulation parameters.

METHODS: Each patient specific model was created with a series of five steps: (1) definition of the neurosurgical stereotactic coordinate system within the context of preoperative imaging data; (2) entry of intraoperative microelectrode recording locations from neurophysiologically defined thalamic, subthalamic and substantia nigra neurons into the context of the imaging data; (3) fitting a three dimensional brain atlas to the neuroanatomy and neurophysiology of the patient; (4) positioning the DBS electrode in the documented stereotactic location, verified by postoperative imaging data; and (5) calculation of the VTA using a diffusion tensor based finite element neurostimulation model.

RESULTS: The patient specific models show that therapeutic benefit was achieved with direct stimulation of a wide range of anatomical structures in the subthalamic region. Interestingly, of the five patients exhibiting a greater than 40\% improvement in their Unified PD Rating Scale (UPDRS), all but one had the majority of their VTA outside the atlas defined borders of the STN. Furthermore, of the five patients with less than 40\% UPDRS improvement, all but one had the majority of their VTA inside the STN.

CONCLUSIONS: Our results are consistent with previous studies suggesting that therapeutic benefit is associated with electrode contacts near the dorsal border of the STN, and provide quantitative estimates of the electrical spread of the stimulation in a clinically relevant context.

Keywords: Brain Mapping, Brain Mapping: methods, Cerebral,Cerebral: physiology, Computer-Assisted, Computer-Assisted: methods, Deep Brain Stimulation, Deep Brain Stimulation: methods, Diffusion Magnetic Resonance Imaging, Diffusion Magnetic Resonance Imaging: methods, Dominance, Electrodes, Humans, Image Processing, Imaging, Implanted, Magnetic Resonance Imaging, Magnetic Resonance Imaging: methods, Nerve Net, Nerve Net: physiopathology, Neurologic Examination, Neurons, Neurons: physiology, Parkinson Disease, Parkinson Disease: physiopathology, Parkinson Disease: therapy, Substantia Nigra, Substantia Nigra: physiopathology, Subthalamic Nucleus, Subthalamic Nucleus: physiopathology, Synaptic Transmission, Synaptic Transmission: physiology, Thalamus, Thalamus: physiopathology, Three-Dimensional, Tomography, Treatment Outcome, X-Ray Computed, X-Ray Computed: methods

Deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease and shows great promise for numerous other disorders. While the fundamental purpose of DBS is to modulate neural activity with electric fields, little is known about the actual voltage distribution generated in the brain by DBS electrodes and as a result it is difficult to accurately predict which brain areas are directly affected by the stimulation. The goal of this study was to characterize the spatial and temporal characteristics of the voltage distribution generated by DBS electrodes. We experimentally recorded voltages around active DBS electrodes in either a saline bath or implanted in the brain of a non-human primate. Recordings were made during voltage-controlled and current-controlled stimulation. The experimental findings were compared to volume conductor electric field models of DBS parameterized to match the different experiments. Three factors directly affected the experimental and theoretical voltage measurements: 1) DBS electrode impedance, primarily dictated by a voltage drop at the electrode-electrolyte interface and the conductivity of the tissue medium, 2) capacitive modulation of the stimulus waveform, and 3) inhomogeneity and anisotropy of the tissue medium. While the voltage distribution does not directly predict the neural response to DBS, the results of this study do provide foundational building blocks for understanding the electrical parameters of DBS and characterizing its effects on the nervous system.

In a companion paper we showed that random channel and synaptic noise improve the ability of a biologically realistic, GENESIS-based computational model of the Hermissenda eye to encode light intensity. In this paper we explore mechanisms for noise-induced improvement by examining contextual spike-timing relationships among neurons in the photoreceptor network. In other systems, synaptically connected pairs of spiking cells can develop phase-locked spike-timing relationships at particular, well-defined frequencies. Consequently, domains of stability (DOS) emerge in which an increase in the frequency of inhibitory postsynaptic potentials can paradoxically increase, rather than decrease, the firing rate of the postsynaptic cell. We have extended this analysis to examine DOS as a function of noise amplitude in the exclusively inhibitory Hermissenda photoreceptor network. In noise-free simulations, DOS emerge at particular firing frequencies of type B and type A photoreceptors, thus producing a nonmonotonic relationship between their firing rates and light intensity. By contrast, in the noise-added conditions, an increase in noise amplitude leads to an increase in the variance of the interspike interval distribution for a given cell; in turn, this blocks the emergence of phase locking and DOS. These noise-induced changes enable the eye to better perform one of its basic tasks: encoding light intensity. This effect is independent of stochastic resonance, which is often used to describe perithreshold stimuli. The constructive role of noise in biological signal processing has implications both for understanding the dynamics of the nervous system and for the design of neural interface devices.

