Models Of Deep Brain Stimulation Research Articles

Nonlinear effects at the electrode-tissue interface of deep brain stimulation electrodes.

Objective.The electrode-tissue interface provides the critical path for charge transfer in neurostimulation therapies and exhibits well-established nonlinear properties at high applied currents or voltages. These nonlinear properties may influence the efficacy and safety of applied stimulation but are typically neglected in computational models. In this study, nonlinear behavior of the electrode-tissue interface impedance was incorporated in a computational model of deep brain stimulation (DBS) to simulate the impact on neural activation and safety considerations.Approach.Nonlinear electrode-tissue interface properties were incorporated in a finite element model of DBS electrodesin vitroandin vivo,in the rat subthalamic nucleus, using an iterative approach. The transition point from linear to nonlinear behavior was determined for voltage and current-controlled stimulation. Predicted levels of neural activation during DBS were examined and the region of linear operation of the electrode was compared with the Shannon safety limit.Main results.A clear transition of the electrode-tissue interface impedance to nonlinear behavior was observed for both current and voltage-controlled stimulation. The transition occurred at lower values of activation overpotential for simulatedin vivothanin vitroconditions (91 mV and 165 mV respectively for current-controlled stimulation; 110 mV and 275 mV for voltage-controlled stimulation), corresponding to an applied current of 30μA and 45μA, or voltage of 330 mV at 1 kHz. The onset of nonlinearity occurred at lower values of the overpotential as frequency was increased. Incorporation of nonlinear properties resulted in activation of a higher proportion of neurons under voltage-controlled stimulation. Under current-controlled stimulation, the predicted transition to nonlinear behavior and Faradaic charge transfer at stimulation amplitudes of 30μA, corresponds to a charge density of 2.29μC cm-2and charge of 1.8 nC, well-below the Shannon safety limit.Significance.The results indicate that DBS electrodes may operate within the nonlinear region at clinically relevant stimulation amplitudes. This affects the extent of neural activation under voltage-controlled stimulation and the transition to Faradaic charge transfer for both voltage- and current-controlled stimulation with important implications for targeting of neural populations and the design of safe stimulation protocols.

Quantifying the Variability Associated with Postoperative Localization of Deep Brain Stimulation Electrodes

Introduction: Computational models of deep brain stimulation (DBS) have become common tools in clinical research studies that attempt to establish correlations between stimulation locations in the brain and behavioral outcome measures. However, the accuracy of any patient-specific DBS model depends heavily upon accurate localization of the DBS electrodes within the anatomy, which is typically defined via co-registration of clinical CT and MRI datasets. Several different approaches exist for this challenging registration problem, and each approach will result in a slightly different electrode localization. The goal of this study was to better understand how different processing steps (e.g., cost-function masking, brain extraction, intensity remapping) affect the estimate of the DBS electrode location in the brain. Methods: No “gold standard” exists for this kind of analysis, as the exact location of the electrode in the living human brain cannot be determined with existing clinical imaging approaches. However, we can estimate the uncertainty associated with the electrode position, which can be used to guide statistical analyses in DBS mapping studies. Therefore, we used high-quality clinical datasets from 10 subthalamic DBS subjects and co-registered their long-term postoperative CT with their preoperative surgical targeting MRI using 9 different approaches. The distances separating all of the electrode location estimates were calculated for each subject. Results: On average, electrodes were located within a median distance of 0.57 mm (0.49–0.74) of one another across the different registration approaches. However, when considering electrode location estimates from short-term postoperative CTs, the median distance increased to 2.01 mm (1.55–2.78). Conclusions: The results of this study suggest that electrode location uncertainty needs to be factored into statistical analyses that attempt to define correlations between stimulation locations and clinical outcomes.

