Deep brain stimulation alleviates parkinsonian bradykinesia by regularizing pallidal activity

Closed-Loop Deep Brain Stimulation With Reinforcement Learning and Neural Simulation

Deep Brain Stimulation (DBS) is effective for movement disorders, particularly Parkinson's disease (PD). However, a closed-loop DBS system using reinforcement learning (RL) for automatic parameter tuning, offering enhanced energy efficiency and the effect of thalamus restoration, is yet to be developed for clinical and commercial applications. In this research, we instantiate a basal ganglia-thalamic (BGT) model and design it as an interactive environment suitable for RL models. Four finely tuned RL agents based on different frameworks, namely Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), are established for further comparison. Within the implemented RL architectures, the optimized TD3 demonstrates a significant 67% reduction in average power dissipation when compared to the open-loop system while preserving the normal response of the simulated BGT circuitry. As a result, our method mitigates thalamic error responses under pathological conditions and prevents overstimulation. In summary, this study introduces a novel approach to implementing an adaptive parameter-tuning closed-loop DBS system. Leveraging the advantages of TD3, our proposed approach holds significant promise for advancing the integration of RL applications into DBS systems, ultimately optimizing therapeutic effects in future clinical trials.

Circuit-Specific Deep Brain Stimulation Provides Insights into Movement Control

Deep brain stimulation (DBS), a method in which electrical stimulation is delivered to specific areas of the brain, is an effective treatment for managing symptoms of a number of neurological and neuropsychiatric disorders. Clinical access to neural circuits during DBS provides an opportunity to study the functional link between neural circuits and behavior. This review discusses how the use of DBS in Parkinson's disease and dystonia has provided insights into the brain networks and physiological mechanisms that underlie motor control. In parallel, insights from basic science about how patterns of electrical stimulation impact plasticity and communication within neural circuits are transforming DBS from a therapy for treating symptoms to a therapy for treating circuits, with the goal of training the brain out of its diseased state.

Advances in Neurorehabilitation: Strategies and Outcomes for Traumatic Brain Injury Recovery

Traumatic brain injury (TBI) consists of an external physical force that causes brain function impairment or pathology and globally affects 50 million people each year, with a cost of 400 billion US dollars. Clinical presentation of TBI can occur in many forms, and patients usually require prolonged hospital care and lifelong rehabilitation, which leads to an impact on the quality of life. For this narrative review, no particular method was used to extract data. With the aid of health descriptors and Medical Subject Heading (MeSH) terms, a search was thoroughly conducted in databases such as PubMed and Google Scholar. After the application of exclusion and inclusion criteria, a total of 146 articles were effectively used for this review. Results indicate that rehabilitation after TBI happens through neuroplasticity, which combines neural regeneration and functional reorganization. The role of technology, including artificial intelligence, virtual reality, robotics, computer interface, and neuromodulation, is to impact rehabilitation and life quality improvement significantly. Pharmacological intervention, however, did not result in any benefit when compared to standard care and still needs further research. It is possible to conclude that, given the high and diverse degree of disability associated with TBI, rehabilitation interventions should be precocious and tailored according to the individual's needs in order to achieve the best possible results. An interdisciplinary patient-centered care health team and well-oriented family members should be involved in every stage. Lastly, strategies must be adequate, well-planned, and communicated to patients and caregivers to attain higher functional outcomes.

The effect of transcranial ultrasound pulse repetition frequency on sustained inhibition in the human primary motor cortex: A double-blind, sham-controlled study

BACKGROUND

Non-invasive brain stimulation techniques such as transcranial magnetic stimulation and transcranial direct current stimulation hold promise for inducing brain plasticity. However, their limited precision may hamper certain applications. In contrast, Transcranial Ultrasound Stimulation (TUS), known for its precision and deep brain targeting capabilities, requires further investigation to establish its efficacy in producing enduring effects for treating neurological and psychiatric disorders.

OBJECTIVE

To investigate the enduring effects of different pulse repetition frequencies (PRF) of TUS on motor corticospinal excitability.

METHODS

T1-, T2-weighted, and zero echo time magnetic resonance imaging scans were acquired from 21 neurologically healthy participants for neuronavigation, skull reconstruction, and the performance of transcranial ultrasound and thermal modelling. The effects of three different TUS PRFs (10, 100, and 1000 Hz) with a constant duty cycle of 10 % on corticospinal excitability in the primary motor cortex were assessed using TMS-induced motor evoked potentials (MEPs). Each PRF and sham condition was evaluated on separate days, with measurements taken 5-, 30-, and 60-min post-TUS.

RESULTS

A significant decrease in MEP amplitude was observed with a PRF of 10 Hz (p = 0.007), which persisted for at least 30 min, and with a PRF of 100 Hz (p = 0.001), lasting over 60 min. However, no significant changes were found for the PRF of 1000 Hz and the sham conditions.

CONCLUSION

This study highlights the significance of PRF selection in TUS and underscores its potential as a non-invasive approach to reduce corticospinal excitability, offering valuable insights for future clinical applications.

A Review on Implantable Neuroelectrodes

The efficacy of every neuromodulation modality depends upon the characteristics of the electrodes used to stimulate the chosen target. The geometrical, chemical, mechanical and physical configuration of electrodes used in neurostimulation affects several performance attributes like stimulation efficiency, selectivity, tissue response, etc. The efficiency of stimulation in relation to electrode impedance is influenced by the electrode material and/or its geometry. The nature of the electrode material determines the charge transfer across the electrode-tissue interface, which also relates to neuronal tissue damage. Electrode morphology or configuration pattern can facilitate the modulation of extracellular electric field (field shaping). This enables selective activation of neurons and minimizes side effects. Biocompatibility and biostability of the electrode materials or electrode coating have a role in glial formation and tissue damage. Mechanical and electrochemical stability (corrosion resistance) determines the long-term efficacy of any neuromodulation technique. Here, a review of electrodes typically used for implantable neuromodulation is discussed. Factors affecting the performance of electrodes like stimulation efficiency, selectivity and tissue responses to the electrode-tissue interface are discussed. Technological advancements to improve electrode characteristics are also included.

