Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation

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.

Impact of deep brain stimulation therapy on the vertebral sagittal balance in Parkinson's disease patients

Parkinson's disease (PD) is characterized by cardinal motor signs: 4-6 Hz resting tremor, rigidity, and bradykinesia. In addition, 3-18% of PD patients have camptocormia, an abnormal forward flexion of the thoracolumbar spine, which may have a negative impact on patients' quality of life. Different possible treatments have been suggested for such a condition, but no one is resolutive. This study aims to define the possible impact of DBS, with selective targeting on the dorsal-lateral region of the STN, on the sagittal balance of patients affected by PD. Among all patients that have undergone DBS procedures in our institution, we selected eight subjects, four females and four males, with selective targeting on the dorsal-lateral region of the subthalamic nucleus (STN) because of camptocormia and other severe postural changes. Radiological assessments of spinal balance parameters before surgery and at 6 and 12 months postoperatively were carried out. Comparison of preoperative and postoperative spine X-ray data showed a statistically significant improvement in dorsal kyphosis angle (D-Cobb) 12 months after the operation. Deep brain stimulation with selective targeting of the dorsal lateral part of the STN may induce changes of the posture in patients with Parkinson's disease 12 months after the operation, which appears to improve in this small sample size, but larger observational and controlled trials would be required to confirm this observation.

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

Psychiatric Symptoms in Parkinson's Disease Patients before and One Year after Subthalamic Nucleus Deep Brain Stimulation Therapy: Role of Lead Positioning and Not of Total Electrical Energy Delivered

Parkinson’s disease (PD) patients may experience neuropsychiatric symptoms, including depression, anxiety, sleep disturbances, psychosis, as well as behavioral and cognitive symptoms during all the different stages of the illness. Deep Brain Stimulation (DBS) therapy has proven to be successful in controlling the motor symptoms of PD and its possible correlation with the occurrence or worsening of neuropsychiatric symptoms has been reported. We aimed to assess the neuropsychiatric symptoms of 14 PD patients before and after one year of Subthalamic Nucleus (STN)-DBS and to correlate the possible changes to the lead placement and to the total electrical energy delivered. We assessed PD motor symptoms, depression, anxiety, apathy, impulsivity, and suicidality using clinician- and/or self-administered rating scales and correlated the results to the lead position using the Medtronic SuretuneTM software and to the total electrical energy delivered (TEED). At the 12-month follow-up, the patients showed a significant improvement in PD symptoms on the UPDRS (Unified Parkinson’s disease Rating Scale) (−38.5%; p < 0.001) and in anxiety on the Hamilton Anxiety Rating Scale (HAM-A) (−29%; p = 0.041), with the most significant reduction in the physiological anxiety subscore (−36.26%; p < 0.001). A mild worsening of impulsivity was detected on the Barratt Impulsiveness Scale (BIS-11) (+9%; p = 0.048), with the greatest increase in the attentional impulsiveness subscore (+13.60%; p = 0.050). No statistically significant differences were found for the other scales. No correlation was found between TEED and scales’ scores, while the positioning of the stimulating electrodes in the different portions of the STN was shown to considerably influence the outcome, with more anterior and/or medial lead position negatively influencing psychiatric symptoms.

Utility of Postoperative Imaging Software for Deep Brain Stimulation Targeting in Patients with Movement Disorders

OBJECTIVE

The objective of this study was to evaluate the accuracy of the SureTune3 postoperative imaging software in determining the location of a deep brain stimulation (DBS) electrode based on clinical outcomes and the adverse effects (AEs) observed.

METHODS

Twenty-six consecutive patients with Parkinson disease (n = 17), essential tremor (n = 8), and dystonia (n = 1) who underwent bilateral DBS surgery (52 electrodes) were included in this study. Presurgical assessments were performed in all patients prior to surgery and at 3 and 6 months after surgery, using quality-of-life and clinical scales in each case. The SureTune3 software was used to evaluate the anatomical positioning of the DBS electrodes.

RESULTS

Following DBS surgery, motor and quality-of-life improvement was observed in all patients. Different AEs were detected in 12 patients, in 10 of whom (83.3%) SureTune3 related the symptoms to the positioning of an electrode. A clinical association was observed with SureTune3 for 48 of 52 (92.3%) electrodes, whereas no association was found between the AEs or clinical outcomes and the SureTune3 reconstructions for 4 of 52 electrodes (7.7%) from 4 different patients. In 2 patients, the contact chosen was modified based on the SureTune3 data, and in 2 cases, the software helped determine that second electrode replacement surgery was necessary.

