Browsing by Author "Grill, Warren M"

Severe constipation can be life-threatening and disproportionately affects patients who may not benefit from conventional treatments. Sacral nerve stimulation (SNS) is an alternative to laxatives and pharmaceuticals, and it modulates propulsive action in the colon. Conventional SNS failed to treat slow-transit constipation. I hypothesized bursts of nerve stimulation interleaved by quiescent periods increase colonic transit more effectively than continuous nerve stimulation. I electrically stimulated the colon directly in computational models and the isolated mouse colon to characterize properties of the colonic motor complex (CMC), and I used optical and fluorescent imaging, electromyography, and manometry to compare the effect of pelvic and sacral nerve stimulation on colonic motility. I developed a computational model of colonic motility and compared the effects of burst and conventional nerve stimulation on pellet velocity and colonic emptying under normal and slow transit conditions. Burst nerve stimulation evoked more frequent calcium and pressure waves, and increased fecal pellet output than continuous nerve stimulation in the isolated mouse colon, anesthetized rat, and computational model, respectively. Burst nerve stimulation with optimized burst frequency, duration, and interval more effectively produced prokinetic motility than continuous nerve stimulation, suggesting that burst SNS may be a viable clinical treatment for severe and slow transit constipation.

Deep brain stimulation (DBS) electrodes are intended to stimulate specific areas of the brain to treat movement disorders including essential tremor, Parkinson's disease and dystonia. An important goal in the design of next generation DBS electrodes is to minimize the power needed to stimulate specific regions of the brain. A reduction in power consumption will prolong battery life and reduce the size of implanted pulse generator. Electrode geometry is one approach to increase the efficiency of neural stimulation and reduce the power required to produce the level of activation required for clinical efficacy.

We first characterized the impedance of the presently used clinical DBS electrodes in vitro and in vivo. Characterization of the electrode-tissue interface impedance is required to quantify the composition of charge transfer to the brain tissue. The composition of charge transfer was dependent on both the current density and the sinusoidal frequency. The assumption of the DBS electrode being ideally polarizable was not valid under clinical stimulating conditions. This implies that irreversible processes that can cause electrode or tissue damage might occur when high charge injection is required for DBS.

Current density distribution is an important factor in determining patterns of neural excitation, tissue damage and electrode corrosion. We developed a recursive simulation scheme to calculate the current density distribution that incorporates the nonlinear electrode-tissue interface into finite-element based models of electrodes. The current density distributions on the electrode surface were strongly dependent on the sinusoidal frequency. The primary current density distribution without including the electrode-tissue interface can be used to estimate neural excitation, tissue damage and electrode corrosion with rectangular stimulus pulses as most of the signal power is at frequencies where the secondary current density distribution matches closely the primary current density distribution.

We designed and analyzed novel electrode geometries to decrease stimulation thresholds, thus reducing power consumption of implanted stimulators. Our hypothesis was that high-perimeter electrode geometries that increase the variation of current density on the electrode surface will generate larger activating functions for surrounding neurons and thereby increase stimulation efficiency. We investigated three classes of electrodes: segmented cylindrical electrodes, serpentine-perimeter planar electrodes, and serpentine-perimeter cylindrical electrodes. An approach that combined finite element models of potentials and cable models of axonal excitation was used to quantify the stimulation efficiency of electrodes with various geometries. Increasing the electrode perimeter increased the electrode efficiency by decreasing stimulation threshold. Both segmentation and serpentine edges provided means to increase the efficiency of stimulation. Novel cylindrical electrodes that combined segmentation with serpentine edges decreased power consumption by ~20% for axons parallel to the electrode and by ~35% for axons perpendicular to the electrode. These electrode designs could potentially prolong the average battery life of deep brain stimulator by more than one year.