The Therapeutic Frequency Profile of Subthalamic Nucleus Deep Brain Stimulation in Rats Is Shaped by Antidromic Spike Failure

The therapeutic mechanisms of subthalamic nucleus (STN) deep brain stimulation (DBS) may depend on antidromic activation of cortex via the hyperdirect pathway. However, hyperdirect pathway neurons cannot reliably follow high-stimulation frequencies, and the spike failure rate appears to correlate with symptom relief as a function of stimulation frequency. We hypothesized that antidromic spike failure contributes to the cortical desynchronization caused by DBS. We measured in vivo evoked cortical activity in female Sprague Dawley rats and developed a computational model of cortical activation from STN DBS. We modeled stochastic antidromic spike failure to determine how spike failure affected the desynchronization of pathophysiological oscillatory activity in cortex. We found that high-frequency STN DBS desynchronized pathologic oscillations via the masking of intrinsic spiking through a combination of spike collision, refractoriness, and synaptic depletion. Antidromic spike failure shaped the parabolic relationship between DBS frequency and cortical desynchronization, with maximum desynchronization at ∼130 Hz. These findings reveal that antidromic spike failure plays a critical role in mediating the dependency of symptom relief on stimulation frequency.SIGNIFICANCE STATEMENT Deep brain stimulation (DBS) is a highly effective neuromodulation therapy, yet it remains uncertain why conventionally used stimulation frequencies (e.g., ∼130 Hz) are optimal. In this study, we demonstrate a potential explanation for the stimulation frequency dependency of DBS through a combination of in vivo experimental measurements and computational modeling. We show that high-frequency stimulation can desynchronize pathologic firing patterns in populations of neurons by inducing an informational lesion. However, sporadic spike failure at these high frequencies limits the efficacy of the informational lesion, yielding a parabolic profile with optimal effects at ∼130 Hz. This work provides a potential explanation for the therapeutic mechanism of DBS, and highlights the importance of considering spike failure in mechanistic models of DBS.

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

Introduction

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.

Methods

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.

Results

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.

Discussion

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.

A non-contact system for intraoperative quantitative assessment of bradykinesia in deep brain stimulation surgery

BACKGROUND AND OBJECTIVE

Deep brain stimulation (DBS) is an effective treatment for a number of neurological diseases, especially for the advanced stage of Parkinson's disease (PD). Objective assessment of patients' motor symptoms is crucial for accurate electrode targeting and treatment. Existing approaches suffer from subjective variability or interference with voluntary motion. This work is aimed to establish an objective assessment system to quantify bradykinesia in DBS surgery.

METHODS

Based on the analysis of the requirements for intraoperative assessment, we developed a system with non-contact measurement, online movement feature extraction, and interactive data analysis and visualization. An optical sensor, Leap Motion Controller (LMC), was taken to detect hand movement in three clinical tasks. A graphic user interface was designed to process, compare and visualize the collected data and assessment results online. Quantified movement features include amplitude, frequency, velocity, their decrement and variability, etc. Technical validation of the system was performed with a motion capture system (Mocap), with respect to data-level and feature-level accuracy and reliability. Clinical validation was conducted with 20 PD patients for intraoperative assessments in DBS surgery. Treatment responses with respect to the bradykinesia movement features were analyzed. Single case analysis and group statistical analysis were performed to examine the differences between preoperative and intraoperative performance, and the correlation between the clinical ratings and the quantified assessment was analyzed.

RESULTS

For the movements measured by LMC and Mocap, the average Pearson's correlation coefficient was 0.986, and the mean amplitude difference was 2.11 mm. No significant difference was found for all movement features quantified by LMC and Mocap. For the clinical tests, key movement features showed significant differences between the preoperative baseline and intraoperative performance when the brain stimulation was ON. The assessment results were significantly correlated with the MDS-UPDRS clinical ratings.

CONCLUSIONS

The proposed non-contact system has established itself as an objective intraoperative assessment, analysis, and visualization tool for DBS treatment of Parkinson's disease.

Temporally optimized patterned stimulation (TOPS®) as a therapy to personalize deep brain stimulation treatment of Parkinson’s disease

Deep brain stimulation (DBS) is a well-established therapy for the motor symptoms of Parkinson’s disease (PD), but there remains an opportunity to improve symptom relief. The temporal pattern of stimulation is a new parameter to consider in DBS therapy, and we compared the effectiveness of Temporally Optimized Patterned Stimulation (TOPS) to standard DBS at reducing the motor symptoms of PD. Twenty-six subjects with DBS for PD received three different patterns of stimulation (two TOPS and standard) while on medication and using stimulation parameters optimized for standard DBS. Side effects and motor symptoms were assessed after 30 min of stimulation with each pattern. Subjects experienced similar types of side effects with TOPS and standard DBS, and TOPS were well-tolerated by a majority of the subjects. On average, the most effective TOPS was as effective as standard DBS at reducing the motor symptoms of PD. In some subjects a TOPS pattern was the most effective pattern. Finally, the TOPS pattern with low average frequency was found to be as effective or more effective in about half the subjects while substantially reducing estimated stimulation energy use. TOPS DBS may provide a new programing option to improve DBS therapy for PD by improving symptom reduction and/or increasing energy efficiency. Optimizing stimulation parameters specifically for TOPS DBS may demonstrate further clinical benefit of TOPS DBS in treating the motor symptoms of Parkinson’s disease.

Model-based quantitative optimization of deep brain stimulation and prediction of parkinson's states

Although the exact etiology of Parkinson's disease (PD) is still unknown, there are a variety of treatments available to alleviate its symptoms according to the development stage of PD. Deep brain stimulation (DBS), the most common surgical treatment for advanced PD, accurately locates and implants stimulating electrodes at specific targets in the brain to deliver high-frequency electrical stimulation that alters the excitability of the corresponding nuclei. However, for different patients and stages of PD development, there exists a choice of the optimal DBS protocol. In this paper, we propose a quantitative method (multi-dimensional feature indexes) to determine the stimulation pattern, stimulation parameters, and target of DBS from the perspective of the network model. On the other hand, based on this method, the development of PD can be predicted so that timely treatment can be given to patients. Simulation results show that, first, different network states can be distinguished by extracting features of the firing activity of neuronal populations within the basal ganglia network system. Secondly, the optimal DBS treatment can be selected by comparing the feature indexes vectors of the pre- and post-state of the network after the action of different modes of DBS. Lastly, the evolution of the network state from normal to pathological is simulated. The critical point of network state transitions is determined. These results provide a quantitative and qualitative method for determining the optimal regimen for DBS for PD, which is helpful for clinical practice.