CONCLUSIONS

The anatomical position of electrodes analyzed with SureTune3 software was strongly correlated with both the AEs and clinical outcomes. Thus, SureTune3 may be useful in clinical practice, and it could help improve stimulation parameters and influence decisions to undertake electrode replacement surgery.

Troubleshooting Gait Disturbances in Parkinson's Disease With Deep Brain Stimulation

Deep brain stimulation (DBS) of the subthalamic nucleus or the globus pallidus is an established treatment for Parkinson's disease (PD) that yields a marked and lasting improvement of motor symptoms. Yet, DBS benefit on gait disturbances in PD is still debated and can be a source of dissatisfaction and poor quality of life. Gait disturbances in PD encompass a variety of clinical manifestations and rely on different pathophysiological bases. While gait disturbances arising years after DBS surgery can be related to disease progression, early impairment of gait may be secondary to treatable causes and benefits from DBS reprogramming. In this review, we tackle the issue of gait disturbances in PD patients with DBS by discussing their neurophysiological basis, providing a detailed clinical characterization, and proposing a pragmatic programming approach to support their management.

Modulation of sensory cortical activity by deep brain stimulation in advanced Parkinson's Disease

Despite optimal oral drug treatment, about 90% of patients with Parkinson's disease develop motor fluctuation and dyskinesia within 5-10 years from the diagnosis. Moreover, the patients show non-motor symptoms in different sensory domains. Bilateral deep brain stimulation applied to the subthalamic nucleus is considered the most effective treatment in advanced Parkinson's disease and it has been suggested to affect sensorimotor modulation and relate to motor improvement in patients. However, observations on the relationship between sensorimotor activity and clinical improvement have remained sparse. Here we studied the somatosensory evoked magnetic fields in thirteen right-handed patients with advanced Parkinson's disease before and 7 months after stimulator implantation. Somatosensory processing was addressed with magnetoencephalography during alternated median nerve stimulation at both wrists. The strengths and the latencies of the ~60-ms responses at the contralateral primary somatosensory cortices were highly variable but detectable and reliably localized in all patients. The response strengths did not differ between preoperative and postoperative DBS_ON measurements. The change in the response strength between pre- and postoperative condition in the dominant left hemisphere of our right-handed patients correlated with the alleviation of their motor symptoms (p = 0.04). However, the result did not survive correction for multiple comparisons. Magnetoencephalography appears an effective tool to explore non-motor effects in patients with Parkinson's disease, and it may help in understanding the neurophysiological basis of deep brain stimulation. However, the high interindividual variability in the somatosensory responses and poor tolerability of DBS_OFF condition warrants larger patient groups and measurements also in non-medicated patients.

Developments in Deep Brain Stimulators for Successful Aging Towards Smart Devices—An Overview

This work provides an overview of the present state-of-the-art in the development of deep brain Deep Brain Stimulation (DBS) and how such devices alleviate motor and cognitive disorders for a successful aging. This work reviews chronic diseases that are addressable via DBS, reporting also the treatment efficacies. The underlying mechanism for DBS is also reported. A discussion on hardware developments focusing on DBS control paradigms is included specifically the open- and closed-loop “smart” control implementations. Furthermore, developments towards a “smart” DBS, while considering the design challenges, current state of the art, and constraints, are also presented. This work also showcased different methods, using ambient energy scavenging, that offer alternative solutions to prolong the battery life of the DBS device. These are geared towards a low maintenance, semi-autonomous, and less disruptive device to be used by the elderly patient suffering from motor and cognitive disorders.

