Toward feedback controlled deep brain stimulation: dynamics of glutamate release in the subthalamic nucleus in rats

In Search of a Feedback Signal for Closed-Loop Deep Brain Stimulation: Stimulation of the Subthalamic Nucleus Reveals Altered Glutamate Dynamics in the Globus Pallidus in Anesthetized, 6-Hydroxydopamine-Treated Rats

Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is a surgical procedure for alleviating motor symptoms of Parkinson's Disease (PD). The pattern of DBS (e.g., the electrode pairs used and the intensity of stimulation) is usually optimized by trial and error based on a subjective evaluation of motor function. We tested the hypotheses that DBS releases glutamate in selected basal ganglia nuclei and that the creation of 6-hydroxydopamine (6-OHDA)-induced nigrostriatal lesions alters glutamate release during DBS in those basal ganglia nuclei. We studied the relationship between a pseudo-random binary sequence of DBS and glutamate levels in the STN itself or in the globus pallidus (GP) in anesthetized, control, and 6-OHDA-treated rats. We characterized the stimulus-response relationships between DBS and glutamate levels using a transfer function estimated using System Identification. Stimulation of the STN elevated glutamate levels in the GP and in the STN. Although the 6-OHDA treatment did not affect glutamate dynamics in the STN during DBS in the STN, the transfer function between DBS in the STN and glutamate levels in the GP was significantly altered by the presence or absence of 6-OHDA-induced lesions. Thus, glutamate responses in the GP in the 6-OHDA-treated animals (but not in the STN) depended on dopaminergic inputs. For this reason, measuring glutamate levels in the GP may provide a useful feedback target in a closed-loop DBS device in patients with PD since the dynamics of glutamate release in the GP during DBS seem to reflect the loss of dopaminergic neurons in the SNc.

Anesthesia and analgesia for experimental craniotomy in mice and rats: a systematic scoping review comparing the years 2009 and 2019

Experimental craniotomies are a common surgical procedure in neuroscience. Because inadequate analgesia appears to be a problem in animal-based research, we conducted this review and collected information on management of craniotomy-associated pain in laboratory mice and rats. A comprehensive search and screening resulted in the identification of 2235 studies, published in 2009 and 2019, describing craniotomy in mice and/or rats. While key features were extracted from all studies, detailed information was extracted from a random subset of 100 studies/year. Reporting of perioperative analgesia increased from 2009 to 2019. However, the majority of studies from both years did not report pharmacologic pain management. Moreover, reporting of multimodal treatments remained at a low level, and monotherapeutic approaches were more common. Among drug groups, reporting of pre- and postoperative administration of non-steroidal anti-inflammatory drugs, opioids, and local anesthetics in 2019 exceeded that of 2009. In summary, these results suggest that inadequate analgesia and oligoanalgesia are persistent issues associated with experimental intracranial surgery. This underscores the need for intensified training of those working with laboratory rodents subjected to craniotomies.

Systematic review registration

https://osf.io/7d4qe.

Advances in neurochemical measurements: A review of biomarkers and devices for the development of closed-loop deep brain stimulation systems

Neurochemical recording techniques have expanded our understanding of the pathophysiology of neurological disorders, as well as the mechanisms of action of treatment modalities like deep brain stimulation (DBS). DBS is used to treat diseases such as Parkinson's disease, Tourette syndrome, and obsessive-compulsive disorder, among others. Although DBS is effective at alleviating symptoms related to these diseases and improving the quality of life of these patients, the mechanism of action of DBS is currently not fully understood. A leading hypothesis is that DBS modulates the electrical field potential by modifying neuronal firing frequencies to non-pathological rates thus providing therapeutic relief. To address this gap in knowledge, recent advances in electrochemical sensing techniques have given insight into the importance of neurotransmitters, such as dopamine, serotonin, glutamate, and adenosine, in disease pathophysiology. These studies have also highlighted their potential use in tandem with electrophysiology to serve as biomarkers in disease diagnosis and progression monitoring, as well as characterize response to treatment. Here, we provide an overview of disease-relevant neurotransmitters and their roles and implications as biomarkers, as well as innovations to the biosensors used to record these biomarkers. Furthermore, we discuss currently available neurochemical and electrophysiological recording devices, and discuss their viability to be implemented into the development of a closed-loop DBS system.

Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), chloride (Cl⁻), hydrogen (H⁺) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.

Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release

Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinson's disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status. A key element to achieving these precision therapeutic interventions is the development of mathematical modeling approaches capable of describing the nonlinear transfer function between neuromodulation parameters and evoked neurochemical changes. Here, we propose two computational modeling frameworks, based on artificial neural networks (ANNs) and Volterra kernels, that can characterize the input/output transfer functions of stimulation-evoked neurochemical release. We evaluate the ability of these modeling frameworks to characterize subject-specific neurochemical kinetics by accurately describing stimulation-evoked dopamine release across rodent (R² = 0.83 Volterra kernel, R² = 0.86 ANN), swine (R² = 0.90 Volterra kernel, R² = 0.93 ANN), and non-human primate (R² = 0.98 Volterra kernel, R² = 0.96 ANN) models of brain stimulation. Ultimately, these models will not only improve understanding of neurochemical signaling in healthy and diseased brains but also facilitate the development of neuromodulation strategies capable of controlling neurochemical release via closed-loop strategies.

Generation of Stimulus Triggering from Intracortical Spike Activity for Brain-Machine-Body Interfaces (BMBIs)

Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity. The approach features digital spike discrimination based on user-set thresholding and time-amplitude windowing, decision making to support different triggering patterns for various stimulation scenarios, as well as trigger-pattern-dependent blanking schemes for robust operation in the presence of stimulus artifacts. Readily lending itself to low-power, low-area implementation for future integration, the algorithm has been synthesized on a Cyclone II FPGA using Altera's Quartus II design software and validated experimentally with prerecorded intracortical neural spike activity from an anesthetized laboratory rat.

Neurochemostat: A Neural Interface SoC With Integrated Chemometrics for Closed-Loop Regulation of Brain Dopamine

This paper presents a 3.3×3.2 mm(2) system-on-chip (SoC) fabricated in AMS 0.35 μm 2P/4M CMOS for closed-loop regulation of brain dopamine. The SoC uniquely integrates neurochemical sensing, on-the-fly chemometrics, and feedback-controlled electrical stimulation to realize a "neurochemostat" by maintaining brain levels of electrically evoked dopamine between two user-set thresholds. The SoC incorporates a 90 μW, custom-designed, digital signal processing (DSP) unit for real-time processing of neurochemical data obtained by 400 V/s fast-scan cyclic voltammetry (FSCV) with a carbon-fiber microelectrode (CFM). Specifically, the DSP unit executes a chemometrics algorithm based upon principal component regression (PCR) to resolve in real time electrically evoked brain dopamine levels from pH change and CFM background-current drift, two common interferents encountered using FSCV with a CFM in vivo. Further, the DSP unit directly links the chemically resolved dopamine levels to the activation of the electrical microstimulator in on-off-keying (OOK) fashion. Measured results from benchtop testing, flow injection analysis (FIA), and biological experiments with an anesthetized rat are presented.

RECENT DEVELOPMENTS IN ELECTROCHEMICAL SENSORS FOR THE DETECTION OF NEUROTRANSMITTERS FOR APPLICATIONS IN BIOMEDICINE

Neurotransmitters are important biological molecules that are essential to many neurophysiological processes including memory, cognition, and behavioral states. The development of analytical methodologies to accurately detect neurotransmitters is of great importance in neurological and biological research. Specifically designed microelectrodes or microbiosensors have demonstrated potential for rapid, real-time measurements with high spatial resolution. Such devices can facilitate study of the role and mechanism of action of neurotransmitters and can find potential uses in biomedicine. This paper reviews the current status and recent advances in the development and application of electrochemical sensors for the detection of small-molecule neurotransmitters. Measurement challenges and opportunities of electroanalytical methods to advance study and understanding of neurotransmitters in various biological models and disease conditions are discussed.