Deep brain stimulation in the subthalamic nucleus for Parkinson's disease can restore dynamics of striatal networks

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in alleviating movement disability in patients with Parkinson’s disease (PD). However, its therapeutic mechanism of action is unknown. The healthy striatum exhibits rich dynamics resulting from an interaction of beta, gamma, and theta oscillations. These rhythms are essential to selection and execution of motor programs, and their loss or exaggeration due to dopamine (DA) depletion in PD is a major source of behavioral deficits. Restoring the natural rhythms may then be instrumental in the therapeutic action of DBS. We develop a biophysical networked model of a BG pathway to study how abnormal beta oscillations can emerge throughout the BG in PD and how DBS can restore normal beta, gamma, and theta striatal rhythms. Our model incorporates STN projections to the striatum, long known but understudied, found to preferentially target fast-spiking interneurons (FSI). We find that DBS in STN can normalize striatal medium spiny neuron activity by recruiting FSI dynamics and restoring the inhibitory potency of FSIs observed in normal conditions. We also find that DBS allows the reexpression of gamma and theta rhythms, thought to be dependent on high DA levels and thus lost in PD, through cortical noise control. Our study highlights that DBS effects can go beyond regularizing BG output dynamics to restoring normal internal BG dynamics and the ability to regulate them. It also suggests how gamma and theta oscillations can be leveraged to supplement DBS treatment and enhance its effectiveness.

The cortical evoked potential corresponds with deep brain stimulation efficacy in rats

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) antidromically activates the motor cortex (M1), and this cortical activation appears to play a role in the treatment of hypokinetic motor behaviors (Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K. Science 324: 354-359, 2009; Yu C, Cassar IR, Sambangi J, Grill WM. J Neurosci 40: 4323-4334, 2020). The synchronous antidromic activation takes the form of a short-latency cortical evoked potential (cEP) in electrocorticography (ECoG) recordings of M1. We assessed the utility of the cEP as a biomarker for STN DBS in unilateral 6-hydroxydopamine-lesioned female Sprague Dawley rats, with stimulating electrodes implanted in the STN and the ECoG recorded above M1. We quantified the correlations of the cEP magnitude and latency with changes in motor behavior from DBS and compared them to the correlation between motor behaviors and several commonly used spectral-based biomarkers. The cEP features correlated strongly with motor behaviors and were highly consistent across animals, whereas the spectral biomarkers correlated weakly with motor behaviors and were highly variable across animals. The cEP may thus be a useful biomarker for assessing the therapeutic efficacy of DBS parameters, as its features strongly correlate with motor behavior, it is consistent across time and subjects, it can be recorded under anesthesia, and it is simple to quantify with a large signal-to-noise ratio, enabling rapid, real-time evaluation. Additionally, our work provides further evidence that antidromic cortical activation mediates changes in motor behavior from STN DBS and that the dependence of DBS efficacy on stimulation frequency may be related to antidromic spike failure.NEW & NOTEWORTHY We characterize a new potential biomarker for deep brain stimulation (DBS), the cortical evoked potential (cEP), and demonstrate that it exhibits a robust correlation with motor behaviors as a function of stimulation frequency. The cEP may thus be a useful clinical biomarker for changes in motor behavior. This work also provides insight into the cortical mechanisms of DBS, suggesting that motor behaviors are strongly affected by the rate of antidromic spike failure during DBS.

Long-Lasting Desynchronization of Plastic Neuronal Networks by Double-Random Coordinated Reset Stimulation

Hypersynchrony of neuronal activity is associated with several neurological disorders, including essential tremor and Parkinson's disease (PD). Chronic high-frequency deep brain stimulation (HF DBS) is the standard of care for medically refractory PD. Symptoms may effectively be suppressed by HF DBS, but return shortly after cessation of stimulation. Coordinated reset (CR) stimulation is a theory-based stimulation technique that was designed to specifically counteract neuronal synchrony by desynchronization. During CR, phase-shifted stimuli are delivered to multiple neuronal subpopulations. Computational studies on CR stimulation of plastic neuronal networks revealed long-lasting desynchronization effects obtained by down-regulating abnormal synaptic connectivity. This way, networks are moved into attractors of stable desynchronized states such that stimulation-induced desynchronization persists after cessation of stimulation. Preclinical and clinical studies confirmed corresponding long-lasting therapeutic and desynchronizing effects in PD. As PD symptoms are associated with different pathological synchronous rhythms, stimulation-induced long-lasting desynchronization effects should favorably be robust to variations of the stimulation frequency. Recent computational studies suggested that this robustness can be improved by randomizing the timings of stimulus deliveries. We study the long-lasting effects of CR stimulation with randomized stimulus amplitudes and/or randomized stimulus timing in networks of leaky integrate-and-fire (LIF) neurons with spike-timing-dependent plasticity. Performing computer simulations and analytical calculations, we study long-lasting desynchronization effects of CR with and without randomization of stimulus amplitudes alone, randomization of stimulus times alone as well as the combination of both. Varying the CR stimulation frequency (with respect to the frequency of abnormal target rhythm) and the number of separately stimulated neuronal subpopulations, we reveal parameter regions and related mechanisms where the two qualitatively different randomization mechanisms improve the robustness of long-lasting desynchronization effects of CR. In particular, for clinically relevant parameter ranges double-random CR stimulation, i.e., CR stimulation with the specific combination of stimulus amplitude randomization and stimulus time randomization, may outperform regular CR stimulation with respect to long-lasting desynchronization. In addition, our results provide the first evidence that an effective reduction of the overall stimulation current by stimulus amplitude randomization may improve the frequency robustness of long-lasting therapeutic effects of brain stimulation.

Ultrasound does not activate but can inhibit in vivo mammalian nerves across a wide range of parameters

Ultrasound (US) has been shown to stimulate brain circuits, however, the ability to excite peripheral nerves with US remains controversial. To the best of our knowledge, there is still no in vivo neural recording study that has applied US stimulation to a nerve isolated from surrounding tissue to confirm direct activation effects. Here, we show that US cannot excite an isolated mammalian sciatic nerve in an in vivo preparation, even at high pressures (relative to levels recommended in the FDA guidance for diagnostic ultrasound) and for a wide range of parameters, including different pulse patterns and center frequencies. US can, however, reliably inhibit nerve activity whereby greater suppression is correlated with increases in nerve temperature. By prohibiting the nerve temperature from increasing during US application, we did not observe suppressive effects. Overall, these findings demonstrate that US can reliably inhibit nerve activity through a thermal mechanism that has potential for various health disorders, though future studies are needed to evaluate the long-term safety of therapeutic ultrasound applications.

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model

A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities are derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities (synchronisation index, mean synaptic activity and response efficacy) switch from normal to Parkinsonian conditions. Simulating DBS of the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and Parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.