A fully implantable wireless bidirectional neuromodulation system for mice

Novel research in the field of bioelectronic medicine requires neuromodulation systems that pair high-performance neurostimulation and bio-signal acquisition hardware with advanced signal processing and control algorithms. Although mice are the most commonly used animal in medical research, the size, weight, and power requirements of such bioelectronic systems either preclude use in mice or impose significant constraints on experimental design. Here, a fully-implantable recording and stimulation neuromodulation system suitable for use in mice is presented, measuring 2.2 cm3 and weighing 2.8 g. The bidirectional wireless interface allows simultaneous readout of multiple physiological signals and complete control over stimulation parameters, and a wirelessly rechargeable battery provides a lifetime of up to 5 days on a single charge. The device was implanted to deliver vagus nerve stimulation (n = 12 animals) and a functional neural interface (capable of inducing acute bradycardia) was demonstrated with lifetimes exceeding three weeks. The design utilizes only commercially-available electrical components and 3D-printed packaging, with the goal of facilitating widespread adoption and accelerating discovery and translation of future bioelectronic therapeutics.

Technological Improvement Rates and Evolution of Energy-Based Therapeutics

This paper examines the field of energy-based medical therapies based on the analysis of patents. We define the field as the use of external stimuli to achieve biomedical modifications to treat disease and to increase health. Based upon distinct sets of patents, the field is subdivided into sub-domains for each energy category used to achieve the stimulation: electrical, magnetic, microwave, ultrasound, and optical. Previously developed techniques are used to retrieve the relevant patents for each of the stimulation modes and to determine main paths along the trajectory followed by each sub-domain. The patent sets are analyzed to determine key assignees, number of patents, and dates of emergence of the sub-domains. The sub-domains are found to be largely independent as to patent assignees. Electrical and magnetic stimulation patents emerged earliest in the 1970s and microwave most recently around 1990. The annual rate of improvement of all sub-domains (12-85%) is found to be significantly higher than one we find for an aggregate pharmaceutical domain (5%). Overall, the results suggest an increasingly important role for energy-based therapies in the future of medicine.

Predict initial subthalamic nucleus stimulation outcome in Parkinson's disease with brain morphology

AIM

Subthalamic nucleus deep brain stimulation (STN-DBS) has been reported to be effective in treating motor symptoms in Parkinson's disease (PD), which may be attributed to changes in the brain network. However, the association between brain morphology and initial STN-DBS efficacy, as well as the performance of prediction using neuroimaging, has not been well illustrated. Therefore, we aim to investigate these issues.

METHODS

In the present study, 94 PD patients underwent bilateral STN-DBS, and the initial stimulation efficacy was evaluated. Brain morphology was examined by magnetic resonance imaging (MRI). The volume of tissue activated in the motor STN was measured with MRI and computed tomography. The prediction of stimulation efficacy was achieved with a support vector machine, using brain morphology and other features, after feature selection and hyperparameter optimization.

RESULTS

A higher stimulation efficacy was correlated with a thicker right precentral cortex. No association with subcortical gray or white matter volumes was observed. These morphological features could estimate the individual stimulation response with an r value of 0.5678, an R² of 0.3224, and an average error of 11.4%. The permutation test suggested these predictions were not based on chance.

CONCLUSION

Our results indicate that changes in morphology are associated with the initial stimulation motor response and could be used to predict individual initial stimulation-related motor responses.

Identification of effective features of LFP signal for making closed-loop deep brain stimulation in parkinsonian rats

Traditional deep brain stimulation (DBS) is one of the acceptable methods to relieve the clinical symptoms of Parkinson's disease in its advanced stages. Today, the use of closed-loop DBS to increase stimulation efficiency and patient satisfaction is one of the most important issues under investigation. The present study was aimed to find local field potential (LFP) features of parkinsonian rats, which can determine the timing of stimulation with high accuracy. The LFP signals from rats were recorded in three groups of parkinsonian rat models receiving stimulation (stimulation), without getting stimulation (off-stimulation), and sham-controlled group. The frequency domain and chaotic features of signals were extracted for classifying three classes by support vector machine (SVM) and neural networks. The best combination of features was selected using the genetic algorithm (GA). Finally, the effective features were introduced to determine the on/off stimulation time, and the optimal stimulation parameters were identified. It was found that a combination of frequency domain and chaotic features with an accuracy of about 99% was able to determine the time the DBS must switch on. In about 80.67% of the 1861 different stimulation parameters, the brain was able to maintain its state for about 3 min after stimulation discontinuation.