Acute deep brain stimulation of the paraventricular nucleus of the hypothalamus increases brown adipose tissue thermogenesis in rats

Brown adipose tissue (BAT) activity is controlled by the sympathetic nervous system. Activation of BAT has shown significant promise in preclinical studies to elicit weight loss. Since the hypothalamic paraventricular nucleus (PVN) contributes to the regulation of BAT thermogenic activity, we sought to determine the effects of electrical stimulation of the PVN as a model of deep brain stimulation (DBS) for increasing BAT sympathetic nerve activity (SNA). The rostral raphe pallidus area (rRPa) was also chosen as a target for DBS since it contains the sympathetic premotor neurons for BAT. Electrical stimulation (100 µA, 100 µs, 100 Hz, for 5 min at a 50 % duty cycle) of the PVN increased BAT SNA and BAT thermogenesis. These effects were prevented by a local nanoinjection of bicuculline, a GABAA receptor antagonist. We suggest that electrical stimulation of the PVN elicited local release of GABA, which inhibited BAT sympathoinhibitory neurons in PVN, thereby releasing a restraint on BAT SNA. Electrical stimulation of the rRPa inhibited BAT thermogenesis and this was prevented by a local nanoinjection of bicuculline, suggesting that local release of GABA suppressed BAT SNA. Electrical stimulation of the PVN activates BAT metabolism via a mechanism that may include activation of local GABAA receptors. These findings contribute to our understanding of the mechanisms underlying the effects of DBS in the regulation of fat metabolism and provide a foundation for further DBS studies targeting hypothalamic circuits regulating BAT thermogenesis as a therapy for obesity.

Efficient suppression of parkinsonian beta oscillations in a closed-loop model of deep brain stimulation with amplitude modulation.

Parkinson's disease (PD) is a movement disorder characterized by the pathological beta band (15-30 Hz) neural oscillations within the basal ganglia (BG). It is shown that the suppression of abnormal beta oscillations is correlated with the improvement of PD motor symptoms, which is a goal of standard therapies including deep brain stimulation (DBS). To overcome the stimulation-induced side effects and inefficiencies of conventional DBS (cDBS) and to reduce the administered stimulation current, closed-loop adaptive DBS (aDBS) techniques were developed. In this method, the frequency and/or amplitude of stimulation are modulated based on various disease biomarkers. Here, by computational modeling of a cortico-BG-thalamic network in normal and PD conditions, we show that closed-loop aDBS of the subthalamic nucleus (STN) with amplitude modulation leads to a more effective suppression of pathological beta oscillations within the parkinsonian BG. Our results show that beta band neural oscillations are restored to their normal range and the reliability of the response of the thalamic neurons to motor cortex commands is retained due to aDBS with amplitude modulation. Furthermore, notably less stimulation current is administered during aDBS compared with cDBS due to a closed-loop control of stimulation amplitude based on the STN local field potential (LFP) beta activity. Efficient models of closed-loop stimulation may contribute to the clinical development of optimized aDBS techniques designed to reduce potential stimulation-induced side effects of cDBS in PD patients while leading to a better therapeutic outcome.

Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems.

We studied the effects of using fractional order proportional, integral, and derivative (PID) controllers in a closed-loop mathematical model of deep brain stimulation. The objective of the controller was to dampen oscillations from a neural network model of Parkinson's disease. We varied intrinsic parameters, such as the gain of the controller, and extrinsic variables, such as the excitability of the network. We found that in most cases, fractional order components increased the robustness of the model multi-fold to changes in the gains of the controller. Similarly, the controller could be set to a fixed set of gains and remain stable to a much larger range, than for the classical PID case, of changes in synaptic weights that otherwise would cause oscillatory activity. The increase in robustness is a consequence of the properties of fractional order derivatives that provide an intrinsic memory trace of past activity, which works as a negative feedback system. Fractional order PID controllers could provide a platform to develop stand-alone closed-loop deep brain stimulation systems.

Reviewing and explaining the use of Kuramoto oscillators in models of deep brain stimulation

Reviewing and explaining the use of Kuramoto oscillators in models of deep brain stimulation

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells.

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

A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation

Detailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. Predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results. We developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations. The DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%. DF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.