Serendipitous Stimulation of Nucleus Basalis of Meynert-The Effect of Unintentional, Long-Term High-Frequency Stimulation on Cognition in Parkinson's Disease

There is a growing interest in deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) as a potential therapeutic modality for Parkinson’s disease dementia (PDD). Low-frequency stimulation has yielded encouraging results in individual patients; however, these are not yet sustained in larger studies. With the aim to expand the understanding of NBM-DBS, we share our experience with serendipitous NBM-DBS in patients treated with DBS of the internal Globus pallidus (GPi) for Parkinson’s disease. Since NBM is anatomically located ventral to GPi, several GPi-treated patients appeared to have the distal contact of DBS-electrode(s) positioned in the NBM. We hypothesized that unintentional high-frequency NBM-DBS over a period of one year would result in the opposite effect of low-frequency NBM-stimulation and cause cognitive decline. We studied a cohort of 33 patients with bilateral high-frequency DBS in the GPi for Parkinson’s disease, of which twelve were unintentionally co-stimulated in NBM. The subgroups of unintentional unilateral (N = 7) and bilateral NBM-DBS (N = 5) were compared to the control group of bilateral GPi-DBS (N = 11). Here, we show that unintentional high-frequency NBM-DBS did not cause a significantly faster decline in cognitive function. Further research is warranted for characterizing the therapeutic role of NBM-DBS.

Long-Lasting Desynchronization Effects of Coordinated Reset Stimulation Improved by Random Jitters

Abnormally strong synchronized activity is related to several neurological disorders, including essential tremor, epilepsy, and Parkinson's disease. Chronic high-frequency deep brain stimulation (HF DBS) is an established treatment for advanced Parkinson's disease. To reduce the delivered integral electrical current, novel theory-based stimulation techniques such as coordinated reset (CR) stimulation directly counteract the abnormal synchronous firing by delivering phase-shifted stimuli through multiple stimulation sites. In computational studies in neuronal networks with spike-timing-dependent plasticity (STDP), it was shown that CR stimulation down-regulates synaptic weights and drives the network into an attractor of a stable desynchronized state. This led to desynchronization effects that outlasted the stimulation. Corresponding long-lasting therapeutic effects were observed in preclinical and clinical studies. Computational studies suggest that long-lasting effects of CR stimulation depend on the adjustment of the stimulation frequency to the dominant synchronous rhythm. This may limit clinical applicability as different pathological rhythms may coexist. To increase the robustness of the long-lasting effects, we study randomized versions of CR stimulation in networks of leaky integrate-and-fire neurons with STDP. Randomization is obtained by adding random jitters to the stimulation times and by shuffling the sequence of stimulation site activations. We study the corresponding long-lasting effects using analytical calculations and computer simulations. We show that random jitters increase the robustness of long-lasting effects with respect to changes of the number of stimulation sites and the stimulation frequency. In contrast, shuffling does not increase parameter robustness of long-lasting effects. Studying the relation between acute, acute after-, and long-lasting effects of stimulation, we find that both acute after- and long-lasting effects are strongly determined by the stimulation-induced synaptic reshaping, whereas acute effects solely depend on the statistics of administered stimuli. We find that the stimulation duration is another important parameter, as effective stimulation only entails long-lasting effects after a sufficient stimulation duration. Our results show that long-lasting therapeutic effects of CR stimulation with random jitters are more robust than those of regular CR stimulation. This might reduce the parameter adjustment time in future clinical trials and make CR with random jitters more suitable for treating brain disorders with abnormal synchronization in multiple frequency bands.

Bilateral Intracranial Beta Activity During Forced and Spontaneous Movements in a 6-OHDA Hemi-PD Rat Model

Cortico-basal ganglia beta oscillations (13–30 Hz) are assumed to be involved in motor impairments in Parkinson’s Disease (PD), especially in bradykinesia and rigidity. Various studies have utilized the unilateral 6-hydroxydopamine (6-OHDA) rat PD model to further investigate PD and test novel treatments. However, a detailed behavioral and electrophysiological characterization of the model, including analyses of popular PD treatments such as DBS, has not been documented in the literature. We hence challenged the 6-OHDA rat hemi-PD model with a series of experiments (i.e., cylinder test, open field test, and rotarod test) aimed at assessing the motor impairments, analyzing the effects of Deep Brain Stimulation (DBS), and identifying under which conditions excessive beta oscillations occur. We found that 6-OHDA hemi-PD rats presented an impaired performance in all experiments compared to the sham group, and DBS could improve their overall performance. Across all the experiments and behaviors, the power in the high beta band was observed to be an important biomarker for PD as it showed differences between healthy and lesioned hemispheres and between 6-OHDA-lesioned and sham rats. This all shows that the 6-OHDA hemi-PD model accurately represents many of the motor and electrophysiological symptoms of PD and makes it a useful tool for the pre-clinical testing of new treatments when low β (13–21 Hz) and high β (21–30 Hz) frequency bands are considered separately.

Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation

Excessive neuronal synchrony is a hallmark of neurological disorders such as epilepsy and Parkinson's disease. An established treatment for medically refractory Parkinson's disease is high-frequency (HF) deep brain stimulation (DBS). However, symptoms return shortly after cessation of HF-DBS. Recently developed decoupling stimulation approaches, such as Random Reset (RR) stimulation, specifically target pathological connections to achieve long-lasting desynchronization. During RR stimulation, a temporally and spatially randomized stimulus pattern is administered. However, spatial randomization, as presented so far, may be difficult to realize in a DBS-like setup due to insufficient spatial resolution. Motivated by recently developed segmented DBS electrodes with multiple stimulation sites, we present a RR stimulation protocol that copes with the limited spatial resolution of currently available depth electrodes for DBS. Specifically, spatial randomization is realized by delivering stimuli simultaneously to L randomly selected stimulation sites out of a total of M stimulation sites, which will be called L/M-RR stimulation. We study decoupling by L/M-RR stimulation in networks of excitatory integrate-and-fire neurons with spike-timing dependent plasticity by means of theoretical and computational analysis. We find that L/M-RR stimulation yields parameter-robust decoupling and long-lasting desynchronization. Furthermore, our theory reveals that strong high-frequency stimulation is not suitable for inducing long-lasting desynchronization effects. As a consequence, low and high frequency L/M-RR stimulation affect synaptic weights in qualitatively different ways. Our simulations confirm these predictions and show that qualitative differences between low and high frequency L/M-RR stimulation are present across a wide range of stimulation parameters, rendering stimulation with intermediate frequencies most efficient. Remarkably, we find that L/M-RR stimulation does not rely on a high spatial resolution, characterized by the density of stimulation sites in a target area, corresponding to a large M. In fact, L/M-RR stimulation with low resolution performs even better at low stimulation amplitudes. Our results provide computational evidence that L/M-RR stimulation may present a way to exploit modern segmented lead electrodes for long-lasting therapeutic effects.

