Pallidal stimulation: effect of pattern and rate on bradykinesia in the non-human primate model of Parkinson's disease

Subthalamic nucleus deep brain stimulation driven by primary motor cortex γ2 activity in parkinsonian monkeys

In parkinsonism, subthalamic nucleus (STN) electrical deep brain stimulation (DBS) improves symptoms, but may be associated with side effects. Adaptive DBS (aDBS), which enables modulation of stimulation, may limit side effects, but limited information is available about clinical effectiveness and efficaciousness. We developed a brain-machine interface for aDBS, which enables modulation of stimulation parameters of STN-DBS in response to γ2 band activity (80-200 Hz) of local field potentials (LFPs) recorded from the primary motor cortex (M1), and tested its effectiveness in parkinsonian monkeys. We trained two monkeys to perform an upper limb reaching task and rendered them parkinsonian with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Bipolar intracortical recording electrodes were implanted in the M1, and a recording chamber was attached to access the STN. In aDBS, the M1 LFPs were recorded, filtered into the γ2 band, and discretized into logic pulses by a window discriminator, and the pulses were used to modulate the interval and amplitude of DBS pulses. In constant DBS (cDBS), constant stimulus intervals and amplitudes were used. Reaction and movement times during the task were measured and compared between aDBS and cDBS. The M1-γ2 activities were increased before and during movements in parkinsonian monkeys and these activities modulated the aDBS pulse interval, amplitude, and dispersion. With aDBS and cDBS, reaction and movement times were significantly decreased in comparison to DBS-OFF. The electric charge delivered was lower with aDBS than cDBS. M1-γ2 aDBS in parkinsonian monkeys resulted in clinical benefits that did not exceed those from cDBS. However, M1-γ2 aDBS achieved this magnitude of benefit for only two thirds of the charge delivered by cDBS. In conclusion, M1-γ2 aDBS is an effective therapeutic approach which requires a lower electrical charge delivery than cDBS for comparable clinical benefits.

Temporal patterns in electrical nerve stimulation: Burst gap code shapes tactile frequency perception

We have previously described a novel temporal encoding mechanism in the somatosensory system, where mechanical pulses grouped into periodic bursts create a perceived tactile frequency based on the duration of the silent gap between bursts, rather than the mean rate or the periodicity. This coding strategy may offer new opportunities for transmitting information to the brain using various sensory neural prostheses and haptic interfaces. However, it was not known whether the same coding mechanisms apply when using electrical stimulation, which recruits a different spectrum of afferents. Here, we demonstrate that the predictions of the burst gap coding model for frequency perception apply to burst stimuli delivered with electrical pulses, re-emphasising the importance of the temporal structure of spike patterns in neural processing and perception of tactile stimuli. Reciprocally, the electrical stimulation data confirm that the results observed with mechanical stimulation do indeed depend on neural processing mechanisms in the central nervous system, and are not due to skin mechanical factors and resulting patterns of afferent activation.

A Framework for Adapting Deep Brain Stimulation Using Parkinsonian State Estimates

The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s.

Development of a miniature device for emerging deep brain stimulation paradigms

Deep brain stimulation (DBS) is a neuromodulatory approach for treatment of several neurological and psychiatric disorders. A new focus on optimising the waveforms used for stimulation is emerging regarding the mechanism of DBS treatment. Many existing DBS devices offer only a limited set of predefined waveforms, mainly rectangular, and hence are inapt for exploring the emerging paradigm. Advances in clinical DBS are moving towards incorporating new stimulation parameters, yet we remain limited in our capacity to test these in animal models, arguably a critical first step. Accordingly, there is a need for the development of new miniature, low-power devices to enable investigation into the new DBS paradigms in preclinical settings. The ideal device would allow for flexibility in the stimulation waveforms, while remaining suitable for chronic, tetherless, biphasic deep brain stimulation. In this work, we elucidate several key parameters in a DBS system, identify gaps in existing solutions, and propose a new device to support preclinical DBS. The device allows for a high degree of flexibility in the output waveform with easily altered shape, frequency, pulse-width and amplitude. The device is suitable for both traditional and modern stimulation schemes, including those using non-rectangular waveforms, as well as delayed feedback schemes. The device incorporates active charge balancing to ensure safe operation, and allows for simple production of custom biphasic waveforms. This custom waveform output is unique in the field of preclinical DBS devices, and could be advantageous in performing future DBS studies investigating new treatment paradigms. This tetherless device can be easily and comfortably carried by an animal in a back-mountable configuration. The results of in-vitro tests are presented and discussed.