The ‘virtual DBS population’: five realistic computational models of deep brain stimulation patients for electromagnetic MR safety studies

We design, develop, and disseminate a ‘virtual population’ of five realistic computational models of deep brain stimulation (DBS) patients for electromagnetic (EM) analysis. We found five DBS patients in our institution’ research patient database who received high quality post-DBS surgery computer tomography (CT) examinations of the head and neck. Three patients have a single implanted pulse generator (IPG) and the two others have two IPGs (one for each lead). Moreover, one patient has two abandoned leads on each side of the head. For each patient, we combined the head and neck volumes into a ‘virtual CT’, from which we extracted the full-length DBS path including the IPG, extension cables, and leads. We corrected topology errors in this path, such as self-intersections, using a previously published optimization procedure. We segmented the virtual CT volume into bones, internal air, and soft tissue classes and created two-manifold, watertight surface meshes of these distributions. In addition, we added a segmented model of the brain (grey matter, white matter, eyes and cerebrospinal fluid) to one of the model (nickname Freddie) that was derived from a T1-weighted MR image obtained prior to the DBS implantation. We simulated the EM fields and specific absorption rate (SAR) induced at 3 Tesla by a quadrature birdcage body coil in each of the five patient models using a co-simulation strategy. We found that inter-subject peak SAR variability across models was independent of the target averaging mass and equal to ~45%. In our simulations of the full brain segmentation and six simplified versions of the Freddie model, the error associated with incorrect dielectric property assignment around the DBS electrodes was greater than the error associated with modeling the whole model as a single tissue class. Our DBS patient models are freely available on our lab website (Webpage of the Martinos Center Phantom Resource 2018 https://phantoms.martinos.org/Main_Page).

Suppression of Parkinsonian Beta Oscillations by Deep Brain Stimulation: Determination of Effective Protocols.

A neural field model of the corticothalamic-basal ganglia system is developed that describes enhanced beta activity within subthalamic and pallidal circuits in Parkinson's disease (PD) via system resonances. A model of deep brain stimulation (DBS) of typical clinical targets, the subthalamic nucleus (STN) and globus pallidus internus (GPi), is added and studied for several distinct stimulation protocols that are used for treatment of the motor symptoms of PD and that reduce pathological beta band activity (13–30 Hz) in the corticothalamic-basal ganglia network. The resulting impact of DBS on enhanced beta activity in the STN and GPi, as well as cortico-subthalamic and cortico-pallidal coherence, are studied. Both STN-DBS and GPi-DBS are found to be effective for suppressing peak STN and GPi power in the beta band, with GPi-DBS being slightly more effective in both the STN and the GPi for all stimulus protocols tested. The largest decrease in cortico-STN coherence is observed during STN-DBS, whereas GPi-DBS is most effective for reducing cortico-GPi coherence. A reduction of the pathologically large STN connection strengths that define the parkinsonian state results in enhanced 6 Hz activity and could thus represent a compensatory mechanism that has the side effect of driving parkinsonian tremor-like oscillations. This model provides a method for systematically testing effective DBS protocols that agrees with experimental and clinical findings. Furthermore, the model suggests GPi-DBS and STN-DBS have distinct impacts on elevated synchronization between the basal ganglia and motor cortex in PD.

The intranuclear length of the electrode trajectory according to the ‘port’ model for deep brain stimulation of the nucleus accumbens

I read the article by Zhang et al . (1), on deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) for the treatment of neuropsychiatric disease, with great interest. They describe the development of a new electrode for simultaneous and independently programmed stimulation of the nucleus accumbens (NAc) and the anterior limb of the internal capsule (ALIC) based on the anatomical features of the VC/VS (1). The purpose of this communication is to comment on some aspects of the anatomy of the NAc as reported by Zhang et al . (1).

Adaptive Estimation of the Neural Activation Extent in Computational Volume Conductor Models of Deep Brain Stimulation.