Closed-Loop neuromodulation for clustering neuronal populations

Pathological synchronization of neurons is associated with symptoms of movement disorders, such as Parkinson's disease and essential tremor. High-frequency deep brain stimulation (DBS) suppresses symptoms, presumably through the desynchronization of neurons. Coordinated reset (CR) delivers trains of high-frequency stimuli to different regions in the brain through multiple electrodes and may have more persistent therapeutic effects than conventional DBS. As an alternative to CR, we present a closed-loop control setup that desynchronizes neurons in brain slices by inducing clusters using a single electrode. Our setup uses calcium fluorescence imaging to extract carbachol-induced neuronal oscillations in real time. To determine the appropriate stimulation waveform for inducing clusters in a population of neurons, we calculate the phase of the neuronal populations and then estimate the phase response curve (PRC) of those populations to electrical stimulation. The phase and PRC are then fed into a control algorithm called the input of maximal instantaneous efficiency (IMIE). By using IMIE, the synchrony across the slice is decreased by dividing the population of neurons into subpopulations without suppressing the oscillations locally. The desynchronization effect is persistent 10 s after stimulation is stopped. The IMIE control algorithm may be used as a novel closed-loop DBS approach to suppress the symptoms of Parkinson's disease and essential tremor by inducing clusters with a single electrode.NEW & NOTEWORTHY Here, we present a closed-loop controller to desynchronize neurons in brain slices by inducing clusters using a single electrode using calcium imaging feedback. Phase of neurons are estimated in real time, and from the phase response curve stimulation is applied to achieve target phase differences. This method is an alternative to coordinated reset and is a novel therapy that could be used to disrupt synchronous neuronal oscillations thought to be the mechanism underlying Parkinson's disease.

Distinct perceptive pathways selected with tonic and bursting patterns of thalamic stimulation

BACKGROUND

Novel patterns of electrical stimulation of the brain and spinal cord hold tremendous promise to improve neuromodulation therapies for diverse disorders, including tremor and pain. To date, there are limited numbers of experimental studies in human subjects to help explain how stimulation patterns impact the clinical response, especially with deep brain stimulation. We propose using novel stimulation patterns during electrical stimulation of somatosensory thalamus in awake deep brain stimulation surgeries and hypothesize that stimulation patterns will influence the sensory percept without moving the electrode.

METHODS

In this study of 15 fully awake patients, the threshold of perception as well as perceptual characteristics were compared for tonic (trains of regularly-repeated pulses) and bursting stimulation patterns.

RESULTS

In a majority of subjects, tonic and burst percepts were located in separate, non-overlapping body regions (i.e., face vs. hand) without moving the stimulating electrode (p < 0.001; binomial test). The qualitative features of burst percepts also differed from those of tonic-evoked percepts as burst patterns were less likely to evoke percepts described as tingling (p = 0.013; Fisher's exact test).

CONCLUSIONS

Because somatosensory thalamus is somatotopically organized, percept location can be related to anatomic thalamocortical pathways. Thus, stimulation pattern may provide a mechanism to select for different thalamocortical pathways. This added control could lead to improvements in neuromodulation - such as improved efficacy and side effect attenuation - and may also improve localization for sensory prostheses.

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.

Frequency-Specific Optogenetic Deep Brain Stimulation of Subthalamic Nucleus Improves Parkinsonian Motor Behaviors

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy for the motor symptoms of Parkinson's disease (PD). However, the neural elements mediating symptom relief are unclear. A previous study concluded that direct optogenetic activation of STN neurons was neither necessary nor sufficient for relief of parkinsonian symptoms. However, the kinetics of the channelrhodopsin-2 (ChR2) used for cell-specific activation are too slow to follow the high rates required for effective DBS, and thus the contribution of activation of STN neurons to the therapeutic effects of DBS remains unclear. We quantified the behavioral and neuronal effects of optogenetic STN DBS in female rats following unilateral 6-hydroxydopamine (6-OHDA) lesion using an ultrafast opsin (Chronos). Optogenetic STN DBS at 130 pulses per second (pps) reduced pathologic circling and ameliorated deficits in forelimb stepping similarly to electrical DBS, while optogenetic STN DBS with ChR2 did not produce behavioral effects. As with electrical DBS, optogenetic STN DBS exhibited a strong dependence on stimulation rate; high rates produced symptom relief while low rates were ineffective. High-rate optogenetic DBS generated both increases and decreases in firing rates of single neurons in STN, globus pallidus externa (GPe), and substantia nigra pars reticular (SNr), and disrupted β band oscillatory activity in STN and SNr. High-rate optogenetic STN DBS can indeed ameliorate parkinsonian motor symptoms through reduction of abnormal oscillatory activity in the STN-associated neural circuit, and these results highlight that the kinetic properties of opsins have a strong influence on the effects of optogenetic stimulation.SIGNIFICANCE STATEMENT Whether STN local cells contribute to the therapeutic effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) remains unclear. We re-examined the role of STN local cells in mediating the symptom-relieving effects of STN DBS using cell type-specific optogenetic stimulation with a much faster opsin, Chronos. Direct optogenetic stimulation of STN neurons was effective in treating the symptoms of parkinsonism in the 6-hydroxydopamine (6-OHDA) lesion rat. These results highlight that the kinetic properties of opsins can have a strong influence on the effects of optogenetic activation/inhibition and must be considered when employing optogenetic to study high-rate neural stimulation.

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson's Disease

This study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson's disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshold controllers for suppressing beta-band activity by modulating the DBS amplitude were first verified, showing levels of beta suppression and reductions in power consumption comparable with previous clinical studies. Proportional (P) and proportional-integral (PI) closed-loop controllers for amplitude and frequency modulation were then investigated. A simple tuning rule was derived for selecting effective PI controller parameters to target long duration beta bursts while respecting clinical constraints that limit the rate of change of stimulation parameters. Of the controllers tested, PI controllers displayed superior performance for regulating network beta-band activity whilst accounting for clinical considerations. Proportional controllers resulted in undesirable rapid fluctuations of the DBS parameters which may exceed clinically tolerable rate limits. Overall, the PI controller for modulating DBS frequency performed best, reducing the mean error by 83% compared to DBS off and the mean power consumed to 25% of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers.