Subspace-based predictive control of Parkinson's disease: A model-based study

Deep brain stimulation (DBS) of the Basal Ganglia (BG) is an effective treatment to suppress the symptoms of Parkinson's disease (PD). Using a closed-loop scheme in DBS can not only improve its therapeutic effects but it can also reduce its energy consumption and possible side effects. In this paper, a predictive closed loop control strategy is employed to suppress the PD in real-time. A linear multi-input multi-output (MIMO) state-delayed system is considered as a simplified model of the BG neuronal network relating the stimulation signals as inputs to the beta power of local field potentials as PD biomarkers. The effect of time delay in different areas of the BG is incorporated into this model and a real-time subspace-based identification is implemented to continuously model the state of the BG neuronal network and drive the predictive control strategy. Simulation results show that the proposed MIMO subspace based predictive controller can suppress PD symptoms more effectively and with less power consumption compared to the conventional open-loop DBS and a recently proposed single-input single-output closed loop controller.

Closed-loop neuromodulation will increase the utility of mouse models in Bioelectronic Medicine

Mouse models have been of tremendous benefit to medical science for the better part of a century, yet bioelectronic medicine research using mice has been limited to mostly acute studies because of a lack of tools for chronic stimulation and sensing. A wireless neuromodulation platform small enough for implantation in mice will significantly increase the utility of mouse models in bioelectronic medicine. This perspective examines the necessary functionality of such a system and the technical challenges needed to be overcome for its development. Recent progress is examined and the outlook for the future of implantable devices for mice is discussed.

Thalamic deep brain stimulation for post-traumatic neuropathic limb pain: Efficacy at five years' follow-up and effective volume of activated brain tissue

Chronic neuropathic pain affects 7%-10% of the population. Deep brain stimulation (DBS) has shown variable but promising results in its treatment. This study prospectively assessed the long-term effectiveness of DBS in a series of patients with chronic neuropathic pain, correlating clinical results with neuroimaging. Sixteen patients received 5 years' post-surgical follow-up in a single center. Six had phantom limb pain after amputation and 10 had deafferentation pain after traumatic brachial plexus injury. Patient-reported outcome measures were completed before and after surgery, using VAS, UWNPS, BPI and SF-36 scores. Neuroimaging evaluated electrode location and effective volumes of activated tissue (VAT). Two subgroups were created based on the percentage of VAT superimposed upon the ventroposterolateral thalamic nucleus (eVAT), and clinical outcomes were compared. Analgesic effect was assessed at 5 years and compared to preoperative pain, with an improvement on VAS of 76.4% (p=0.0001), on UW-NPS of 35.2% (p=0.3582), on BPI of 65.1% (p=0.0505) and on SF-36 of 5% (p=0.7406). Eight patients with higher eVAT showed improvement on VAS of 67.5% (p=0.0017) while the remaining patients, with lower eVAT, improved by 50.6% (p=0.03607). DBS remained effective in improving chronic neuropathic pain after 5 years. While VPL-targeting contributes to success, analgesia is also obtained by stimulating surrounding posterior ventrobasal thalamic structures and related spinothalamocortical tracts.

Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI

Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep-learning based neural network, which we term GP-net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP-net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP-net performs 3D inference in a patient-specific manner, alleviating the need for atlas-based segmentation. GP-net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state-of-the-art atlas-based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP-net.

Electrical stimulation of the nucleus basalis of meynert: a systematic review of preclinical and clinical data

Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has been clinically investigated in Alzheimer’s disease (AD) and Lewy body dementia (LBD). However, the clinical effects are highly variable, which questions the suggested basic principles underlying these clinical trials. Therefore, preclinical and clinical data on the design of NBM stimulation experiments and its effects on behavioral and neurophysiological aspects are systematically reviewed here. Animal studies have shown that electrical stimulation of the NBM enhanced cognition, increased the release of acetylcholine, enhanced cerebral blood flow, released several neuroprotective factors, and facilitates plasticity of cortical and subcortical receptive fields. However, the translation of these outcomes to current clinical practice is hampered by the fact that mainly animals with an intact NBM were used, whereas most animals were stimulated unilaterally, with different stimulation paradigms for only restricted timeframes. Future animal research has to refine the NBM stimulation methods, using partially lesioned NBM nuclei, to better resemble the clinical situation in AD, and LBD. More preclinical data on the effect of stimulation of lesioned NBM should be present, before DBS of the NBM in human is explored further.