The aim of this study is to propose an adaptive scheme embedded into an open-source environment for the estimation of the neural activation extent during deep brain stimulation and to investigate the feasibility of approximating the neural activation extent by thresholds of the field solution. Open-source solutions for solving the field equation in volume conductor models of deep brain stimulation and computing the neural activation are embedded into a Python package to estimate the neural activation dependent on the dielectric tissue properties and axon parameters by employing a spatially adaptive scheme. Feasibility of the approximation of the neural activation extent by field thresholds is investigated to further reduce the computational expense. The varying extents of neural activation for different patient-specific dielectric properties were estimated with the adaptive scheme. The results revealed the strong influence of the dielectric properties of the encapsulation layer in the acute and chronic phase after surgery. The computational time required to determine the neural activation extent in each studied model case was substantially reduced. The neural activation extent is altered by patient-specific parameters. Threshold values of the electric potential and electric field norm facilitate a computationally efficient method to estimate the neural activation extent. The presented adaptive scheme is able to robustly determine neural activation extents and field threshold estimates for varying dielectric tissue properties and axon diameters while substantially reducing the computational expense.

An animal model of deep brain stimulation for treating tinnitus: A proof of concept study.

This proof-of-concept study aimed to demonstrate therapeutic effects of deep brain stimulation (DBS) on noise-induced tinnitus. Experimental animal study. After Institutional Animal Care and Use Committee approval, nine adult rats were implanted in the caudate nucleus with custom-made electrode array. The rats were exposed to noise to induce tinnitus. Auditory brainstem response was performed to evaluate hearing threshold changes. Noise-induced tinnitus and its suppression by DBS were evaluated using the gap-detection acoustic startle reflex behavioral paradigm and electrophysiological evaluation of modulatory effects on neural correlates of tinnitus. Various stimulation parameters were used to determine the most effective ones in affecting behavioral changes, along with corresponding neural activity in the caudate nucleus. The correlation between the caudate nucleus and auditory cortex also was determined. Analysis of variance with Bonferroni correction was performed to examine DBS-induced effects on behavioral evidence of tinnitus. Bursting activity, a neural marker of tinnitus, was noted to decrease compared to baseline in tinnitus (+) animals. After stimulation, spontaneous and bursting activity increased in the tinnitus (+) animals but decreased in the tinnitus (-) animals. Behavioral data suggested suppression of tinnitus after DBS. These effects lasted up to 5 days. To our knowledge, this is the first development of an animal model to test deep brain stimulation of the caudate region for the treatment of tinnitus. Deep brain stimulation of the caudate nucleus can modulate tinnitus in a rat model of tinnitus. NA. Laryngoscope, 128:1213-1222, 2018.

WINCS Harmoni: Closed-loop dynamic neurochemical control of therapeutic interventions

There has been significant progress in understanding the role of neurotransmitters in normal and pathologic brain function. However, preclinical trials aimed at improving therapeutic interventions do not take advantage of real-time in vivo neurochemical changes in dynamic brain processes such as disease progression and response to pharmacologic, cognitive, behavioral, and neuromodulation therapies. This is due in part to a lack of flexible research tools that allow in vivo measurement of the dynamic changes in brain chemistry. Here, we present a research platform, WINCS Harmoni, which can measure in vivo neurochemical activity simultaneously across multiple anatomical targets to study normal and pathologic brain function. In addition, WINCS Harmoni can provide real-time neurochemical feedback for closed-loop control of neurochemical levels via its synchronized stimulation and neurochemical sensing capabilities. We demonstrate these and other key features of this platform in non-human primate, swine, and rodent models of deep brain stimulation (DBS). Ultimately, systems like the one described here will improve our understanding of the dynamics of brain physiology in the context of neurologic disease and therapeutic interventions, which may lead to the development of precision medicine and personalized therapies for optimal therapeutic efficacy.