Small Changes in Inter-Pulse-Intervals Can Cause Synchronized Neuronal Firing During High-Frequency Stimulations in Rat Hippocampus

Deep brain stimulation (DBS) traditionally utilizes electrical pulse sequences with a constant frequency, i.e., constant inter-pulse-interval (IPI), to treat certain brain disorders in clinic. Stimulation sequences with varying frequency have been investigated recently to improve the efficacy of existing DBS therapy and to develop new treatments. However, the effects of such sequences are inconclusive. The present study tests the hypothesis that stimulations with varying IPI can generate neuronal activity markedly different from the activity induced by stimulations with constant IPI. And, the crucial factor causing the distinction is the relative differences in IPI lengths rather than the absolute lengths of IPI nor the average lengths of IPI. In rat experiments in vivo, responses of neuronal populations to applied stimulation sequences were collected during stimulations with both constant IPI (control) and random IPI. The stimulations were applied in the efferent fibers antidromically (in alveus) or in the afferent fibers orthodromically (in Schaffer collaterals) of pyramidal cells, the principal cells of hippocampal CA1 region. Amplitudes and areas of population spike (PS) waveforms were used to evaluate the neuronal responses induced by different stimulation paradigms. During the periods of both antidromic and orthodromic high-frequency stimulation (HFS), the HFS with random IPI induced synchronous neuronal firing with large PS even if the lengths of random IPI were limited to a small range of 5-10 ms, corresponding to a frequency range 100-200 Hz. The large PS events did not appear during control stimulations with a constant frequency at 100, 200, or 130 Hz (i.e., the mean frequency of HFS with random IPI uniformly distributed within 5-10 ms). Presumably, nonlinear dynamics in neuronal responses to random IPI might cause the generation of synchronous firing under the situation without any long pauses in HFS sequences. The results indicate that stimulations with random IPI can generate salient impulses to brain tissues and modulate the synchronization of neuronal activity, thereby providing potential stimulation paradigms for extending DBS therapy in treating more brain diseases, such as disorders of consciousness and vegetative states.

Temporal Pattern of Electrical Stimulation is a New Dimension of Therapeutic Innovation

Artificial activation of the nervous system requires selection of appropriate stimulation parameters including stimulation amplitude, stimulation pulse duration, and stimulation pulse repetition rate. The temporal pattern of stimulation, i.e., the timing between stimulation pulses, is a novel dimension of stimulation parameter tuning. The effects evoked by artificial activation of the nervous system are dependent on the pattern of stimulation, and different patterns of stimulation, even when delivered at the same average rate, evoke different functional effects, different changes in synaptic plasticity, and even different patterns of gene expression. Non-regular temporal patterns of stimulation offer the opportunity to improve the efficacy and efficiency of therapeutic stimulation as well as to manipulate other processes in the nervous system. The potential design space for sequences of varying interpulse intervals is exceedingly large and sound approaches to design stimulation patterns are required as an empirical approach is not practical.

Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network

The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia-thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.

Reengineering deep brain stimulation for movement disorders: Emerging technologies

Deep brain stimulation (DBS) is a neurosurgical technique, which consists of continuous delivery of an electrical pulse through chronically implanted electrodes connected to a neurostimulator, programmable in amplitude, pulse width, frequency, and stimulation channel. DBS is a promising treatment option for addressing severe and drug-resistant movement disorders. The success of DBS therapy is a combination of surgical implantation techniques, device technology, and clinical programming strategies. Changes in device settings require highly trained and experienced clinicians to achieve maximal therapeutic benefit for each targeted symptom, and optimization of stimulation parameters can take many visits. Thus, the development of innovative DBS technologies that can optimize the clinical implementation of DBS will lead to wider scale utilization. This review aims to present engineering approaches that have the potential to improve clinical outcomes of DBS, focusing on the development novel temporal patterns, innovative electrode designs, computational models to guide stimulation, closed-loop DBS, and remote programming.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

METHODS

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

RESULTS

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

CONCLUSIONS

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Non-human primate models of PD to test novel therapies

Non-human primate (NHP) models of Parkinson disease show many similarities with the human disease. They are very useful to test novel pharmacotherapies as reviewed here. The various NHP models of this disease are described with their characteristics including the macaque, the marmoset, and the squirrel monkey models. Lesion-induced and genetic models are described. There is no drug to slow, delay, stop, or cure Parkinson disease; available treatments are symptomatic. The dopamine precursor, L-3,4-dihydroxyphenylalanine (L-Dopa) still remains the gold standard symptomatic treatment of Parkinson. However, involuntary movements termed L-Dopa-induced dyskinesias appear in most patients after chronic treatment and may become disabling. Dyskinesias are very difficult to manage and there is only amantadine approved providing only a modest benefit. In this respect, NHP models have been useful to seek new drug targets, since they reproduce motor complications observed in parkinsonian patients. Therapies to treat motor symptoms in NHP models are reviewed with a discussion of their translational value to humans. Disease-modifying treatments tested in NHP are reviewed as well as surgical treatments. Many biochemical changes in the brain of post-mortem Parkinson disease patients with dyskinesias are reviewed and compare well with those observed in NHP models. Non-motor symptoms can be categorized into psychiatric, autonomic, and sensory symptoms. These symptoms are present in most parkinsonian patients and are already installed many years before the pre-motor phase of the disease. The translational usefulness of NHP models of Parkinson is discussed for non-motor symptoms.