Deep brain stimulation (DBS) is an established treatment for movement disorders that has been implanted in more than 40,000 patients worldwide. Despite the successes of DBS, its mechanisms of action are not well understood. Early descriptions of the mechanisms of DBS focused on whether DBS excited or inhibited neurons in the stimulated nucleus. However, changes in the patterns of neuronal activity, and not just changes in the rate of neuronal activity, play a major role in the pathology of movement disorders. Therefore, we hypothesized that the temporal pattern of stimulation might be an important factor in determining the effectiveness of DBS. The purpose of this dissertation was to use temporally irregular patterns of stimulation (non-regular interpulse intervals) to probe the mechanisms of thalamic DBS in suppressing tremor. The clinical tremor measurements reported in this dissertation represent the first tremor data published during stimulation with temporally irregular stimulus trains in human subjects. First, we tested the effects of paired-pulse DBS on tremor suppression in human subjects with essential tremor and on the responses of a computational model of thalamic neurons. DBS was more effective at reducing tremor when pulses were evenly spaced than when there were large differences between intrapair and interpair pulse intervals, suggesting that tremor suppression is dependent on the pattern of DBS and not just the average rate of stimulation. Increasing the difference between the intrapair and interpair intervals in the computational model rendered model neurons more likely to fire synchronous bursts. Second, we quantified the effects of the degree of regularity of temporally random stimulus trains in human subjects with tremor. We pioneered an innovative preparation to conduct these experiments--during surgery to replace the implantable pulse generator--which allowed us to establish a direct connection to implanted DBS leads under stable conditions. Stimulus trains were less effective at relieving tremor as the temporal spacing between stimulus pulses in DBS trains became more irregular. However, the reasons for the decreased efficacy of the temporally irregular stimulus trains was not clear. Third, we evaluated the contributions of `pauses,' `bursts,' and `irregularity, per se' to the inability of irregular stimulus trains to suppress tremor. Stimulus trains with pauses were significantly less effective at suppressing tremor than stimulus trains without pauses, while there were no significant changes in tremor suppression between trains with bursts and those without bursts, or between trains that were irregular and those that were periodic. We also developed a computer-based biophysical model of a thalamic network to simulate the response of thalamic neurons to the same temporal patterns of DBS. Trains that effectively suppressed tremor in human subjects also suppressed fluctuations in transmembrane potential at the frequency associated with burst-driven cerebellar inputs to the thalamus. Both clinical and computational findings indicate that DBS suppresses tremor by masking cerebellar burst-driven input to the thalamus.

The work in this dissertation bridges an important gap between the hypothesis that high-frequency DBS masks pathological activity in the cerebello-thalamo-cortical circuit and the experimentally observed finding that DBS in the subthalamic area suppresses tremor more effectively than DBS in the Vim thalamus proper. We provided experimental and computational evidence that the mechanism of DBS is to mask the burst-driven cerebellar inputs to the thalamus. Hence, the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus.

Deep brain stimulation (DBS) is an established surgical therapy for movement disorders. The mechanisms of action of DBS remain unclear, and selection of stimulation parameters is a clinical challenge and can result in sub-optimal outcomes. Closed-loop DBS systems would use a feedback control signal for automatic adjustment of DBS parameters and improved therapeutic effectiveness. We hypothesized that evoked compound action potentials (ECAPs), generated by activated neurons in the vicinity of the stimulating electrode, would reveal the type and spatial extent of neural activation, as well as provide signatures of clinical effectiveness. The objective of this dissertation was to record and characterize the ECAP during DBS to determine its suitability as a feedback signal in closed-loop systems. The ECAP was investigated using computer simulation and in vivo experiments, including the first preclinical and clinical ECAP recordings made from the same DBS electrode implanted for stimulation.

First, we developed DBS-ECAP recording instrumentation to reduce the stimulus artifact and enable high fidelity measurements of the ECAP at short latency. In vitro and in vivo validation experiments demonstrated the capability of the instrumentation to suppress the stimulus artifact, increase amplifier gain, and reduce distortion of short latency ECAP signals.

Second, we characterized ECAPs measured during thalamic DBS across stimulation parameters in anesthetized cats, and determined the neural origin of the ECAP using pharmacological interventions and a computer-based biophysical model of a thalamic network. This model simulated the ECAP response generated by a population of thalamic neurons, calculated ECAPs similar to experimental recordings, and indicated the relative contribution from different types of neural elements to the composite ECAP. Signal energy of the ECAP increased with DBS amplitude or pulse width, reflecting an increased extent of activation. Shorter latency, primary ECAP phases were generated by direct excitation of neural elements, whereas longer latency, secondary phases were generated by post-synaptic activation.

Third, intraoperative studies were conducted in human subjects with thalamic DBS for tremor, and the ECAP and tremor responses were measured across stimulation parameters. ECAP recording was technically challenging due to the presence of a wide range of stimulus artifact magnitudes across subjects, and an electrical circuit equivalent model and finite element method model both suggested that glial encapsulation around the DBS electrode increased the artifact size. Nevertheless, high fidelity ECAPs were recorded from acutely and chronically implanted DBS electrodes, and the energy of ECAP phases was correlated with changes in tremor.

Fourth, we used a computational model to understand how electrode design parameters influenced neural recording. Reducing the diameter or length of recording contacts increased the magnitude of single-unit responses, led to greater spatial sensitivity, and changed the relative contribution from local cells or passing axons. The effect of diameter or contact length varied across phases of population ECAPs, but ECAP signal energy increased with greater contact spacing, due to changes in the spatial sensitivity of the contacts. In addition, the signal increased with glial encapsulation in the peri-electrode space, decreased with local edema, and was unaffected by the physical presence of the highly conductive recording contacts.