Changes in Neuronal Entropy in a Network Model of the Cortico-Basal Ganglia during Deep Brain Stimulation

Neuronal entropy changes are observed in the basal ganglia circuit in Parkinson's disease (PD). These changes are observed in both single unit recordings from globus pallidus (GP) neurons and in local field potential (LFP) recordings from the subthalamic nucleus (STN). These changes are hypothesized as representing changes in the information coding capacity of the network, with PD resulting in a reduction in the coding capacity of the basal ganglia network. Entropy changes in the LFP and in single unit recordings are investigated in a detailed physiological model of the cortico-basal ganglia network during STN deep brain stimulation (DBS). The model incorporates extracellular stimulation of STN afferent fibers, with both orthodromic and antidromic activation, and simulation of the LFP detected at a differential recording electrode. LFP sample entropy and beta-band oscillation power were found to be altered following the application of DBS. The ring pattern entropy of GP neurons in the network were observed to decrease during high frequency stimulation and increase during low frequency stimulation. Simulation results were consistent with experimentally reported changes in neuronal entropy during DBS.

Effects of ramped-frequency thalamic deep brain stimulation on tremor and activity of modeled neurons

OBJECTIVE

We conducted intraoperative measurements of tremor to quantify the effects of temporally patterned ramped-frequency DBS trains on tremor.

METHODS

Seven patterns of stimulation were tested in nine subjects with thalamic DBS for essential tremor: stimulation 'off', three ramped-frequency stimulation (RFS) trains from 130 → 50 Hz, 130 → 60 Hz, and 235 → 90 Hz, and three constant frequency stimulation (CFS) trains at 72, 82, and 130 Hz. The same patterns were applied to a computational model of the thalamic neural network.

RESULTS

Temporally patterned 130 → 60 Hz ramped-frequency trains suppressed tremor relative to stimulation 'off,' but 130 → 50 Hz, 130 → 60 Hz, and 235 → 90 Hz ramped-frequency trains were no more effective than constant frequency stimulation with the same mean interpulse interval (IPI). Computational modeling revealed that rhythmic burst-driver inputs to thalamus were masked during DBS, but long IPIs, concurrent with pauses in afferent cerebellar and cortical firing, allowed propagation of bursting activity. The mean firing rate of bursting-type model neurons as well as the firing pattern entropy of model neurons were both strongly correlated with tremor power across stimulation conditions.

CONCLUSION

Frequency-ramped DBS produced equivalent tremor suppression as constant frequency thalamic DBS. Tremor-related thalamic burst activity may result from burst-driver input, rather than by an intrinsic rebound mechanism.

SIGNIFICANCE

Ramping stimulation frequency may exacerbate thalamic burst firing by introducing consecutive pauses of increasing duration to the stimulation pattern.

Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease

Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present a closed-loop DBS method based on reinforcement learning (RL) to regulate stimulation parameters based on a computational model. The network model consists of interconnected biophysically-based spiking neurons, and the PD state is described as distorted relay reliability of thalamus (TH). Results show that the RL-based closed-loop control strategy can effectively restore the distorted relay reliability of the TH but with less DBS energy expenditure.

Beta Frequency Oscillations in the Subthalamic Nucleus Are Not Sufficient for the Development of Symptoms of Parkinsonian Bradykinesia/Akinesia in Rats

Substantial correlative evidence links the synchronized, oscillatory patterns of neural activity that emerge in Parkinson's disease (PD) in the beta (β) frequency range (13-30 Hz) with bradykinesia in PD. However, conflicting evidence exists, and whether these changes in neural activity are causal of motor symptoms in PD remains unclear. We tested the hypothesis that the synchronized β oscillations that emerge in PD are causal of symptoms of bradykinesia/akinesia. We designed patterns of stimulation that mimicked the temporal characteristics of single unit β bursting activity seen in PD animals and humans. We applied these β-patterned stimulation patterns along with continuous low-frequency and high-frequency controls to the subthalamic nucleus (STN) of intact and 6-OHDA-lesioned female Long-Evans and Sprague-Dawley rats. β-Patterned paradigms were superior to low-frequency controls at induction of β power in downstream substantia nigra reticulata (SNr) neurons and in ipsilateral motor cortex. However, we did not detect deleterious effects on motor performance across a wide battery of validated behavioral tasks. Our results suggest that β frequency oscillations (BFOs) may not be sufficient for the generation of bradykinesia/akinesia in PD.

The onset mechanism of Parkinson's beta oscillations: A theoretical analysis

In this paper, we build a basal ganglia-cortex-thalamus model to study the oscillatory mechanisms and boundary conditions of the beta frequency band (13-30 Hz) that appears in the subthalamic nucleus. First, a theoretical oscillatory boundary formula is obtained in a simplified model by using the Laplace transform and linearization process of the system at fixed points. Second, we simulate the oscillatory boundary conditions through numerical calculations, which fit with our theoretical results very well, at least in the changing trend. We find that several critical coupling strengths in the model exert great effects on the oscillations, the mechanisms of which differ but can be explained in detail by our model and the oscillatory boundary formula. Specifically, we note that the relatively small or large sizes of the coupling strength from the fast-spiking interneurons to the medium spiny neurons and from the cortex to the fast-spiking interneurons both have obvious maintenance roles on the states. Similar phenomena have been reported in other neurological diseases, such as absence epilepsy. However, some of those interesting mutual regulation mechanisms in the model have rarely been considered in previous studies. In addition to the coupling weight in the pathway, in this work, we show that the delay is a key parameter that affects oscillations. On the one hand, the system needs a minimum delay to generate oscillations; on the other hand, in the appropriate range, a longer delay leads to a higher activation level of the subthalamic nucleus. In this paper, we study the oscillation activities that appear on the subthalamic nucleus. Moreover, all populations in the model show the dynamic behaviour of a synchronous resonance. Therefore, we infer that the mechanisms obtained can be expanded to explore the state of other populations, and that the model provides a unified framework for studying similar problems in the future. Moreover, the oscillatory boundary curves obtained are all critical conditions between the stable state and beta frequency oscillation. The method is also suitable for depicting other common frequency bands during brain oscillations, such as the alpha band (8-12 Hz), theta band (4-7 Hz) and delta band (1-3 Hz). Thus, the results of this work are expected to help us better understand the onset mechanism of parkinson's oscillations and can inspire related experimental research in this field.

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.

Robust desynchronization of Parkinson's disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

The hyperkinetic symptoms of Parkinson's Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.