Early decreases in cortical mid-gamma peaks coincide with the onset of motor deficits and precede exaggerated beta build-up in rat models for Parkinson's disease

Evidence suggests that exaggerated beta range local field potentials (LFP) in basal ganglia-thalamocortical circuits constitute an important biomarker for feedback for deep brain stimulation in Parkinson's disease patients, although the role of this phenomenon in triggering parkinsonian motor symptoms remains unclear. A useful model for probing the causal role of motor circuit LFP synchronization in motor dysfunction is the unilateral dopamine cell-lesioned rat, which shows dramatic motor deficits walking contralaterally to the lesion but can walk steadily ipsilaterally on a circular treadmill. Within hours after 6-OHDA injection, rats show marked deficits in ipsilateral walking with early loss of significant motor cortex (MCx) LFP peaks in the mid-gamma 41-45 Hz range in the lesioned hemisphere; both effects were reversed by dopamine agonist administration. Increases in MCx and substantia nigra pars reticulata (SNpr) coherence and LFP power in the 29-40 Hz range emerged more gradually over 7 days, although without further progression of walking deficits. Twice-daily chronic dopamine antagonist treatment induced rapid onset of catalepsy and also reduced MCx 41-45 Hz LFP activity at 1 h, with increases in MCx and SNpr 29-40 Hz power/coherence emerging over 7 days, as assessed during periods of walking before the morning treatments. Thus, increases in high beta power in these parkinsonian models emerge gradually and are not linearly correlated with motor deficits. Earlier changes in cortical circuits, reflected in the rapid decreases in MCx LFP mid-gamma LFP activity, may contribute to evolving plasticity supporting increased beta range synchronized activity in basal ganglia-thalamocortical circuits after loss of dopamine receptor stimulation.

Ramp Rate Evaluation and Configuration for Safe and Tolerable Closed-Loop Deep Brain Stimulation

Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or "ramp" rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson's disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.

Deep Brain Stimulation of the Subgenual Cingulate Cortex for the Treatment of Chronic Low Back Pain

OBJECTIVES

Despite converging basic scientific and clinical evidence of the link between chronic pain and depression, existing therapies do not often take advantage of this overlap. Here, we provide a critical review of the literature that highlights the intersection in brain networks between chronic low back pain (CLBP) and depression and discuss findings from previous deep brain stimulation (DBS) studies for pain. Based on a multidimensional model of pain processing and the connectivity of the subgenual cingulate cortex (SCC) with areas that are implicated in both CLBP and depression, we propose a novel approach to the treatment of CLBP using DBS of the SCC.

MATERIALS AND METHODS

A narrative review with literature assessment.

RESULTS

CLBP is associated with a shift away from somatosensory representation toward brain regions that mediate emotional processes. There is a high degree of overlap between these regions and those involved in depression, including the anterior cingulate cortex, medial prefrontal cortex, nucleus accumbens, and amygdala. Whereas targets sites from previous DBS trials for pain were not anatomically positioned to engage these areas and their associated networks, the SCC is structurally connected to all of these regions and as well as others involved in mediating sensory, cognitive, and affective processing in CLBP.

CONCLUSIONS

CLBP and depression share a common underlying brain network interconnected by the SCC. Current data and novel technology provide an optimal opportunity to develop clinically effective trials of SCC DBS for CLBP.

State-space optimal feedback control of optogenetically driven neural activity

Objective.The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse.Approach.Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch.Main results.This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications.Significance.To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

Electroceutically induced subthalamic high-frequency oscillations and evoked compound activity may explain the mechanism of therapeutic stimulation in Parkinson's disease

Despite having remarkable utility in treating movement disorders, the lack of understanding of the underlying mechanisms of high-frequency deep brain stimulation (DBS) is a main challenge in choosing personalized stimulation parameters. Here we investigate the modulations in local field potentials induced by electrical stimulation of the subthalamic nucleus (STN) at therapeutic and non-therapeutic frequencies in Parkinson's disease patients undergoing DBS surgery. We find that therapeutic high-frequency stimulation (130-180 Hz) induces high-frequency oscillations (~300 Hz, HFO) similar to those observed with pharmacological treatment. Along with HFOs, we also observed evoked compound activity (ECA) after each stimulation pulse. While ECA was observed in both therapeutic and non-therapeutic (20 Hz) stimulation, the HFOs were induced only with therapeutic frequencies, and the associated ECA were significantly more resonant. The relative degree of enhancement in the HFO power was related to the interaction of stimulation pulse with the phase of ECA. We propose that high-frequency STN-DBS tunes the neural oscillations to their healthy/treated state, similar to pharmacological treatment, and the stimulation frequency to maximize these oscillations can be inferred from the phase of ECA waveforms of individual subjects. The induced HFOs can, therefore, be utilized as a marker of successful re-calibration of the dysfunctional circuit generating PD symptoms.