Fluctuations in nucleus accumbens extracellular glutamate and glucose during motivated glucose-drinking behavior: dissecting the neurochemistry of reward

While motivated behavior involves multiple neurochemical systems, few studies have focused on the role of glutamate, the brain's excitatory neurotransmitter, and glucose, the energetic substrate of neural activity in reward-related neural processes. Here, we used high-speed amperometry with enzyme-based substrate-sensitive and control, enzyme-free biosensors to examine second-scale fluctuations in the extracellular levels of these substances in the nucleus accumbens shell during glucose-drinking behavior in trained rats. Glutamate rose rapidly after the presentation of a glucose-containing cup and before the initiation of drinking (reward seeking), decreased more slowly to levels below baseline during consumption (sensory reward), and returned to baseline when the ingested glucose reached the brain (metabolic reward). When water was substituted for glucose, glutamate rapidly increased with cup presentation and in contrast to glucose drinking, increased above baseline after rats tasted the water and refused to drink further. Therefore, extracellular glutamate show distinct changes associated with key events of motivated drinking behavior and opposite dynamics during sensory and metabolic components of reward. In contrast to glutamate, glucose increased at each stimulus and behavioral event, showing a sustained elevation during the entire behavior and a robust post-ingestion rise that correlated with the gradual return of glutamate levels to their baseline. By comparing active drinking with passive intra-gastric glucose delivery, we revealed that fluctuations in extracellular glucose are highly dynamic, reflecting a balance between rapid delivery because of neural activity, intense metabolism, and the influence of ingested glucose reaching the brain.

Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis

Movement is planned and coordinated by the brain and carried out by contracting muscles acting on specific joints. Motor commands initiated in the brain travel through descending pathways in the spinal cord to effector motor neurons before reaching target muscles. Damage to these pathways by spinal cord injury (SCI) can result in paralysis below the injury level. However, the planning and coordination centers of the brain, as well as peripheral nerves and the muscles that they act upon, remain functional. Neuroprosthetic devices can restore motor function following SCI by direct electrical stimulation of the neuromuscular system. Unfortunately, conventional neuroprosthetic techniques are limited by a myriad of factors that include, but are not limited to, a lack of characterization of non-linear input/output system dynamics, mechanical coupling, limited number of degrees of freedom, high power consumption, large device size, and rapid onset of muscle fatigue. Wireless multi-channel closed-loop neuroprostheses that integrate command signals from the brain with sensor-based feedback from the environment and the system's state offer the possibility of increasing device performance, ultimately improving quality of life for people with SCI. In this manuscript, we review neuroprosthetic technology for improving functional restoration following SCI and describe brain-machine interfaces suitable for control of neuroprosthetic systems with multiple degrees of freedom. Additionally, we discuss novel stimulation paradigms that can improve synergy with higher planning centers and improve fatigue-resistant activation of paralyzed muscles. In the near future, integration of these technologies will provide SCI survivors with versatile closed-loop neuroprosthetic systems for restoring function to paralyzed muscles.

A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies

Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a “smart” neuroprosthetic system for treatment of neurologic and psychiatric disorders.

Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation

This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations.

System Identification of mGluR-Dependent Long-Term Depression

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)–dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.

Principles of electrical stimulation of neural tissue

Deep brain stimulation is a remarkable therapy that has mainstreamed electrical stimulation of the brain for the treatment of neurological dysfunction. To appreciate the mechanisms of deep brain stimulation, we need to understand the excitability of neural tissue. Here, we survey the pertinent principles of electrical excitation in the brain. The amount of current delivered and the tissue conductivity together determine the strength and extent of potentials generated by stimulation. The electrode-tissue interface is an important junction where electrical charge carriers in the stimulation hardware are converted to ionic charge carriers in the tissue. Cathodic stimulation tends to depolarize neural elements more easily than anodic stimulation. The current-distance relationship describes how the amount of current needed to excite an axon increases as a function of its distance from the electrode. This relationship also depends on the axon's diameter because large-diameter axons are excited more easily than small-diameter axons. For a given axon, the strength-duration relationship describes the inverse relationship between threshold current amplitude and pulse duration. Specific stimulation parameters must be considered to avoid stimulation-induced tissue damage. A strong foundation in these principles facilitates understanding of the complex effects of electrical stimulation in the brain.