Keywords: Action Potentials, Action Potentials: physiology, Animals, Artifacts, Computer Simulation, Eye, Eye: cytology, Hermissenda, Hermissenda: physiology, Invertebrate, Invertebrate: physiology, Nerve Net, Nerve Net: physiology, Neurons, Neurons: physiology, Ocular, Ocular Physiological Phenomena, Ocular: physiology, Photic Stimulation, Photoreceptor Cells, Reaction Time, Reaction Time: physiology, Sensory Thresholds, Sensory Thresholds: physiology, Synaptic Transmission, Synaptic Transmission: physiology, Vision

Neurons are notoriously noisy devices. Although the traditional view posits that noise degrades system performance, recent evidence suggests that noise may instead enhance neural information processing under certain conditions. Here we report that random channel and synaptic noise improve the ability of a biologically realistic computational model of the Hermissenda eye to encode light intensity. The model was created in GENESIS and is based on a previous model used to examine effects of changes in type B photoreceptor excitability, synaptic strength, and network architecture. The network consists of two type A and three type B multicompartmental photoreceptors. Each compartment contains a population of Hodgkin-Huxley-type ion channels and each cell is stimulated via artificial light currents. We found that the addition of random channel and synaptic noise yielded a significant improvement in the accuracy of the network's encoding of light intensity across eight light levels spanning 3.5 log units (P

Keywords: Action Potentials, Action Potentials: physiology, Animals, Artifacts, Computer Simulation, Eye, Eye: cytology, Hermissenda, Hermissenda: physiology, Invertebrate, Invertebrate: physiology, Nerve Net, Nerve Net: physiology, Neurons, Neurons: physiology, Ocular, Ocular Physiological Phenomena, Ocular: physiology, Photic Stimulation, Photoreceptor Cells, Reaction Time, Reaction Time: physiology, Sensory Thresholds, Sensory Thresholds: physiology, Synaptic Transmission, Synaptic Transmission: physiology, Vision

BACKGROUND: Over the last two decades, deep brain stimulation (DBS) has become a recognized and effective clinical therapy for numerous neurological conditions. Since its inception, clinical DBS technology has progressed at a relatively slow rate; however, advances in neural engineering research have the potential to improve DBS systems. One such advance is the concept of current steering, or the use of multiple stimulation sources to direct current flow through targeted regions of brain tissue.

OBJECTIVE: The goals of this study were to develop a theoretical understanding of the effects of current steering in the context of DBS, and use that information to evaluate the potential utility of current steering during stimulation of the subthalamic nucleus.

METHODS: We used finite element electric field models, coupled to multi-compartment cable axon models, to predict the volume of tissue activated (VTA) by DBS as a function of the stimulation parameter settings.

RESULTS: Balancing current flow through adjacent cathodes increased the VTA magnitude, relative to monopolar stimulation, and current steering enabled us to sculpt the shape of the VTA to fit a given anatomical target.

CONCLUSIONS: These results provide motivation for the integration of current steering technology into clinical DBS systems, thereby expanding opportunities to customize DBS to individual patients, and potentially enhancing therapeutic efficacy.

OBJECTIVE: Two different Medtronic implantable pulse generator (IPG) models are currently used in clinical applications of deep brain stimulation (DBS): Soletra and Kinetra. The goal of this study was to evaluate and compare the stimulation waveforms produced by each IPG model.

METHODS: We recorded waveforms from a broad range of stimulation parameter settings in each IPG model, and compared them to idealized waveforms that adhered to the parameters specified in the programming device. We then used a previously published computational model to predict the neural response to the various stimulation waveforms.