Synchronous Responses of Population Neurons to the Changes of Inter-Pulse-Intervals during Stimulations of Afferent Fibers

Deep brain stimulation (DBS) has been used to treat many brain disorders. Studies have shown that in DBS therapies, high frequency stimulation (HFS) with a constant pulse frequency over ~90 Hz can obtain better efficacy than stimulations with irregular inter-pulse-interval (IPI). The reasons are not clear yet. We hypothesized that irregular IPI might cause synchronous firing in target neurons thereby weakening the DBS efficacy. To test this hypothesis, stimulation trains of orthodromic-HFS (O-HFS) with different IPI were applied on the Schaffer collaterals, i.e., the afferent fiber tracts of the hippocampal CA1 region in anaesthetized rats. The amplitude of evoked population spikes (PS) in the downstream region was used as an electrophysiological index to evaluate the synchronicity of neuronal firing. The results showed that 100 Hz O-HFS with constant IPI induced de-synchronized firing of downstream neurons without PS events, whereas O-HFS with sparse prolonged IPI (20 or 100 ms) or with irregular IPI (1.7 - 50 ms) generated large PS events. Presumably, the longer IPI in O-HFS trains might provide adequate time to allow axons to recover from HFS-induced block and to respond the next coming pulse, synchronously. Therefore, following longer IPI, the population neurons in the target region could receive synchronous impulses from a lot of axonal fibers thereby generating action potentials synchronously. These findings are important for revealing new underlying mechanisms of DBS and for advancing the application of DBS.

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.

Effects of the temporal pattern of subthalamic deep brain stimulation on the neuronal complexity in the globus pallidus

Deep brain stimulation (DBS) is a surgical treatment for Parkinson's disease (PD) but, despite clinical efficacy, the mechanisms of DBS still require investigation. Recent evidence suggests that the temporal pattern of the electrical pulses may be critical to the therapeutic merit of DBS and carefully-designed, non-regular patterns could ameliorate some of the motor symptoms in PD. It is unclear, though, how different stimulation patterns affect the neural activity in the basal ganglia and whether this is related to the pathophysiology of PD. In this study, a non-human primate was treated with DBS of the subthalamic nucleus while single-unit recordings were collected in the animal's globus pallidus internus (GPi). Three stimulation patterns were applied (one regular, two non-regular) and the stimulation effects on the GPi spike trains were assessed via point process modeling. On a preliminary set of 23 GPi neurons, we show that regular DBS maximized the neuronal complexity, which is a measure of the amount of information that a single neuron can encode, and significantly increased the dependency of the neurons' spike trains on the background ensemble activity through an articulated balance of excitation and inhibition. Overall, regular DBS caused the largest modulation in the neurons' spiking pattern and the largest increment in encoding capabilities. Both results may be relevant to the mechanisms of therapeutic DBS.

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

Deep brain stimulation (DBS) is an effective therapy for movement disorders, including Parkinson's disease (PD), although the mechanisms of action remain unclear. Abnormal oscillatory neural activity is correlated with motor symptoms, and pharmacological or DBS treatment that alleviates motor symptoms appears to suppress abnormal oscillations. However, whether such oscillatory activity is causal of motor deficits such as tremor remains unclear. Our goal was to generate abnormal oscillatory activity in the cortex-basal ganglia loop using patterned subthalamic nucleus DBS and to quantify motor behavior in awake healthy rats. Stimulation patterns were designed via model-based optimization to increase power in the low-frequency (7-11 Hz) band because these oscillations are associated with the emergence of motor symptoms in the 6-hydroxydopamine lesioned rat model of parkinsonism. We measured motor activity using a head-mounted accelerometer, as well as quantified neural activity in cortex and globus pallidus (GP), in response to 5 stimulation patterns that generated a range of 7- to 11-Hz spectral power. Stimulation patterns induced oscillatory activity in the low-frequency band in the cortex and GP and caused tremor, whereas control patterns and regular 50-Hz DBS did not generate any such effects. Neural and motor-evoked responses observed during stimulation were synchronous and time-locked to stimulation bursts within the patterns. These results identified elements of irregular patterns of stimulation that were correlated with tremor and tremor-related neural activity in the cortex and basal ganglia and may lead to the identification of the oscillatory activity and structures associated with the generation of tremor activity. NEW & NOTEWORTHY Subthalamic nucleus deep brain stimulation is a promising therapy for movement disorders such as Parkinson's disease. Several groups reported correlation between suppression of abnormal oscillatory activity in the cortex-basal ganglia and motor symptoms, but it remains unclear whether such oscillations play a causal role in the emergence of motor symptoms. We demonstrate generation of tremor and pathological oscillatory activity in otherwise healthy rats by stimulation with patterns that produced increases in low-frequency oscillatory activity.

Closed-loop low-frequency DBS restores thalamocortical relay fidelity in a computational model of the motor loop

Closed-loop modulation of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is investigated to automatically adjust the stimulation to the patients' conditions, optimize the clinical outcomes, and reduce the energy requirements. This study proposes a closed-loop control system for real-time adaptation of the STN-DBS amplitude based on the neural activity in the motor thalamus. Population-averaged post-stimulus time histograms are used to measure the average effects of STN-DBS on the thalamocortical neurons and a L₂-norm minimization problem is solved to design the control algorithm, while the frequency of stimulation is kept constant. Applied on a large-scale, biophysically-based, anatomically-compliant model of the cortico-basal ganglia-thalamo-cortical motor loop under PD conditions, our adaptive DBS significantly (P-value P<;0.05) improved the relay fidelity of the thalamocortical neurons and restored the average power of the thalamocortical spike trains in the band [3, 100] Hz, two indicators of restored thalamocortical activity. Furthermore, adaptive-DBS significantly decreased the energy requirements when compared with non-adaptive-DBS at the same frequency. Finally, 30- and 60-Hz-adaptive-DBS determined the maximal restoration of thalamocortical activity and outperformed high-frequency, non-adaptive-DBS. Overall, results suggest that a feedback-controlled, low-frequency DBS pattern may result in significant restoration of the thalamocortical encoding while lowering the energy requirements.

Pallidal deep brain stimulation modulates excessive cortical high β phase amplitude coupling in Parkinson disease

OBJECTIVE

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) are equally efficacious in the management of Parkinson disease (PD). Studies of STN-DBS have revealed a therapeutic reduction in excessive cortical β-γ phase-amplitude coupling (PAC). It is unclear whether this is specific to STN-DBS and potentially mediated by modulation of the hyperdirect pathway or if it is a generalizable mechanism seen with DBS of other targets. Moreover, it remains unclear how cortical signals are differentially modulated by movement versus therapy. To clarify, the effects of GPi-DBS and movement on cortical β power and β-γ PAC were examined.

METHODS

Right sensorimotor electrocorticographic signals were recorded in 10 PD patients undergoing GPi-DBS implantation surgery. We evaluated cortical β power and β-γ PAC during blocks of rest and contralateral hand movement (finger tapping) with GPi-DBS off and on.

RESULTS

Movement suppressed cortical low β power (P = 0.008) and high β-γ PAC (P = 0.028). Linear mixed effect modeling (LMEM) showed that power in low and high β bands are differentially modulated by movement (P = 0.022). GPi-DBS also results in a significant suppression of high β-γ PAC but without power modulation in either β sub-band (P = 0.008). Cortical high β-γ PAC is significantly correlated with severity of bradykinesia (Rho = 0.59, P = 0.045) and changes proportionally with therapeutic improvement (Rho = 0.61, P = 0.04).