Organic bioelectronics for electronic-to-chemical translation in modulation of neuronal signaling and machine-to-brain interfacing

BACKGROUND

A major challenge when creating interfaces for the nervous system is to translate between the signal carriers of the nervous system (ions and neurotransmitters) and those of conventional electronics (electrons).

SCOPE OF REVIEW

Organic conjugated polymers represent a unique class of materials that utilizes both electrons and ions as charge carriers. Based on these materials, we have established a series of novel communication interfaces between electronic components and biological systems. The organic electronic ion pump (OEIP) presented in this review is made of the polymer-polyelectrolyte system poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). The OEIP translates electronic signals into electrophoretic migration of ions and neurotransmitters.

MAJOR CONCLUSIONS

We demonstrate how spatio-temporally controlled delivery of ions and neurotransmitters can be used to modulate intracellular Ca(2+) signaling in neuronal cells in the absence of convective disturbances. The electronic control of delivery enables strict control of dynamic parameters, such as amplitude and frequency of Ca(2+) responses, and can be used to generate temporal patterns mimicking naturally occurring Ca(2+) oscillations. To enable further control of the ionic signals we developed the electrophoretic chemical transistor, an analog of the traditional transistor used to amplify and/or switch electronic signals. Finally, we demonstrate the use of the OEIP in a new "machine-to-brain" interface by modulating brainstem responses in vivo.

GENERAL SIGNIFICANCE

This review highlights the potential of communication interfaces based on conjugated polymers in generating complex, high-resolution, signal patterns to control cell physiology. We foresee widespread applications for these devices in biomedical research and in future medical devices within multiple therapeutic areas. This article is part of a Special Issue entitled Organic Bioelectronics-Novel Applications in Biomedicine.

Rapid changes in extracellular glutamate induced by natural arousing stimuli and intravenous cocaine in the nucleus accumbens shell and core

Glutamate (Glu) is a major excitatory neurotransmitter, playing a crucial role in the functioning of the nucleus accumbens (NAc), a critical area implicated in somatosensory integration and regulation of motivated behavior. In this study, high-speed amperometry with enzyme-based biosensors was used in freely moving rats to examine changes in extracellular Glu in the NAc shell and core induced by a tone, tail pinch (TP), social interaction with a male conspecific (SI), and intravenous (iv) cocaine (1 mg/kg). To establish the contribution of Glu to electrochemical signal changes, similar recordings were conducted with null (Glu(0)) sensors, which were exposed to the same chemical and physical environment but were insensitive to Glu. TP, SI, and cocaine, but not a tone, induced relatively large and prolonged current increases detected by both Glu and Glu(0) sensors. However, current differentials revealed very rapid, much smaller, and transient increases in extracellular Glu levels, more predominantly in the NAc shell than core. In contrast to monophasic responses with natural stimuli, cocaine induced a biphasic Glu increase in the shell, with a transient peak during the injection and a slower postinjection peak. Therefore, Glu is phasically released in the NAc after exposure to natural arousing stimuli and cocaine; this release is rapid, stimulus dependent, and structure specific, suggesting its role in triggering neural and behavioral activation induced by these stimuli. This study also demonstrates the need for multiple in vitro and in vivo controls to reveal relatively small, highly phasic, and transient fluctuations in Glu levels occurring under behaviorally relevant conditions.

Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and parkinsonian states

Controlling the spatiotemporal firing pattern of an intricately connected network of neurons through microstimulation is highly desirable in many applications. We investigated in this paper the feasibility of using a model-based approach to the analysis and control of a basal ganglia (BG) network model of Hodgkin-Huxley (HH) spiking neurons through microstimulation. Detailed analysis of this network model suggests that it can reproduce the experimentally observed characteristics of BG neurons under a normal and a pathological Parkinsonian state. A simplified neuronal firing rate model, identified from the detailed HH network model, is shown to capture the essential network dynamics. Mathematical analysis of the simplified model reveals the presence of a systematic relationship between the network's structure and its dynamic response to spatiotemporally patterned microstimulation. We show that both the network synaptic organization and the local mechanism of microstimulation can impose tight constraints on the possible spatiotemporal firing patterns that can be generated by the microstimulated network, which may hinder the effectiveness of microstimulation to achieve a desired objective under certain conditions. Finally, we demonstrate that the feedback control design aided by the mathematical analysis of the simplified model is indeed effective in driving the BG network in the normal and Parskinsonian states to follow a prescribed spatiotemporal firing pattern. We further show that the rhythmic/oscillatory patterns that characterize a dopamine-depleted BG network can be suppressed as a direct consequence of controlling the spatiotemporal pattern of a subpopulation of the output Globus Pallidus internalis (GPi) neurons in the network. This work may provide plausible explanations for the mechanisms underlying the therapeutic effects of deep brain stimulation (DBS) in Parkinson's disease and pave the way towards a model-based, network level analysis and closed-loop control and optimization of DBS parameters, among many other applications.

Evolving refractory major depressive disorder diagnostic and treatment paradigms: toward closed-loop therapeutics

Current antidepressant therapies do not effectively control or cure depressive symptoms. Pharmaceutical therapies altogether fail to address an estimated 4 million Americans who suffer from a recurrent and severe treatment-resistant form of depression known as refractory major depressive disorder. Subjective diagnostic schemes, differing manifestations of the disorder, and antidepressant treatments with limited theoretical bases each contribute to the general lack of therapeutic efficacy and differing levels of treatment resistance in the refractory population. Stimulation-based therapies, such as vagus nerve stimulation, transcranial magnetic stimulation, and deep brain stimulation, are promising treatment alternatives for this treatment-resistant subset of patients, but are plagued with inconsistent reports of efficacy and variable side effects. Many of these problems stem from the unknown mechanisms of depressive disorder pathogenesis, which prevents the development of treatments that target the specific underlying causes of the disorder. Other problems likely arise due to the non-specific stimulation of various limbic and paralimbic structures in an open-loop configuration. This review critically assesses current literature on depressive disorder diagnostic methodologies, treatment schemes, and pathogenesis in order to emphasize the need for more stringent depressive disorder classifications, quantifiable biological markers that are suitable for objective diagnoses, and alternative closed-loop treatment options tailored to well-defined forms of the disorder. A closed-loop neurostimulation device design framework is proposed, utilizing symptom-linked biomarker abnormalities as control points for initiating and terminating a corrective electrical stimulus which is autonomously optimized for correcting the magnitude and direction of observed biomarker abnormality.

Local glutamate release in the rat ventral lateral thalamus evoked by high-frequency stimulation

Thalamic deep brain stimulation (DBS) is proven therapy for essential tremor, Parkinson's disease and Tourette's syndrome. We tested the hypothesis that high-frequency electrical stimulation results in local thalamic glutamate release. Enzyme-linked glutamate amperometric biosensors were implanted in anesthetized rat thalamus adjacent to the stimulating electrode. Electrical stimulation was delivered to investigate the effect of frequency, pulse width, voltage-controlled or current-controlled stimulation, and charge balancing. Monophasic electrical stimulation-induced glutamate release was linearly dependent on stimulation frequency, intensity and pulse width. Prolonged stimulation evoked glutamate release to a plateau that subsequently decayed back to baseline after stimulation. Glutamate release was less pronounced with voltage-controlled stimulation and not present with charge balanced current-controlled stimulation. Using fixed potential amperometry in combination with a glutamate bioprobe and adjacent microstimulating electrode, the present study has shown that monophasic current-controlled stimulation of the thalamus in the anesthetized rat evoked linear increases in local extracellular glutamate concentrations that were dependent on stimulation duration, frequency, intensity and pulse width. However, the efficacy of monophasic voltage-controlled stimulation, in terms of evoking glutamate release in the thalamus, was substantially lower compared to monophasic current-controlled stimulation and entirely absent with biphasic (charge balanced) current-controlled stimulation. It remains to be determined whether similar glutamate release occurs with human DBS electrodes and similar charge balanced stimulation. As such, the present results indicate the importance of evaluating local neurotransmitter dynamics in studying the mechanism of action of DBS.