Optimized temporal pattern of brain stimulation designed by computational evolution

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Randomized, Blinded Pilot Testing of Nonconventional Stimulation Patterns and Shapes in Parkinson's Disease and Essential Tremor: Evidence for Further Evaluating Narrow and Biphasic Pulses

OBJECTIVES

Evidence suggests that nonconventional programming may improve deep brain stimulation (DBS) therapy for movement disorders. The primary objective was to assess feasibility of testing the tolerability of several nonconventional settings in Parkinson's disease (PD) and essential tremor (ET) subjects in a single office visit. Secondary objectives were to explore for potential efficacy signals and to assess the energy demand on the implantable pulse-generators (IPGs).

MATERIALS AND METHODS

A custom firmware (FW) application was developed and acutely uploaded to the IPGs of eight PD and three ET subjects, allowing delivery of several nonconventional DBS settings, including narrow pulse widths, square biphasic pulses, and irregular pulse patterns. Standard clinical rating scales and several objective measures were used to compare motor outcomes with sham, clinically-optimal and nonconventional settings. Blinded and randomized testing was conducted in a traditional office setting.

RESULTS

Overall, the nonconventional settings were well tolerated. Under these conditions it was also possible to detect clinically-relevant differences in DBS responses using clinical rating scales but not objective measures. Compared to the clinically-optimal settings, some nonconventional settings appeared to offer similar benefit (e.g., narrow pulse widths) and others lesser benefit. Moreover, the results suggest that square biphasic pulses may deliver greater benefit. No unexpected IPG efficiency disadvantages were associated with delivering nonconventional settings.

CONCLUSIONS

It is feasible to acutely screen nonconventional DBS settings using controlled study designs in traditional office settings. Simple IPG FW upgrades may provide more DBS programming options for optimizing therapy. Potential advantages of narrow and biphasic pulses deserve follow up.

Mechanisms of deep brain stimulation

Deep brain stimulation (DBS) is widely used for the treatment of movement disorders including Parkinson's disease, essential tremor, and dystonia and, to a lesser extent, certain treatment-resistant neuropsychiatric disorders including obsessive-compulsive disorder. Rather than a single unifying mechanism, DBS likely acts via several, nonexclusive mechanisms including local and network-wide electrical and neurochemical effects of stimulation, modulation of oscillatory activity, synaptic plasticity, and, potentially, neuroprotection and neurogenesis. These different mechanisms vary in importance depending on the condition being treated and the target being stimulated. Here we review each of these in turn and illustrate how an understanding of these mechanisms is inspiring next-generation approaches to DBS.

High frequency stimulation extends the refractory period and generates axonal block in the rat hippocampus

BACKGROUND

The therapeutic mechanisms of deep brain stimulations (DBS) are not fully understood. Axonal block induced by high frequency stimulation (HFS) has been suggested as one possible underlying mechanism of DBS.

OBJECTIVE

To investigate the mechanism of the generation of HFS-induced axonal block.

METHODS

High frequency pulse trains were applied to the fiber tracts of alveus and Schaffer collaterals in the hippocampal CA1 neurons in anaesthetized rats at 50, 100 and 200 Hz. The amplitude changes of antidromic-evoked population spikes (APS) were measured to determine the degree of axonal block. The amplitude ratio of paired-pulse evoked APS was used to assess the changes of refractory period.

RESULTS

There were two distinct recovery stages of axonal block following the termination of HFS. One frequency-dependent faster phase followed by another frequency-independent slower phase. Experiments with specially designed temporal patterns of stimulation showed that HFS produced an extension of the duration of axonal refractory period thereby causing a fast recovery phase of the axonal block. Thus, prolonged gaps inserted within HFS trains could eliminate the axonal block and induced large population spikes and even epileptiform activity in the upstream or downstream regions.

CONCLUSIONS

Extension of refractory period plays an important role on HFS induced axonal block. Stimulation pattern with properly designed pauses could be beneficial for different requirements of excitation or inhibition in DBS therapies.