Impedance detection of the electrical resistivity of the wound tissue around deep brain stimulation electrodes permits registration of the encapsulation process in a rat model

Abstract An animal model of deep brain stimulation (DBS) was used in in vivo studies of the encapsulation process of custom-made platinum/iridium microelectrodes in the subthalamic nucleus of hemiparkinsonian rats via electrical impedance spectroscopy. Two electrode types with 100-μm bared tips were used: i) a unipolar electrode with a 200-μm diameter and a subcutaneous gold wire counter electrode and ii) a bipolar electrode with two parallelshifted 125-μm wires. Miniaturized current-controlled pulse generators (130 Hz, 200 μA, 60 μs) enabled chronic DBS of the freely moving animals. A phenomenological electrical model enabled recalculation of the resistivity of the wound tissue around the electrodes from daily in vivo recordings of the electrode impedance over two weeks. In contrast to the commonly used 1 kHz impedance, the resistivity is independent of frequency, electrode properties, and current density. It represents the ionic DC properties of the tissue. Significant resistivity changes were detected with a characteristic decrease at approximately the 2nd day after implantation. The maximum resistivity was reached before electrical stimulation was initiated on the 8th day, which resulted in a decrease in resistivity. Compared with the unipolar electrodes, the bipolar electrodes exhibited an increased sensitivity for the tissue resistivity.

Role of Soft-Tissue Heterogeneity in Computational Models of Deep Brain Stimulation.

Bioelectric field models of deep brain stimulation (DBS) are commonly utilized in research and industrial applications. However, the wide range of different representations used for the human head in these models may be responsible for substantial variance in the stimulation predictions. Determine the relative error of ignoring cerebral vasculature and soft-tissue heterogeneity outside of the brain in computational models of DBS. We used a detailed atlas of the human head, coupled to magnetic resonance imaging data, to construct a range of subthalamic DBS volume conductor models. We incrementally simplified the most detailed base model and quantified changes in the stimulation thresholds for direct activation of corticofugal axons. Ignoring cerebral vasculature altered predictions of stimulation thresholds by <10%, whereas ignoring soft-tissue heterogeneity outside of the brain altered predictions between -44 % and 174%. Heterogeneity in the soft tissues of the head, if unaccounted for, introduces a degree of uncertainty in predicting electrical stimulation of neural elements that is not negligible and thereby warrants consideration in future modeling studies.

Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation

Objective. Deep brain stimulation (DBS) is an adjunctive therapy that is effective in treating movement disorders and shows promise for treating psychiatric disorders. Computational models of DBS have begun to be utilized as tools to optimize the therapy. Despite advancements in the anatomical accuracy of these models, there is still uncertainty as to what level of electrical complexity is adequate for modeling the electric field in the brain and the subsequent neural response to the stimulation. Approach. We used magnetic resonance images to create an image-based computational model of subthalamic DBS. The complexity of the volume conductor model was increased by incrementally including heterogeneity, anisotropy, and dielectric dispersion in the electrical properties of the brain. We quantified changes in the load of the electrode, the electric potential distribution, and stimulation thresholds of descending corticofugal (DCF) axon models. Main results. Incorporation of heterogeneity altered the electric potentials and subsequent stimulation thresholds, but to a lesser degree than incorporation of anisotropy. Additionally, the results were sensitive to the choice of method for defining anisotropy, with stimulation thresholds of DCF axons changing by as much as 190%. Typical approaches for defining anisotropy underestimate the expected load of the stimulation electrode, which led to underestimation of the extent of stimulation. More accurate predictions of the electrode load were achieved with alternative approaches for defining anisotropy. The effects of dielectric dispersion were small compared to the effects of heterogeneity and anisotropy. Significance. The results of this study help delineate the level of detail that is required to accurately model electric fields generated by DBS electrodes.