CONCLUSIONS

Similar to STN-DBS, GPi-DBS reduces motor cortical β-γ PAC, like that also reported with dopaminergic mediations, suggesting it is a generalizable symptom biomarker in PD, independent of therapeutic target or proximity to the hyperdirect pathway.

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

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

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.

Theta-phase closed-loop stimulation induces motor paradoxical responses in the rat model of Parkinson disease

BACKGROUND

High-frequency deep brain stimulation (DBS) has become a widespread therapy used in the treatment of Parkinson's Disease (PD) and other diseases. Although it has proved beneficial, much recent attention has been centered around the potential of new closed-loop DBS implementations.

OBJECTIVE

Here we present a new closed-loop DBS scheme based on the phase of the theta activity recorded from the motor cortex. By testing the implementation on freely moving 6-OHDA lesioned and control rats, we assessed the behavioral and neurophysiologic effects of this implementation and compared it against the classical high-frequency DBS.

RESULTS

Results show that both stimulation modalities produce significant and opposite changes on the movement and neurophysiological activity. Close-loop stimulation, far from improving the animals' behavior, exert contrary effects to those of high-frequency DBS which reverts the parkinsonian symptoms. Motor improvement during open-loop, high-frequency DBS was accompanied by a reduction in the amount of cortical beta oscillations while akinetic and disturbed behavior during close-loop stimulation coincided with an increase in the amplitude of beta activity.

CONCLUSION

Cortical-phase-dependent close-loop stimulation of the STN exerts significant behavioral and oscillatory changes in the rat model of PD. Open-loop and close-loop stimulation outcomes differed dramatically, thus suggesting that the scheme of stimulation determines the output of the modulation even if the target structure is maintained. The current framework could be extended in future studies to identify the correct parameters that would provide a suitable control signal to the system. It may well be that with other stimulation parameters, this sort of DBS could be beneficial.

Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation

The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms.

Subthalamic nucleus stimulation impairs emotional conflict adaptation in Parkinson's disease

The subthalamic nucleus (STN) occupies a strategic position in the motor network, slowing down responses in situations with conflicting perceptual input. Recent evidence suggests a role of the STN in emotion processing through strong connections with emotion recognition structures. As deep brain stimulation (DBS) of the STN in patients with Parkinson's disease (PD) inhibits monitoring of perceptual and value-based conflict, STN DBS may also interfere with emotional conflict processing. To assess a possible interference of STN DBS with emotional conflict processing, we used an emotional Stroop paradigm. Subjects categorized face stimuli according to their emotional expression while ignoring emotionally congruent or incongruent superimposed word labels. Eleven PD patients ON and OFF STN DBS and eleven age-matched healthy subjects conducted the task. We found conflict-induced response slowing in healthy controls and PD patients OFF DBS, but not ON DBS, suggesting STN DBS to decrease adaptation to within-trial conflict. OFF DBS, patients showed more conflict-induced slowing for negative conflict stimuli, which was diminished by STN DBS. Computational modelling of STN influence on conflict adaptation disclosed DBS to interfere via increased baseline activity.

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.

Subthalamic neural entropy is a feature of freezing of gait in freely moving people with Parkinson's disease

The goal of this study was to investigate subthalamic (STN) neural features of Freezers and Non-Freezers with Parkinson's disease (PD), while freely walking without freezing of gait (FOG) and during periods of FOG, which were better elicited during a novel turning and barrier gait task than during forward walking.

METHODS

Synchronous STN local field potentials (LFPs), shank angular velocities, and ground reaction forces were measured in fourteen PD subjects (eight Freezers) off medication, OFF deep brain stimulation (DBS), using an investigative, implanted, sensing neurostimulator (Activa® PC+S, Medtronic, Inc.). Tasks included standing still, instrumented forward walking, stepping in place on dual forceplates, and instrumented walking through a turning and barrier course.

RESULTS

During locomotion without FOG, Freezers showed lower beta (13-30Hz) power (P=0.036) and greater beta Sample Entropy (P=0.032), than Non-Freezers, as well as greater gait asymmetry and arrhythmicity (P<0.05 for both). No differences in alpha/beta power and/or entropy were evident at rest. During periods of FOG, Freezers showed greater alpha (8-12Hz) Sample Entropy (P<0.001) than during walking without FOG.

CONCLUSIONS

A novel turning and barrier course was superior to FW in eliciting FOG. Greater unpredictability in subthalamic beta rhythms was evident during stepping without freezing episodes in Freezers compared to Non-Freezers, whereas greater unpredictability in alpha rhythms was evident in Freezers during FOG. Non-linear analysis of dynamic neural signals during gait in freely moving people with PD may yield greater insight into the pathophysiology of FOG; whether the increases in STN entropy are causative or compensatory remains to be determined. Some beta LFP power may be useful for rhythmic, symmetric gait and DBS parameters, which completely attenuate STN beta power may worsen rather than improve FOG.

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

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

Mechanisms for pattern specificity of deep-brain stimulation in Parkinson's disease

Deep brain stimulation (DBS) has become a widely used technique for treating advanced stages of neurological and psychiatric illness. In the case of motor disorders related to basal ganglia (BG) dysfunction, several mechanisms of action for the DBS therapy have been identified which might be involved simultaneously or in sequence. However, the identification of a common key mechanism underlying the clinical relevant DBS configurations has remained elusive due to the inherent complexity related to the interaction between the electrical stimulation and the neural tissue, and the intricate circuital structure of the BG-thalamocortical network. In this work, it is shown that the clinically relevant range for both, the frequency and intensity of the electrical stimulation pattern, is an emergent property of the BG anatomy at the system-level that can be addressed using mean-field descriptive models of the BG network. Moreover, it is shown that the activity resetting mechanism elicited by electrical stimulation provides a natural explanation to the ineffectiveness of irregular (i.e., aperiodic) stimulation patterns, which has been commonly observed in previously reported pathophysiology models of Parkinson’s disease. Using analytical and numerical techniques, these results have been reproduced in both cases: 1) a reduced mean-field model that can be thought as an elementary building block capable to capture the underlying fundamentals of the relevant loops constituting the BG-thalamocortical network, and 2) a detailed model constituted by the direct and hyperdirect loops including one-dimensional spatial structure of the BG nuclei. We found that the optimal ranges for the essential parameters of the stimulation patterns can be understood without taking into account biophysical details of the relevant structures.