It is feasible to record ECAP signals during DBS, and the correlation between ECAP characteristics and tremor suggests that this signal could be used in closed-loop DBS. This was demonstrated by implementation in simulation of a closed-loop system, in which a proportional-integral-derivative (PID) controller automatically adjusted DBS parameters to obtain a target ECAP energy value, and modified parameters in response to disturbances. The ECAP also provided insight into neural activation during DBS, with the dominant contribution to clinical ECAPs derived from excited cerebellothalamic fibers, suggesting that activation of these fibers is critical for DBS therapy.

Epidural cortical stimulation (ECS) is a developing therapy for many neurological disorders. However, the mechanisms by which ECS has its effects are unknown, and this lack of understanding has limited the development and optimization of this promising therapy. This dissertation investigates the effects of ECS on the neurons in the cortex and how these effects vary with electrode geometry and location as well as the electrical and geometrical properties of the anatomy.

The effects of ECS on cortical neurons were investigated using a three dimensional computational model of the human precentral gyrus and surrounding anatomy. An epidural electrode was placed above the gyrus, and the model was solved using the finite element method. The outputs of the model included distributions of electric potential, the second spatial derivative of potential (activating function), and current density. The distributions of electric potential were coupled to compartmental models of cortical neurons to quantify the effects of ECS on cortical neurons. A sensitivity analysis was performed to assess how thresholds and distributions of activating function were impacted by changes in the geometrical and electrical properties of the model. In vivo experiments of epidural electrical stimulation of cat motor cortex were performed to measure the effects of stimulation parameters and electrode location on thresholds for evoking motor responses.

During ECS, the region of cortex directly underneath the electrode was activated at the lowest thresholds, and neurons deep in the sulcus could not be directly activated without coactivation of neurons located on the crowns or lips of the gyri. The thresholds for excitation of cortical neurons depended on stimulation polarity as well as the orientation and position of the neurons with respect to the electrode. In addition, the patterns and spatial extent of activation were influenced by the geometry of the cortex and surrounding layers, the dimensions of the electrodes, and the positioning of the lead. In vivo thresholds for evoking motor responses were dependent on electrode location and stimulation polarity, and bipolar thresholds were often different from monopolar thresholds through the respective anode and cathode individually. The effects of stimulation polarity and electrode location on thresholds for evoking motor responses paralleled results of the computational model. Experimental evidence of indirect effects of ECS, mediated by synaptic interactions between neural elements, revealed an opportunity for further development of the computational model. The outcome of this dissertation is an improved understanding of the factors that influence the effects of ECS on cortical neurons, and this knowledge will help facilitate the rational implantation and programming of ECS systems.

Approximately 1 million Americans live with drug-resistant epilepsy. Surgical resection of the brain areas where seizures originate can be curative. However, successful surgical outcomes require delineation of the epileptogenic zone (EZ), the minimum amount of tissue that needs to be resected to eliminate a patient’s seizures. EZ localization is often accomplished using stereo-EEG where 5-30 wires are implanted into the brain through small holes drilled through the skull to map widespread regions of the epileptic network. However, despite the technical advances in surgical planning and epilepsy monitoring, seizure freedom rates following epilepsy surgery have remained at ~60% for decades. In part, seizure freedom rates have not increased because epilepsy neurologists do not have appropriate software tools to optimize stereo-EEG. In this dissertation, we report on the development and analysis of foundational models and software tools to improve the use of stereo-EEG technology and ultimately increase seizure-freedom rates following epilepsy surgery.We developed an automated image-based head-modeling pipeline to generate patient-specific models for stereo-EEG analysis. We assessed the key dipole source model assumption, which assumes that voltages generated by a population of active neurons can be simplified to a single dipole. We found that the dipole source model is appropriate to reproduce the spatial voltage distribution generated by neurons and for source localization applications. Our findings validate a key model parameter for stereo-EEG head-models, which are foundational to all computational tools developed to optimize stereo-EEG. Using the dipole source model, we systematically assessed the origin of recorded brain electrophysiological signals using computational models. We found that, counter to dogma, action potentials contribute appreciably to brain electrophysiological signals. Our findings reshape the cellular interpretation of brain electrophysiological signals and should impact modeling efforts to reproduce neural recordings. We also developed a recording sensitivity metric, which quantifies the cortical areas that are recordable by a set of stereo-EEG electrodes. We used the recording sensitivity metric to develop two software tools to visualize the recording sensitivity on patient-specific brain geometry and to optimize the trajectories of stereo-EEG electrodes. Using the same number of electrodes, our optimization approach identified trajectories that had greater recording sensitivity than clinician-defined trajectories. Using the same target recording sensitivity, our optimization approach found trajectories that mapped the same amount of cortex with fewer electrodes compared to the clinician-defined trajectories. Thus, our optimization approach can improve the outcomes following epilepsy surgery by increasing the chances that an electrode records from the EZ or reduce the risk of surgery by minimizing the number of necessary implanted electrodes. We finally developed a propagating source reconstruction algorithm using a novel TEmporally Dependent Iterative Expansion approach (TEDIE). TEDIE takes as inputs stereo-EEG recordings and patient-specific anatomical images, produces movies of dynamic (moving) neural activity displayed on patient-specific anatomy, and distills the immense intracranial stereo-EEG dataset into an objective reconstruction of the EZ. We validated TEDIE using seizure recordings from 40 patients from two centers. TEDIE consistently localized the EZ closer to the resected regions for patients who are currently seizure-free. Further, TEDIE identified new EZs in 13 of the 23 patients who are currently not seizure-free. Therefore, TEDIE is expected to improve the accuracy of the evaluation of surgical epilepsy candidates, result in increased numbers of patients advancing to surgery, and increase the proportion of patients who achieve seizure freedom through surgery. Together, our suite of software tools constitute important advances to optimize stereo-EEG implantation and analysis, which should lead to more patients achieving seizure freedom following epilepsy surgery.