Spherical statistics for characterizing the spatial distribution of deep brain stimulation effects on neuronal activity

BackgroundComputational models of deep brain stimulation (DBS) have played a key role in understanding its physiological mechanisms. By estimating a volume of tissue directly modulated by DBS, one can relate the neuronal pathways within those volumes to the therapeutic efficacy of a particular DBS setting. New methodA spherical statistical framework is described to quantify and determine salient features of such morphologies using visualization techniques, empirical shape analysis, and formal hypothesis testing. This framework is shown using a 3D model of thalamocortical neurons surrounding a radially-segmented DBS array. ResultsWe show that neuronal population volumes modulated by various DBS electrode configurations can be characterized by parametric distribution models, such as Kent and Watson girdle models. Distribution parameters were found to change with stimulus settings, including amplitude and radial distance from the DBS array. Increasing stimulation amplitude through a single electrode resulted in more diffuse neuronal activation and increased rotational symmetry about the mean direction of the activated population. When stimulation amplitude was held constant, the activated neuronal population distribution was more concentrated with distance from the DBS array and was also more rotationally asymmetric. We also show how data representation (e.g. stimulus-entrained cell body vs. axon node) can significantly alter model distribution shape. Comparison to existing methodsThis statistical framework provides a quantitative method to analyze the spatial morphologies of DBS-induced effects on neuronal activity. ConclusionsThe application of spherical statistics to assess spatial distributions of neuronal activity has potential usefulness for numerous other recording, labeling, and stimulation modalities.

Improved stereotactic procedure enhances the accuracy of deep brain stimulation electrode implantation in non-human primates

Purpose: Non-human primate models of deep brain stimulation (DBS) play an increasingly important role in the exploration of DBS mechanisms. The establishment and recognized usefulness of such models depend on the precise positioning of the stimulating targets and electrode implants. The optimal method of targeting remains controversial. Materials and methods: This paper described an improved stereotactic procedure that uses a self-developed adaptor to improve accuracy. This involved: (1) connecting clinical stereotactic devices with the skull of primates using a self-developed adaptor; (2) pre-operation targeting via magnetic resonance imaging (MRI); (3) target re-checking by microelectrode recording (MER); (4) DBS electrode implantation; (5) post-operative MRI verification of electrode placement and (6) positioning confirmation by DBS programming. Results: Use of the adaptor enabled clinical stereotactic surgery, pre-operative MRI targeting, microelectrode mapping and post-operative verification in primate DBS operations. Discrepancies between achieved and predetermined electrode position were around 0.6 mm. DBS programming improved the motor function of the hemiparkinsonism animals and decreased the numbers of rotation induced by apomorphine, indicating the precise positioning of the stimulating target and successful implanting of electrode using this method. Conclusions: An improved stereotactic procedure was performed during a non-human primate DBS operation using a self-developed adaptor. The accuracy of DBS electrode implantation in non-human primates was improved with this method.

Application of Describing Function Analysis to a Model of Deep Brain Stimulation

Deep brain stimulation effectively alleviates motor symptoms of medically refractory Parkinson's disease, and also relieves many other treatment-resistant movement and affective disorders. Despite its relative success as a treatment option, the basis of its efficacy remains elusive. In Parkinson's disease, increased functional connectivity and oscillatory activity occur within the basal ganglia as a result of dopamine loss. A correlative relationship between pathological oscillatory activity and the motor symptoms of the disease, in particular bradykinesia, rigidity, and tremor, has been established. Suppression of the oscillations by either dopamine replacement or DBS also correlates with an improvement in motor symptoms. DBS parameters are currently chosen empirically using a "trial and error" approach, which can be time-consuming and costly. The work presented here amalgamates concepts from theories of neural network modeling with nonlinear control engineering to describe and analyze a model of synchronous neural activity and applied stimulation. A theoretical expression for the optimum stimulation parameters necessary to suppress oscillations is derived. The effect of changing stimulation parameters (amplitude and pulse duration) on induced oscillations is studied in the model. Increasing either stimulation pulse duration or amplitude enhanced the level of suppression. The predicted parameters were found to agree well with clinical measurements reported in the literature for individual patients. It is anticipated that the simplified model described may facilitate the development of protocols to aid optimum stimulation parameter choice on a patient by patient basis.