Technological advances in the surgical treatment of movement disorders

Technological innovations have driven the advancement of the surgical treatment of movement disorders, from the invention of the stereotactic frame to the adaptation of deep brain stimulation (DBS). Along these lines, this review will describe recent advances in inserting neuromodulation modalities, including DBS, to the target, and in the delivery of therapy at the target. Recent radiological advances are altering the way that DBS leads are targeted and inserted, by refining the ability to visualize the subcortical targets using high-field strength magnetic resonance imaging and other innovations, such as diffusion tensor imaging, and the development of novel targeting devices enabling purely anatomical implantations without the need for neurophysiological monitoring. New portable computed tomography scanners also are facilitating lead implantation without monitoring, as well as improving radiological verification of DBS lead location. Advances in neurophysiological mapping include efforts to develop automatic target verification algorithms, and probabilistic maps to guide target selection. The delivery of therapy at the target is being improved by the development of the next generation of internal pulse generators (IPGs). These include constant current devices that mitigate the variability introduced by impedance changes of the stimulated tissue and, in the near future, devices that deliver novel stimulation patterns with improved efficiency. Closed-loop adaptive IPGs are being tested, which may tailor stimulation to ongoing changes in the nervous system, reflected in biomarkers continuously recorded by the devices. Finer-grained DBS leads, in conjunction with new IPGs and advanced programming tools, may offer improved outcomes via current steering algorithms. Finally, even thermocoagulation-essentially replaced by DBS-is being advanced by new minimally-invasive approaches that may improve this therapy for selected patients in whom it may be preferred. Functional neurosurgery has a history of being driven by technological innovation, a tradition that continues into its future.

The temporal pattern of stimulation may be important to the mechanism of deep brain stimulation

Deep brain stimulation (DBS) has emerged as an important and potentially powerful treatment option for the management of carefully selected patients with advanced Parkinson's disease (PD) who are not adequately controlled by standard medication therapy. Though considerable advances have been made, the mechanisms underlying the therapeutic effects of DBS remain unclear despite its clinical efficacy. It is now widely held that both excitation and inhibition can occur secondary to stimulation, and it is suspected that abnormal synchronized oscillations may also be important in the mechanism of DBS. Other potentially important processes, including blood flow changes, local and upstream neurogenesis, and the modulation of neurotransmitters through stimulation of bordering astrocytes are also being investigated. Recent research has suggested that the temporal pattern of DBS stimulation is also an important variable in DBS neuromodulation, yet the extent of its influence on DBS efficacy has yet to be determined. As high stimulation frequency alone does not appear to be sufficient for optimal symptom suppression, attention to stimulation pattern might lead to more effective symptom control and reduced side effects, possibly at a lower frequency. Stimulation pattern may be potentially amenable to therapeutic modulation and its role in the clinical efficacy of DBS should be addressed through further focus and research.

Improved efficacy of temporally non-regular deep brain stimulation in Parkinson's disease

High frequency deep brain stimulation is an effective therapy for motor symptoms in Parkinson's disease. However, the relative clinical efficacy of regular versus non-regular temporal patterns of stimulation in Parkinson's disease remains unclear. To determine the temporal characteristics of non-regular temporal patterns of stimulation important for the treatment of Parkinson's disease, we compared the efficacy of temporally regular stimulation with four non-regular patterns of stimulation in subjects with Parkinson's disease using an alternating finger tapping task. The patterns of stimulation were also evaluated in a biophysical model of the parkinsonian basal ganglia that exhibited prominent oscillatory activity in the beta frequency range. The temporal patterns of stimulation differentially improved motor task performance. Three of the non-regular patterns of stimulation improved performance of the finger tapping task more than temporally regular stimulation. In the computational model all patterns of deep brain stimulation suppressed beta band oscillatory activity, and the degree of suppression was strongly correlated with the clinical efficacy across stimulation patterns. The three non-regular patterns of stimulation that improved motor performance over regular stimulation also suppressed beta band oscillatory activity in the computational model more effectively than regular stimulation. These data demonstrate that the temporal pattern of stimulation is an important consideration for the clinical efficacy of deep brain stimulation in Parkinson's disease. Furthermore, non-regular patterns of stimulation may ameliorate motor symptoms and suppress pathological rhythmic activity in the basal ganglia more effectively than regular stimulation. Therefore, non-regular patterns of deep brain stimulation may have useful clinical and experimental applications.

Closed-loop deep brain stimulation is superior in ameliorating parkinsonism

Continuous high-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson's disease (PD) management. However, the mechanisms underlying DBS effects remain enigmatic and are the subject of an ongoing debate. Here, we present and test a closed-loop stimulation strategy for PD in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. Application of pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Furthermore, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. Thus, closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.