RESULTS: The stimulation waveforms produced by the IPGs differed from the idealized waveforms assumed in previous theoretical and clinical studies, and the waveforms differed among the IPG models. These differences were greater at higher frequencies and longer pulse widths, and caused variations of up to 0.4 V in activation thresholds for model axons located 3 mm from the DBS electrode contact.

CONCLUSIONS: The specific details of the stimulation waveform directly affect the neural response to DBS and should be accounted for in theoretical and experimental studies of DBS.

SIGNIFICANCE: While the clinical selection of DBS parameters is individualized to each patient based on behavioral outcomes, scientific analysis of stimulation parameter settings and clinical threshold measurements are subject to a previously unrecognized source of error unless the actual waveforms produced by the IPG are accounted for.

Despite the clinical success of deep brain stimulation (DBS) for the treatment of movement disorders, many questions remain about its effects on the nervous system. This study presents a methodology to predict the volume of tissue activated (VTA) by DBS on a patient-specific basis. Our goals were to identify the intersection between the VTA and surrounding anatomical structures and to compare activation of these structures with clinical outcomes. The model system consisted of three fundamental components: (1) a 3D anatomical model of the subcortical nuclei and DBS electrode position in the brain, each derived from magnetic resonance imaging (MRI); (2) a finite element model of the DBS electrode and electric field transmitted to the brain, with tissue conductivity properties derived from diffusion tensor MRI; (3) VTA prediction derived from the response of myelinated axons to the applied electric field, which is a function of the stimulation parameters (contact, impedance, voltage, pulse width, frequency). We used this model system to analyze the effects of subthalamic nucleus (STN) DBS in a patient with Parkinson's disease. Quantitative measurements of bradykinesia, rigidity, and corticospinal tract (CST) motor thresholds were evaluated over a range of stimulation parameter settings. Our model predictions showed good agreement with CST thresholds. Additionally, stimulation through electrode contacts that improved bradykinesia and rigidity generated VTAs that overlapped the zona incerta/fields of Forel (ZI/H2). Application of DBS technology to various neurological disorders has preceded scientific characterization of the volume of tissue directly affected by the stimulation. Synergistic integration of clinical analysis, neuroimaging, neuroanatomy, and neurostimulation modeling provides an opportunity to address wide ranging questions on the factors linked with the therapeutic benefits and side effects of DBS.

Keywords: Deep Brain Stimulation, Finite Element Analysis, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Models, Neurological, Parkinson Disease, Parkinson Disease: therapy, Pyramidal Tracts, Pyramidal Tracts: physiology, Subthalamic Nucleus, Subthalamic Nucleus: physiology

OBJECTIVE: Clinical impedance measurements for deep brain stimulation (DBS) electrodes in human patients are normally in the range 500-1500 Omega. DBS devices utilize voltage-controlled stimulation; therefore, the current delivered to the tissue is inversely proportional to the impedance. The goals of this study were to evaluate the effects of various electrical properties of the tissue medium and electrode-tissue interface on the impedance and to determine the impact of clinically relevant impedance variability on the volume of tissue activated (VTA) during DBS.

METHODS: Axisymmetric finite-element models (FEM) of the DBS system were constructed with explicit representation of encapsulation layers around the electrode and implanted pulse generator. Impedance was calculated by dividing the stimulation voltage by the integrated current density along the active electrode contact. The models utilized a Fourier FEM solver that accounted for the capacitive components of the electrode-tissue interface during voltage-controlled stimulation. The resulting time- and space-dependent voltage waveforms generated in the tissue medium were superimposed onto cable model axons to calculate the VTA.

RESULTS: The primary determinants of electrode impedance were the thickness and conductivity of the encapsulation layer around the electrode contact and the conductivity of the bulk tissue medium. The difference in the VTA between our low (790 Omega) and high (1244 Omega) impedance models with typical DBS settings (-3 V, 90 mus, 130 Hz pulse train) was 121 mm3, representing a 52\% volume reduction. CONCLUSIONS: Electrode impedance has a substantial effect on the VTA and accurate representation of electrode impedance should be an explicit component of computational models of voltage-controlled DBS.

SIGNIFICANCE: Impedance is often used to identify broken leads (for values > 2000 Omega) or short circuits in the hardware (for values during DBS.