Spinal cord injury (SCI) and other neurological diseases and disorders can cause urinary dysfunction that can cause serious health problems and reduce an individual's quality of life. Current methods for treating urinary dysfunction have major limitations or provide inadequate improvement in urinary symptoms. Pudendal nerve stimulation is a potential means of restoring control of bladder function in persons with neurological disease or spinal cord injury. Bladder contraction and relaxation can be evoked by pudendal afferent stimulation, and peripheral pudendal afferent branches may be ideal targets for a bladder control neural prosthesis. This dissertation investigates control of bladder function by selective activation of pudendal afferents.

This study investigated the ability to improve both urinary continence and micturition by both direct and minimally-invasive electrical stimulation of selected pudendal afferents in α-chloralose anesthetized male cats. Direct stimulation of the pudendal afferents in the dorsal nerve of the penis (DNP), percutaneous DNP stimulation, and intraurethral stimulation were used to investigate the bladder response to selective activation of pudendal afferents. Finite element modeling of the cat lower urinary tract was used to investigate the impact of intraurethral stimulation location and intraurethral electrode configuration on activation of pudendal afferents. Also, the impact of pharmacological and surgical block of sympathetic activity to the bladder on the bladder reflexes evoked by DNP stimulation was investigated to determine the role of the sympathetic bladder innervation on the mechanism of bladder activation by pudendal afferent stimulation.

The DNP is an ideal target for restoring urinary function because stimulation at low frequencies (5-10 Hz) improves urinary continence, while stimulation at high frequencies (33-40 Hz) improves urinary voiding. Intraurethral stimulation is a valid method for clinical investigation of the ability to evoke bladder inhibition and activation via selective activation of the DNP or cranial sensory branch (CSN) of the pudendal nerve. In the cat, intraurethral stimulation can activate the bladder via two distinct neural pathways, a supraspinal pathway reflex activated by the CSN and a spinal reflex activated by the DNP. Finite element modeling revealed the importance of urethral location for selective pudendal afferent activation by intraurethral stimulation. Finally, the sympathetic bladder pathway does not play a significant role in the mechanism mediating bladder activation by DNP stimulation. These findings imply that selective pudendal afferent stimulation is a promising approach for restoring control of bladder function to individuals with SCI or other neurological disorders.