International Legal Approaches to Neurosurgery for Psychiatric Disorders

Neurosurgery for psychiatric disorders (NPD), also sometimes referred to as psychosurgery, is rapidly evolving, with new techniques and indications being investigated actively. Many within the field have suggested that some form of guidelines or regulations are needed to help ensure that a promising field develops safely. Multiple countries have enacted specific laws regulating NPD. This article reviews NPD-specific laws drawn from North and South America, Asia and Europe, in order to identify the typical form and contents of these laws and to set the groundwork for the design of an optimal regulation for the field. Key challenges for this design that are revealed by the review are how to define the scope of the law (what should be regulated), what types of regulations are required (eligibility criteria, approval procedures, data collection, and oversight mechanisms), and how to approach international harmonization given the potential migration of researchers and patients.

Establishment of a Visual Analog Scale for DBS Programming (VISUAL-STIM Trial)

Background: Deep brain stimulation (DBS) has become a standard treatment for advanced stages of Parkinson's disease, essential tremor, and dystonia. In addition to the correct surgical device implantation, effective programming is regarded to be the most important factor for clinical outcome. Despite established strategies for adjusting neurostimulation, DBS programming remains time- and resource-consuming. Although kinematic and neuronal biosignals have recently been examined as potential feedback for closed-loop DBS (CL-DBS), there is an ongoing need for programming strategies to adapt the stimulation parameters and electrode configurations accurately and effectively.

Methods: Here, we tested the usefulness of a patient-rated visual analog scale (VAS) for real-time adjustment of DBS parameters. The stimulation parameters (contact and amplitude) in Parkinson's patients with STN-DBS (n = 17) were optimized based on the patient's subjective VAS rating. A Minkowski distance (M_d) was calculated to compare the individual combination of contact selection and amplitude to the stimulation parameters that resulted from classical programming based on clinical signs and symptoms.

Results: We found no statistically significant difference between VAS-based and classical programming in regard to the specific contact or amplitude used or in regard to the clinical disease severity (UPDRS).

Conclusions: Our data suggest that VAS-based and classical programming strategies both lead to similar short-term results. Although further research will be required to assess the validity of VAS-based DBS programming, our results support the investigation of the patient's subjective rating as an additional and valid feedback signal for individualized DBS adjustment.

Primary motor cortex in Parkinson's disease: Functional changes and opportunities for neurostimulation

Movement abnormalities of Parkinson's disease (PD) arise from disordered neural activity in multiple interconnected brain structures. The planning and execution of movement requires recruitment of a heterogeneous collection of pyramidal projection neurons in the primary motor cortex (M1). The neural representations of movement in M1 single-cell and field potential recordings are directly and indirectly influenced by the midbrain dopaminergic neurons that degenerate in PD. This review examines M1 functional alterations in PD as uncovered by electrophysiological recordings and neurostimulation studies in patients and experimental animal models. Dysfunction of the parkinsonian M1 depends on the severity and/or duration of dopamine-depletion and the species examined, and is expressed as alterations in movement-related firing dynamics; functional reorganisation of local circuits; and changes in field potential beta oscillations. Neurostimulation methods that modulate M1 activity directly (e.g., transcranial magnetic stimulation) or indirectly (subthalamic nucleus deep brain stimulation) improve motor function in PD patients, showing that targeted neuromodulation of M1 is a realistic therapy. We argue that the therapeutic profile of M1 neurostimulation is likely to be greatly enhanced with alternative technologies that permit cell-type specific control and incorporate feedback from electrophysiological biomarkers measured locally.

Surgical Treatment of Parkinson's Disease: Devices and Lesion Approaches

Surgical treatments have transformed the management of Parkinson's disease (PD). Therapeutic options available for the management of PD motor complications include deep brain stimulation (DBS), ablative or lesioning procedures (pallidotomy, thalamotomy, subthalamotomy), and dopaminergic medication infusion devices. The decision to pursue these advanced treatment options is typically done by a multidisciplinary team by considering factors such as the patient's clinical characteristics, efficacy, ease of use, and risks of therapy with a goal to improve PD symptoms and quality of life. DBS has become the most widely used surgical therapy, although there is a re-emergence of interest in ablative procedures with the introduction of MR-guided focused ultrasound. In this article, we review DBS and lesioning procedures for PD, including indications, selection process, and management strategies.

Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease

The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.

Acute effects of adaptive Deep Brain Stimulation in Parkinson's disease

Background

Beta-based adaptive Deep Brain Stimulation (aDBS) is effective in Parkinson’s disease (PD), when assessed in the immediate post-implantation phase. However, the potential benefits of aDBS in patients with electrodes chronically implanted, in whom changes due to the microlesion effect have disappeared, are yet to be assessed.

Methods

To determine the acute effectiveness and side-effect profile of aDBS in PD compared to conventional continuous DBS (cDBS) and no stimulation (NoStim), years after DBS implantation, 13 PD patients undergoing battery replacement were pseudo-randomised in a crossover fashion, into three conditions (NoStim, aDBS or cDBS), with a 2-min interval between them. Patient videos were blindly evaluated using a short version of the Unified Parkinson’s Disease Rating Scale (subUPDRS), and the Speech Intelligibility Test (SIT).

Results

Mean disease duration was 16 years, and the mean time since DBS-implantation was 6.9 years. subUPDRS scores (11 patients tested) were significantly lower both in aDBS (p = <.001), and cDBS (p = .001), when compared to NoStim. Bradykinesia subscores were significantly lower in aDBS (p = .002), and did not achieve significance during cDBS (p = .08), when compared to NoStim. Two patients demonstrated re-emerging tremor during aDBS. SIT scores of patients who presented stimulation-induced dysarthria significantly worsened in cDBS (p = .009), but not in aDBS (p = .407), when compared to NoStim. Overall, stimulation was applied 48.8% of the time during aDBS.

Conclusion

Beta-based aDBS is effective in PD patients with bradykinetic phenotypes, delivers less stimulation than cDBS, and potentially has a more favourable speech side-effect profile. Patients with prominent tremor may require a modified adaptive strategy.

Randomized, Double-Blind Assessment of LFP Versus SUA Guidance in STN-DBS Lead Implantation: A Pilot Study

Background: The efficacy of deep brain stimulation (DBS) therapy in Parkinson's disease (PD) patients is highly dependent on the precise localization of the target structures such as subthalamic nucleus (STN). Most commonly, microelectrode single unit activity (SUA) recordings are performed to refine the target. This process is heavily experience based and can be technically challenging. Local field potentials (LFPs), representing the activity of a population of neurons, can be obtained from the same microelectrodes used for SUA recordings and allow flexible online processing with less computational complexity due to lower sampling rate requirements. Although LFPs have been shown to contain biomarkers capable of predicting patients' symptoms and differentiating various structures, their use in the localization of the STN in the clinical practice is not prevalent. Methods: Here we present, for the first time, a randomized and double-blinded pilot study with intraoperative online LFP processing in which we compare the clinical benefit from SUA- versus LFP-based implantation. Ten PD patients referred for bilateral STN-DBS were randomly implanted using either SUA or LFP guided targeting in each hemisphere. Although both SUA and LFP were recorded for each STN, the electrophysiologist was blinded to one at a time. Three months postoperatively, the patients were evaluated by a neurologist blinded to the intraoperative recordings to assess the performance of each modality. While SUA-based decisions relied on the visual and auditory inspection of the raw traces, LFP-based decisions were given through an online signal processing and machine learning pipeline. Results: We found a dramatic agreement between LFP- and SUA-based localization (16/20 STNs) providing adequate clinical improvement (51.8% decrease in 3-month contralateral motor assessment scores), with LFP-guided implantation resulting in greater average improvement in the discordant cases (74.9%, n = 3 STNs). The selected tracks were characterized by higher activity in beta (11-32 Hz) and high-frequency (200-400 Hz) bands (p < 0.01) of LFPs and stronger non-linear coupling between these bands (p < 0.05). Conclusion: Our pilot study shows equal or better clinical benefit with LFP-based targeting. Given the robustness of the electrode interface and lower computational cost, more centers can utilize LFP as a strategic feedback modality intraoperatively, in conjunction to the SUA-guided targeting.

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.