Showing papers by "Warren M. Grill published in 2018"

Biophysically realistic neuron models for simulation of cortical stimulation.

Abstract: Objective We implemented computational models of human and rat cortical neurons for simulating the neural response to cortical stimulation with electromagnetic fields. Approach We adapted model neurons from the library of Blue Brain models to reflect biophysical and geometric properties of both adult rat and human cortical neurons and coupled the model neurons to exogenous electric fields (E-fields). The models included 3D reconstructed axonal and dendritic arbors, experimentally-validated electrophysiological behaviors, and multiple, morphological variants within cell types. Using these models, we characterized the single-cell responses to intracortical microstimulation (ICMS) and uniform E-field with dc as well as pulsed currents. Main results The strength-duration and current-distance characteristics of the model neurons to ICMS agreed with published experimental results, as did the subthreshold polarization of cell bodies and axon terminals by uniform dc E-fields. For all forms of stimulation, the lowest threshold elements were terminals of the axon collaterals, and the dependence of threshold and polarization on spatial and temporal stimulation parameters was strongly affected by morphological features of the axonal arbor, including myelination, diameter, and branching. Significance These results provide key insights into the mechanisms of cortical stimulation. The presented models can be used to study various cortical stimulation modalities while incorporating detailed spatial and temporal features of the applied E-field.

Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

Abstract: Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements it activates and how stimulation parameters affect the neural response. We integrated detailed neuronal models with TMS-induced electric fields in the human head to quantify the effects of TMS on cortical neurons. TMS activated with lowest intensity layer 5 pyramidal cells at their intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 2/3 pyramidal cells and inhibitory basket cells may be activated too, whereas direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, contrary to theories implicating the field component normal to the cortical surface. Varying the induced current’s direction caused a waveform-dependent shift in the activation site and provided a mechanistic explanation for experimentally observed differences in thresholds and latencies of muscle responses. This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other forms of cortical stimulation.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

Abstract: The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice

Model-based deconstruction of cortical evoked potentials generated by subthalamic nucleus deep brain stimulation.

Abstract: Subthalamic nucleus (STN) deep brain stimulation (DBS) is increasingly used to treat Parkinson’s disease (PD). Cortical potentials evoked by STN DBS in patients with PD exhibit consistent short-lat...

Modeling Current Sources for Neural Stimulation in COMSOL

Abstract: Background: Computational modeling provides an important toolset for designing and analyzing neural stimulation devices to treat neurological disorders and diseases. Modeling enables efficient exploration of large parameter spaces, where preclinical and clinical studies would be infeasible. Current commercial finite element method software packages enable straightforward calculation of the potential distributions, but it is not always clear how to implement boundary conditions to appropriately represent metal stimulating electrodes. By quantifying the effects of different electrode representations on activation thresholds for model axons, we provide recommendations for accurate and efficient modeling of neural stimulating electrodes. Methods: We quantified the effects of different representations of current sources for neural stimulation in COMSOL Multiphysics for monopolar, bipolar, and multipolar electrode designs. Results: We recommend modeling each electrode contact as a thin platinum domain, modeling the electrode substrate with the conductivity of silicone, and either using a point current source in the center of each electrode contact or using a boundary current source. Alternatively, to avoid possible numerical instabilities associated with a large range of conductivity values (i.e., platinum and silicone) and to eliminate the small mesh elements required for thin electrode contacts, the electrode substrate can be assigned the conductivity of platinum by using insulating boundaries between the substrate and surrounding medium, and within the substrate to isolate the contacts from each other. When modeling more than one contact, we recommend using superposition by solving the model once for each contact, leaving inactive contacts floating, and superposing the resulting potentials. We computed comparable errors in activation thresholds across the different implementations in a simplified model (electrode in a homogeneous, isotropic medium), and in realistic models of rat spinal cord stimulation (SCS) and human deep brain stimulation, indicating that the recommended approaches are applicable to different stimulation targets.

Modified cable equation incorporating transverse polarization of neuronal membranes for accurate coupling of electric fields.