Deep brain stimulation (DBS) is a highly successful neuromodulation therapy for treating the motor symptoms of Parkinson’s disease (PD). However, DBS has been used for over 30 years with little change in clinically used stimulation parameters and technology, and consequently, there have been few improvements in therapeutic efficacy during that period. Fortunately, recent advances in DBS devices and techniques, including automated stimulation parameter selection, directional leads, closed-loop stimulation, and model-optimized temporal patterns of stimulation, have the potential to improve symptom reduction, decrease side effects, and extend device battery life. However, making use of many of these techniques requires a recordable electrophysiological signal, or biomarker, that correlates strongly with clinical outcomes. The goals of this dissertation were to develop tools that assist in the recording and application of biomarkers, to characterize a new potential biomarker, the cortical evoked potential (cEP), and correlate it with symptom reduction, and to understand mechanistically how the cEP relates to symptom reduction during DBS. First, we quantified the effects of a novel electrodeposited platinum-iridium coating (EPIC) on single unit recording performance. We implanted electrodes in rats and used electrophysiological and histological measurements to compare quantitatively the single unit recording performance of coated vs. uncoated electrodes over a 12-week period. The coated electrodes had lower impedance, reduced noise, increased signal-to-noise ratio, and an increased number of discernible units per electrode as compared to the uncoated electrodes. These results demonstrated that EPIC electrodes provided recording performance and longevity superior to uncoated electrodes, thus improving our ability to quantify potential biomarkers from single unit recordings. Second, we developed a modified genetic algorithm (GA) designed to optimize temporal patterns of stimulation. We developed five modifications to the standard GA repopulation step that adapted the GA to design patterns for neuromodulation applications. We evaluated each modification individually and all modifications collectively by comparing performance to a standard GA across three test functions and two biophysically-based models of neural stimulation. The modifications improved performance across the test functions and performed best when all were used collectively. Thus, we developed a powerful tool for optimizing temporal patterns of stimulation using model-based proxies of DBS biomarkers. Third, we characterized a new candidate biomarker for DBS, the cEP, and quantified its correlation with symptom reduction during DBS. We used the unilateral 6-hydroxydopamine (6-OHDA) lesioned rat model or parkinsonism, with stimulating electrodes implanted in the subthalamic nucleus (STN) and the electrocorticography (ECoG) recorded above motor cortex (M1). We recorded the cEP during a range of stimulation conditions and while performing behavioral assessments of hypokinetic symptoms. The cEP was strongly affected by stimulation condition, and the cEP magnitude declined and the cEP latency increased with higher stimulation frequencies. These effects occurred over multiple minutes and with multiple time-scales. Additionally, the cEP magnitude and latency were each strongly correlated with symptom reduction during DBS, with correlations that were stronger and more consistent than those of conventional spectral-based biomarkers. This study demonstrated the potential utility of the cEP as a biomarker for symptom reduction from DBS. Fourth, to understand better how the cEP may relate mechanistically to symptom reduction from DBS, we developed a computational model of antidromic cortical activation during STN DBS and assessed the ability of DBS to desynchronize pathological cortical beta band oscillations. We tuned and validated the model using experimental data from the 6-OHDA lesioned rat model, and we implemented a stochastic model of antidromic spike failure, which is the presumed cause of the observed changes in the cEP magnitude, to determine how changes in the cEP relate to cortical desynchronization. STN DBS desynchronized pathological oscillations at high stimulation frequencies via a mechanism analogous to the informational lesion theory. Specifically, the DBS-evoked spikes masked the intrinsic pathological spiking via a combination of refractoriness, spike collision, and synaptic depletion. Further, the model revealed that antidromic spike failure played a critical role in shaping the therapeutic frequency profile of this masking effect, enforcing a parabolic shape with maximum desynchronization at ~130 Hz. The results in this dissertation advance our understanding of the therapeutic mechanism of STN DBS, provide important tools for application of electrophysiological biomarkers in DBS, and characterize the utility of the cEP as a potential biomarker to improve therapeutic outcomes.

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Modulation of neural activity with electrical stimulation is a widespread therapy for treating neurological disorders and diseases. Two notable applications that have had striking clinical success are deep brain stimulation (DBS) for the treatment of movement disorders (e.g., Parkinson's disease) and spinal cord stimulation (SCS) for the treatment of chronic low back and limb pain. In these therapies, the battery life of the stimulators is much less than the required duration of treatment, requiring patients to undergo repeated battery replacement surgeries, which are costly and obligate them to incur repeatedly the risks associated with surgery. Further, deviations in lead position of 2-3 mm can preclude some or all potential clinical benefits, and in some cases, generate side-effects by stimulation of non-target regions. Therefore, despite the success of DBS and SCS, their efficiency and ability to activate target neural elements over non-target elements, termed selectivity, are inadequate and need improvement.

We combined computational models of volume conduction in the brain and spine with cable models of neurons to design novel electrode configurations for efficient and selective electrical stimulation of nervous tissue. We measured the efficiency and selectivity of prototype electrode designs in vitro and in vivo. Stimulation efficiency was increased by increasing electrode area and/or perimeter, but the effect of increasing perimeter was not as pronounced as increasing area. Cylindrical electrodes with aspect (height to diameter) ratios of > 5 were the most efficient for stimulating neural elements oriented perpendicular to the axis of the electrode, whereas electrodes with aspect ratios of < 2 were the most efficient for stimulating parallel neural elements.