Keywords: Brain, Brain: physiology, Computer Simulation, Deep Brain Stimulation, Electric Conductivity, Electric Impedance, Electrodes, Humans, Imaging, Models, Neurological, Three-Dimensional

Deep brain stimulation (DBS) is an established clinical treatment for a range of neurological disorders. Depending on the disease state of the patient, different anatomical structures such as the ventral intermediate nucleus of the thalamus (VIM), the subthalamic nucleus or the globus pallidus are targeted for stimulation. However, the same electrode design is currently used in nearly all DBS applications, even though substantial morphological and anatomical differences exist between the various target nuclei. The fundamental goal of this study was to develop a theoretical understanding of the impact of changes in the DBS electrode contact geometry on the volume of tissue activated (VTA) during stimulation. Finite element models of the electrodes and surrounding medium were coupled to cable models of myelinated axons to predict the VTA as a function of stimulation parameter settings and electrode design. Clinical DBS electrodes have cylindrical contacts 1.27 mm in diameter (d) and 1.5 mm in height (h). Our results show that changes in contact height and diameter can substantially modulate the size and shape of the VTA, even when contact surface area is preserved. Electrode designs with a low aspect ratio (d/h) maximize the VTA by providing greater spread of the stimulation parallel to the electrode shaft without sacrificing lateral spread. The results of this study provide the foundation necessary to customize electrode design and VTA shape for specific anatomical targets, and an example is presented for the VIM. A range of opportunities exist to engineer DBS systems to maximize stimulation of the target area while minimizing stimulation of non-target areas. Therefore, it may be possible to improve therapeutic benefit and minimize side effects from DBS with the design of target-specific electrodes.

Keywords: Animals, Brain, Brain: physiology, Computer Simulation, Computer-Aided Design, Deep Brain Stimulation, Deep Brain Stimulation: instrumentation, Deep Brain Stimulation: methods, Electrodes, Equipment Design, Equipment Design: methods, Equipment Failure Analysis, Equipment Failure Analysis: methods, Humans, Implanted, Microelectrodes, Models, Neurological, Neurons, Neurons: physiology, Organ Size, Organ Size: physiology

OBJECTIVE: The growing clinical acceptance of neurostimulation technology has highlighted the need to accurately predict neural activation as a function of stimulation parameters and electrode design. In this study we evaluate the effects of the tissue and electrode capacitance on the volume of tissue activated (VTA) during deep brain stimulation (DBS).

METHODS: We use a Fourier finite element method (Fourier FEM) to calculate the potential distribution in the tissue medium as a function of time and space simultaneously for a range of stimulus waveforms. The extracellular voltages are then applied to detailed multi-compartment cable models of myelinated axons to determine neural activation. Neural activation volumes are calculated as a function of the stimulation parameters and magnitude of the capacitive components of the electrode-tissue interface.

RESULTS: Inclusion of either electrode or tissue capacitance reduces the VTA compared to electrostatic simulations in a manner dependent on the capacitance magnitude and the stimulation parameters (amplitude and pulse width). Electrostatic simulations with typical DBS parameter settings (-3 V or -3 mA, 90 micros, 130 Hz) overestimate the VTA by approximately 20\% for voltage- or current-controlled stimulation. In addition, strength-duration time constants decrease and more closely match clinical measurements when explicitly accounting for the effects of voltage-controlled stimulation.

CONCLUSIONS: Attempts to quantify the VTA from clinical neurostimulation devices should account for the effects of electrode and tissue capacitance.

SIGNIFICANCE: DBS has rapidly emerged as an effective treatment for movement disorders; however, little is known about the VTA during therapeutic stimulation. In addition, the influence of tissue and electrode capacitance has been largely ignored in previous models of neural stimulation. The results and methodology of this study provide the foundation for the quantitative analysis of the VTA during clinical neurostimulation.

Keywords: Algorithms, Axons, Axons: physiology, Computer Simulation, Deep Brain Stimulation, Electric Capacitance, Electrodes, Extracellular Space, Extracellular Space: physiology, Finite Element Analysis, Implanted, Models, Myelinated, Myelinated: physiology, Nerve Fibers, Neurological, Neurons, Neurons: physiology, Poisson Distribution, Statistical