Abstract: Objective We present a theory and computational methods to incorporate transverse polarization of neuronal membranes into the cable equation to account for the secondary electric field generated by the membrane in response to transverse electric fields. The effect of transverse polarization on nonlinear neuronal activation thresholds is quantified and discussed in the context of previous studies using linear membrane models. Approach The response of neuronal membranes to applied electric fields is derived under two time scales and a unified solution of transverse polarization is given for spherical and cylindrical cell geometries. The solution is incorporated into the cable equation re-derived using an asymptotic model that separates the longitudinal and transverse dimensions. Two numerical methods are proposed to implement the modified cable equation. Several common neural stimulation scenarios are tested using two nonlinear membrane models to compare thresholds of the conventional and modified cable equations. Main results The implementations of the modified cable equation incorporating transverse polarization are validated against previous results in the literature. The test cases show that transverse polarization has limited effect on activation thresholds. The transverse field only affects thresholds of unmyelinated axons for short pulses and in low-gradient field distributions, whereas myelinated axons are mostly unaffected. Significance The modified cable equation captures the membrane's behavior on different time scales and models more accurately the coupling between electric fields and neurons. It addresses the limitations of the conventional cable equation and allows sound theoretical interpretations. The implementation provides simple methods that are compatible with current simulation approaches to study the effect of transverse polarization on nonlinear membranes. The minimal influence by transverse polarization on axonal activation thresholds for the nonlinear membrane models indicates that predictions of stronger effects in linear membrane models with a fixed activation threshold are inaccurate. Thus, the conventional cable equation works well for most neuroengineering applications, and the presented modeling approach is well suited to address the exceptions.

Stimulation of the sensory pudendal nerve increases bladder capacity in the rat.

Abstract: Pudendal nerve stimulation is a promising treatment approach for lower urinary tract dysfunction, including symptoms of overactive bladder. Despite some promising clinical studies, there remain man...

Coupling magnetically induced electric fields to neurons: longitudinal and transverse activation

Abstract: We present a theory and computational models to couple the electric field induced by magnetic stimulation to neuronal membranes. The response of neuronal membranes to induced electric fields is examined under different time scales, and the characteristics of the primary and secondary electric fields from electromagnetic induction and charge accumulation on conductivity boundaries, respectively, are analyzed. Based on the field characteristics and decoupling of the longitudinal and transverse field components along the neural cable, quasi-potentials are a simple and accurate approximation for coupling of magnetically induced electric fields to neurons and a modified cable equation provides theoretical consistency for magnetic stimulation. The conventional and modified cable equations are used to simulate magnetic stimulation of long peripheral nerves by circular and figure-8 coils. Activation thresholds are obtained over a range of lateral and vertical coil positions for two nonlinear membrane models representing unmyelinated and myelinated axons and also for undulating myelinated axons. For unmyelinated straight axons, the thresholds obtained with the modified cable equation are significantly lower due to transverse polarization, and the spatial distributions of thresholds as a function of coil position differ significantly from predictions by the activating function. For myelinated axons, the transverse field contributes negligibly to activation thresholds, whereas axonal undulation can increase or decrease thresholds depending on coil position. The analysis provides a rigorous theoretical foundation and implementation methods for the use of the cable equation to model neuronal response to magnetically induced electric fields. Experimentally observed stimulation with the electric fields perpendicular to the nerve trunk cannot be explained by transverse polarization alone and is likely due to nerve fiber undulation and other geometrical inhomogeneities.

Frequency-dependent antidromic activation in thalamocortical relay neurons: effects of synaptic inputs.

Abstract: Objective Deep brain stimulation (DBS) generates action potentials (APs) in presynaptic axons and fibers of passage. The APs may be antidromically propagated to invade the cell body and/or orthodromically transmitted to downstream structures, thereby affecting widespread targets distant from the electrode. Activation of presynaptic terminals also causes trans-synaptic effects, which in turn alter the excitability of the post-synaptic neurons. Our aim was to determine how synaptic inputs affect the antidromic invasion of the cell body. Approach We used a biophysically-based multi-compartment model to simulate antidromic APs in thalamocortical relay (TC) neurons. We applied distributed synaptic inputs to the model and quantified how excitatory and inhibitory inputs contributed to the fidelity of antidromic activation over a range of antidromic frequencies. Main results Antidromic activation exhibited strong frequency dependence, which arose from the hyperpolarizing afterpotentials in the cell body and its respective recovery cycle. Low-frequency axonal spikes faithfully invaded the soma, whereas frequent failures of antidromic activation occurred at high frequencies. The frequency-dependent pattern of the antidromic activation masked burst-driver inputs to TC neurons from the cerebellum in a frequency-dependent manner. Antidromic activation also depended on the excitability of the cell body. Excitatory synaptic inputs improved the fidelity of antidromic activation by increasing the excitability, and inhibitory inputs suppressed antidromic activation by reducing soma excitability. Stimulus-induced depolarization of neuronal segments also facilitated antidromic propagation and activation. Significance The results reveal that synaptic inputs, stimulus frequency, and electrode position regulate antidromic activation of the cell body during extracellular stimulation. These findings provide a biophysical basis for interpreting the widespread inhibition/activation of target nuclei during DBS.