Stimulation selectivity was increased by combining two or more electrodes in multipolar configurations. Asymmetric bipolar configurations were optimal for activating parallel axons over perpendicular axons; arrays of cathodes with short interelectrode spacing were optimal for activating perpendicular axons over parallel axons; anodes displaced from the center of the target region were optimal for selectively activating terminating axons over passing axons; and symmetric tripolar configurations were optimal for activating neural elements based on their proximity to the electrode. The performance of the efficient and selective designs was not be explained solely by differences in their electrical properties, suggesting that field-shaping effects from changing electrode geometry and polarity can be as large as or larger than the effects of decreasing electrode impedance.

Advancing our understanding of the features of electrode geometry that are important for increasing stimulation efficiency and selectivity facilitates the design of the next generation of stimulation electrodes for the brain and spinal cord. Increased stimulation efficiency will increase the battery life of IPGs, increase the recharge interval of rechargeable IPGs, and potentially reduce stimulator volume. Greater selectivity may improve the success rate of DBS and SCS by mitigating the sensitivity of clinical outcomes to malpositioning of the electrode.

Electrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. In Chapter 2, we describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies.

Next, in Chapter 3 we demonstrated how ASCENT can be applied to simulate accurately nerve responses to electrical stimulation. We simulated vagus nerve stimulation (VNS) for humans, pigs, and rats. We informed our models using histology from sample-specific or representative nerves, device design features (i.e., cuff, waveform), published material and tissue conductivities, and realistic fiber models. Despite large differences in nerve size, cuff geometry, and stimulation waveform, the models predicted accurate activation thresholds across species and myelinated fiber types. However, our C fiber model thresholds overestimated thresholds across pulse widths, suggesting that improved models of unmyelinated nerve fibers are needed. Our models of human VNS yielded accurate thresholds to activate laryngeal motor fibers and captured the inter-individual variability for both acute and chronic implants. For B fibers, our small-diameter fiber model underestimated threshold and saturation for pulse widths >0.25 ms. Our models of pig VNS consistently captured the range of in vivo thresholds across all measured nerve and physiological responses (i.e., heart rate, Aδ/B fibers, Aγ fibers, EMG, and Aα fibers). In rats, our smallest diameter myelinated fibers accurately predicted fast fiber thresholds across short and intermediate pulse widths; slow unmyelinated fiber thresholds overestimated thresholds across shorter pulse widths, but there was overlap for pulse widths >0.3 ms. We elevated standards for models of peripheral nerve stimulation in populations of models across species, which enabled us to model accurately nerve responses, demonstrate that individual-specific differences in nerve morphology produce variability in neural and physiological responses, and predict mechanisms of VNS therapy and side effect.

Lastly, in Chapter 4 we investigated how previous efforts to translate VNS therapies (e.g., for stroke, heart failure, and rheumatoid arthritis) have not accounted for individual and species-specific differences in nerve responses while selecting stimulation parameters, which could explain why clinical outcomes have not reproduced promising results from preclinical animal studies. We used previously validated computational models of VNS based on individual-specific nerve morphologies for populations of rats, pigs, and humans from Chapter 3 to show that a range of thresholds exists to achieve a target nerve response within and across species. We found that applying the same parameters across individuals of a species and recycling or linear scaling of stimulation parameters across species produces a large range of nerve responses. Our work highlights the need for systematic approaches to select stimulation parameters that account for individual- and species-specific differences in nerve responses to stimulation, which may be required to achieve higher response rates and greater therapeutic benefit from VNS therapies.

Spinal cord injury (SCI) and neurological diseases can cause lower urinary tract (LUT) dysfunction, significantly disrupting normal urine storage (continence) and efficient bladder emptying (micturition). Electrical stimulation of pudendal afferents is a promising technique to treat LUT dysfunction and restore bladder control via stimulation-evoked bladder inhibition or activation. However, innovative approaches are needed, as the voiding efficiencies produced by traditional pudendal afferent stimulation are insufficient for successful clinical translation. The objective of this dissertation was to investigate the effects of novel patterns of electrical stimulation on the size of bladder contractions and voiding efficiencies produced by pudendal afferent stimulation and to explore the neural mechanisms underlying stimulation-evoked reflex bladder control.

This work quantified the magnitude of bladder contractions and voiding efficiency evoked by spatial and temporal patterns of pudendal afferent stimulation in α-chloralose anesthetized cats. Bilateral stimulation of the dorsal genital nerve (DGN) and co-stimulation of the DGN and cranial sensory nerve (CSN) generated significantly larger isovolumetric bladder contractions and increased voiding efficiencies as compared to individual stimulation and distention-evoked voiding. The temporal pattern of DGN stimulation significantly affected the magnitude of evoked bladder contractions, revealing that the bladder response to pudendal afferent stimulation is dependent on the pattern of stimulation, as well as the frequency.

The effects of intraurethral co-stimulation, combining individual stimulation sites in the proximal and distal urethra, on bladder activation and the electromyographic (EMG) activity of pelvic floor muscles were measured during urodynamics in persons with SCI. The size of stimulation-evoked bladder contractions was dependent on stimulation location and frequency, reflex EMG activity suggested that multiple reflex pathways contributed to bladder activation, and co-stimulation produced larger isovolumetric bladder contractions than single site stimulation.

Pharmacological block of inhibitory neurotransmitters was conducted to identify the mechanism of bladder inhibition evoked by pudendal afferent stimulation in α-chloralose anesthetized cats. Low frequency pudendal afferent stimulation-evoked bladder inhibition was blocked by picrotoxin, revealing that this requires a lumbosacral spinal GABAergic mechanism, and further pharmacological experiments indicated that glycinergic, adrenergic, or opioidergic mechanisms were not necessary for pudendal afferent stimulation evoked inhibition.

A computational model of the pudendo-vesical reflex and was developed based on previous neuroanatomical and electrophysiological studies to evaluate mechanisms underlying the bladder response to pudendal afferent stimulation. The frequency and pattern-dependent effects of pudendal afferent stimulation were determined by changes in firing rate of spinal interneurons in the model, suggesting that neural network interactions at the lumbosacral level can mediate the bladder response to different frequencies or temporal patterns of pudendal afferent stimulation.

The effects and mechanisms of patterned pudendal afferent stimulation represent a substantial improvement in our understanding of pudendal afferent stimulation and will be valuable for the continued development of novel methodologies of electrical stimulation for bladder control.

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Vagus nerve stimulation (VNS) is an effective treatment for epilepsy, depression, and stroke rehabilitation with ongoing studies of additional clinical applications including rheumatoid arthritis and heart failure. Acute VNS alleviates inflammation caused by infection, and clinical studies of VNS treatment for immune dysregulation are promising. However, more widespread use of VNS is limited by surgical implantation, and risks associated with surgery are deleterious in patients with pre-existing neurocognitive impairment. Non-invasive methods of VNS (e.g., transcutaneous VNS) produce inconsistent results and lack a robust biomarker to confirm nerve stimulation. There is a need for a method of VNS that provides targeted stimulation with reduced invasiveness. As well, side effects limit therapy. Reduced heart rate (HR) during stimulation is associated with therapy for heart failure, but stimulation frequency and amplitude are limited by patient tolerance. An understanding of physiological responses to parameter adjustments would allow control of therapeutic and side effects. The purpose of this dissertation was to develop novel methods of VNS, investigate new applications of VNS for post-operative treatment, and conduct parametric studies to increase the dynamic range between therapeutic effects and side effects.First, we developed a minimally invasive, targeted, percutaneous vagus nerve stimulation approach (pVNS). We stimulated the cervical vagus nerve in mice using an ultrasound-guided needle electrode under sevoflurane anesthesia. The concentric bipolar needle electrode was placed adjacent to the carotid sheath, and nerve stimulation was verified in real-time using bradycardia as a biomarker. Activation of vagal fibers was also confirmed by immunohistochemical quantification of c-Fos and choline acetyltransferase expression in relevant brainstem structures, including the dorsal motor nucleus and nucleus tractus solitarius. Following lipopolysaccharide (LPS) administration, pVNS reduced plasma levels of tumor necrosis factor at 3 h post-injection. pVNS also prevented LPS-induced hippocampal microglial activation as analyzed by changes in Iba-1 immunoreactivity, including cell body enlargement and shortened ramifications. LPS injection reduced memory function at 24 and 48 h but not when pVNS was delivered post-injection. These results provide a novel therapeutic approach using VNS to modulate neuro-immune interactions that affect cognition. Second, we assessed pVNS efficacy in prevention of surgery-induced delirium superimposed on dementia in Alzheimer’s Disease-like (CVN-AD) mice. Orthopedic surgery increased hippocampal microglia activation, amyloid-beta (AB) accumulation, and neuronal loss as measured by histology. At 24 h post-operation, neural pathologies were absent in animals that received post-operative pVNS. We quantified blood-brain barrier (BBB) permeability with intracardial tracer injection to investigate vessel integrity. Following surgery, pVNS reduced tracer concentration in the hippocampus indicating improved BBB integrity. We paired 5x familial autosomal dominant (5xFAD) AD mice and wild type (C57) mice using parabiosis to evaluate the contribution of circulating factors from sterile surgery on post-operative pathologies. AB accumulation and microglia activation increased in 5xFAD mice when the C57 parabiosis partner received orthopedic surgery. Thus, post-operative pVNS protects against surgery-induced increases in dementia pathology driven by systemic inflammation. Finally, we measured the effect of the temporal pattern of VNS on HR (a proxy to therapy) and laryngeal EMG (a side effect) in anesthetized mice. Amplitude, intra-burst frequency, and mean pulse rate (MPR) modulated HR while only MPR modulated EMG. Neither outcome was sensitive to stimulation pattern at clinical frequencies. However, stimulation modulated HR to a greater degree than EMG at an amplitude and frequency above those used clinically. We leveraged collected data to construct computational models of HR and laryngeal muscle activity that reproduced VNS responses for amplitudes and patterns. To model HR, we incorporated a mechanism for frequency-dependent filtering of vagal pulses by cardiac ganglia, and the model overestimated HR changes at a high intra-burst frequency when filtering was removed. The experiment outcomes indicate concurrent increases of stimulation amplitude and MPR modulate HR more than laryngeal EMG, and the model outcomes indicate that ganglia fidelity contributes to stimulation frequency effects on HR. The results impact the field of VNS through the invention of a minimally invasive method of stimulation to modulate neuroinflammation, demonstration of protective effects of VNS on delirium superimposed on dementia, and identification of stimulation adjustments to maximize a proxy for therapy over side effects.