Biophysically Realistic Neuron Models for Simulation of Cortical Stimulation

Abstract: Objective: We implemented computational models of human and rat cortical neurons for simulating the neural response to cortical stimulation with electromagnetic fields. Approach: We adapted model neurons from the library of Blue Brain models to reflect biophysical and geometric properties of both adult rat and human cortical neurons and coupled the model neurons to exogenous electric fields (E-fields). The models included 3D reconstructed axonal and dendritic arbors, experimentally-validated electrophysiological behaviors, and multiple, morphological variants within cell-types. Using these models, we characterized the single-cell responses to intracortical microstimulation (ICMS) and uniform E-field with dc as well as pulsed currents. Main results: The strength-duration and current-distance characteristics of the model neurons to ICMS agreed with published experimental results, as did the subthreshold polarization of cell bodies and axon terminals by uniform dc E-fields. For all forms of stimulation, the lowest threshold elements were terminals of the axon collaterals, and the dependence of threshold and polarization on spatial and temporal stimulation parameters was strongly affected by morphological features of the axonal arbor, including myelination, diameter, and branching. Significance: These results provide key insights into the mechanisms of cortical stimulation. The presented models can be used to study various cortical stimulation modalities while incorporating detailed spatial and temporal features of the applied E-field.

Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly Powered Switched-Capacitor Discharge Stimulation System

Abstract: The purpose of this study was to test the feasibility of using a switched-capacitor discharge stimulation (SCDS) system for electrical stimulation, and, subsequently, determine the overall energy saved compared to a conventional stimulator. We have constructed a computational model by pairing an image-based volume conductor model of the cat head with cable models of corticospinal tract (CST) axons and quantified the theoretical stimulation efficiency of rectangular and decaying exponential waveforms, produced by conventional and SCDS systems, respectively. Subsequently, the model predictions were tested in vivo by activating axons in the posterior internal capsule and recording evoked electromyography (EMG) in the contralateral upper arm muscles. Compared to rectangular waveforms, decaying exponential waveforms with time constants >500 μs were predicted to require 2%–4% less stimulus energy to activate directly models of CST axons and 0.4%–2% less stimulus energy to evoke EMG activity in vivo . Using the calculated wireless input energy of the stimulation system and the measured stimulus energies required to evoke EMG activity, we predict that an SCDS implantable pulse generator (IPG) will require 40% less input energy than a conventional IPG to activate target neural elements. A wireless SCDS IPG that is more energy efficient than a conventional IPG will reduce the size of an implant, require that less wireless energy be transmitted through the skin, and extend the lifetime of the battery in the external power transmitter.

Platform for model-based design and testing for deep brain stimulation

Abstract: Deep Brain Stimulation (DBS) is effective at alleviating symptoms of neurological disorders such as Parkinson's disease. Yet, despite its safety-critical nature, there does not exist a platform for integrated design and testing of new algorithms or devices. Consequently, we introduce a model-based design framework for DBS controllers based on a physiologically relevant basal-ganglia model (BGM) that we capture as a network of nonlinear hybrid automata, synchronized via neural activation events. The BGM is parametrized by the number of neurons used to model each of the BG regions, which supports tradeoffs between fidelity and complexity of the model. Our hybrid-automata representation is exploited for design of software (Simulink) and hardware (FPGA) BGM platforms, with the latter enabling real-time model simulation and device testing. We demonstrate that the BGM platform is capable of generating physiologically relevant responses to DBS, and validate the BGM using a set of requirements obtained from existing work. We present the use of our framework for design and test of DBS controllers with varying levels of adaptation/feedback. Our evaluations are based on Quality-of-Control metrics that we introduce for runtime monitoring of DBS effectiveness.