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Deep brain stimulation (DBS) is an effective therapy for motor symptoms in Parkinson's disease (PD). DBS efficacy depends on the stimulation parameters, and the current gold standard therapy is high-frequency stimulation (>100 Hz) with constant interpulse intervals and short pulse widths (<210 μs). However, the temporal pattern of stimulation is a novel parameter dimension that has not been thoroughly explored. We used non-regular temporal patterns of DBS to pursue two goals: to better understand the mechanisms of DBS, and to increase the efficacy and efficiency of DBS for PD.

First, we designed high frequency patterns of non-regular stimulation with distinct features proposed to be important for efficacy and evaluated these patterns in human subjects with PD. Unexpectedly, some non-regular patterns of stimulation improved performance of an alternating finger-tapping task-a proxy for bradykinesia-compared to high frequency regular stimulation. Performance in the motor task was correlated with suppression of beta band power in a computational model of the basal ganglia suggesting a possible mechanism for effective stimulation patterns.

Inspired by the increased clinical efficacy of non-regular patterns of stimulation with high average frequencies, we developed a non-regular pattern of stimulation that reduced motor symptoms in PD using a low average stimulation frequency. Since the number of potential combinations of interpulse intervals is exceedingly large and it is unclear how such timing should be selected, we applied computational evolution to design an optimal temporal pattern of deep brain stimulation to treat the symptoms of PD. Next, we demonstrated the efficacy of the resulting pattern of stimulation in hemi-parkinsonian rats and humans with PD. Both the optimized stimulation pattern and high frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia in the rat and human, providing a shared mechanism of action for effective stimulation patterns. This innovation could allow patients to achieve battery life savings compared to traditional high frequency stimulation, which will reduce the costs and risks of frequent battery replacement procedures. Further, our approach can be used to design novel temporal patterns of stimulation in other applications of neural stimulation.

Finally, we explored evoked field potentials in the subthalamic nucleus (STN) in response to DBS. These potentials were evoked by stimulation through one of the contacts on the DBS lead and recorded from the two surrounding contacts. Subthalamic DBS local evoked potentials (DLEPs) have never before been recorded. We characterized the DLEPs, differences across DBS frequencies and time, their relationship to beta frequency oscillations and phase-amplitude coupling, and their dependence on electrode contact location.

A 3-dimensional biophysical model of DBS in the subthalamic nucleus-globus pallidus externus (GPe) subcircuit was built to explore the neural origin of the DLEPs. The computational model could reproduce the DLEP signal, and it revealed that the quasi-periodic DLEP oscillations are caused by excitatory synaptic currents in STN interrupted periodically by inhibition from GPe.

DLEP power was correlated with beta band oscillation power in the recordings without DBS, and significant phase-amplitude coupling was observed in a subset of subjects with robust DLEP responses. Together, all available evidence suggested the contact location was an important determinant for the presence and characteristics of DLEP signals. Predictions were made concerning contact location relative to the boundaries of the STN based on the DLEP recordings and insights gained using the computational model, and the predictions were in agreement with blinded post hoc imaging based contact localization for ~70% of contacts predicted to be within STN.

DLEPs are an exciting new signal with several useful applications. DLEPs could help neurosurgeons verify accurate DBS lead placement or optimal stimulation parameters, probe the pathological basal ganglia, and elucidate the mechanisms of DBS.

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