Patterned low-frequency deep brain stimulation induces motor deficits and modulates cortex-basal ganglia neural activity in healthy rats

Abstract: Subthalamic nucleus deep brain stimulation is a promising therapy for movement disorders such as Parkinson’s disease. Several groups reported correlation between suppression of abnormal oscillatory...

Detection of Bladder Contractions From the Activity of the External Urethral Sphincter in Rats Using Sparse Regression

Abstract: Bladder overactivity and incontinence and dysfunction can be mitigated by electrical stimulation of the pudendal nerve applied at the onset of a bladder contraction. Thus, it is important to predict accurately both bladder pressure and the onset of bladder contractions. We propose a novel method for prediction of bladder pressure using a time-dependent spectrogram representation of external urethral sphincter electromyographic (EUS EMG) activity and a least absolute shrinkage and selection operator regression model. There was a statistically significant improvement in prediction of bladder pressure compared with methods based on the firing rate of EUS EMG activity. This approach enabled prediction of the onset of bladder contractions with 91% specificity and 96% sensitivity and may be suitable for closed-loop control of bladder continence.

Waveforms for Neural Stimulation

Abstract: The stimulation waveform describes the applied current, i(t), or voltage, v(t), as a function of time. The characteristics of the stimulation waveform contribute to the effectiveness, efficiency, and risks of electrical stimulation, and thus consideration of waveform characteristics is of paramount importance in the design and application of neuromodulation therapies. This chapter reviews the fundamental considerations of waveform effects as well as designs intended to improve performance in the three performance domains of efficiency, selectivity, and risk.

Method to design temporal patterns of nervous system stimulation

Abstract: The present invention relates to methods that enable one to design temporal patterns for the optimal stimulation of a nervous system, one or more nerve cells, or nervous tissue. In one embodiment, the present invention relates to methods to design improved stimulation patterns and/or genetic algorithms for the optimal stimulation of a nervous system, one or more nerve cells, or nervous tissue. In one embodiment, the present invention utilizes a model-based design to achieve a more optimal stimulation pattern for use in connection with a nervous system, one or more nerve cells, or nervous tissue (e.g., a human nervous system). In another embodiment, the model-based design of the present invention utilizes a systematic search method to identify parameters (e.g., design variables) that minimize a cost function (e.g., optimize the fitness of a particular design). In one instance, the system and method of the present invention is demonstrated via optimal temporal patterns of electrical stimulation for a nervous system, one or more nerve cells, or nervous tissue.

Systems and methods for optimized waveform neural block

Abstract: The present disclosure provides systems and methods relating to neuromodulation. In particular, the present disclosure provides systems and methods for identifying optimized waveforms for blocking neural conduction. The systems and methods of neuromodulation disclosed herein facilitate the treatment of various diseases associated with pathological neural activity. The optimized waveforms for blocking neural conduction are identified through use of a global optimization algorithm based on predetermined performance criteria. A plurality of waveforms are generated and evaluated for neuronal conduction block using a computational model of extracellular neuronal stimulation, and at least on candidate waveform having an optimized shape capable of blocking neural conduction is identified.

Learning-based control design for deep brain stimulation

Abstract: By employing low-voltage electrical stimulation of the basal ganglia (BG) regions of the brain, deep brain stimulation (DBS) devices are used to alleviate the symptoms of several neurological disorders, including Parkinson's disease (PD). Recently, we have developed a Basal Ganglia Model (BGM) that can be utilized for design and evaluation of DBS devices. In this work, we focus on the use of a hardware (FPGA) implementation of the BGM platform to facilitate development of new control policies. Specifically, we introduce a design-time framework that allows for development of suitable control policies, in the form of electrical pulses with variable temporal patterns, while supporting tradeoffs between energy efficiency and efficacy (i.e., Quality-of-Control) of the therapy. The developed framework exploits machine learning and optimization based methods for design-space exploration where predictive behavior for any control configuration (i.e., temporal pattern) is obtained using the BGM platform that simulates physiological response to the considered control in real-time. To illustrate the use of the developed framework, in our demonstration we present how the BGM can be utilized for physiologically relevant BG modeling and design-state exploration for DBS controllers, as well as show the effectiveness of obtained controllers that significantly outperform conventional DBS controllers.