Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes

Introduction

In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD.

Methods

This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days.

Results

The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants.

Conclusion

These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.

Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia

AIMS

The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits.

METHODS

We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system.

RESULTS

Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS.

CONCLUSION

Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Parkinson's disease tremor prediction using EEG data analysis-A preliminary and feasibility study

PURPOSE

Tremor is one of the hallmarks of Parkinson's disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series.

METHODS

A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions.

RESULTS

Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%.

CONCLUSION

This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications.

Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop

Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease

INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high.

AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming.

EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor

Objective.Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician.Approach.Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient's response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented 'safe Bayesian optimization' to automatically discover tolerable exploration boundaries.Main results.We tested the system in 15 patients (nine with Parkinson's disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing15.1±0.7settings when maximum safe exploration boundaries were predefined, and17.7±4.9when the algorithm itself determined safe exploration boundaries.Significance.We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.

Floating EMG sensors and stimulators wirelessly powered and operated by volume conduction for networked neuroprosthetics

BACKGROUND

Implantable neuroprostheses consisting of a central electronic unit wired to electrodes benefit thousands of patients worldwide. However, they present limitations that restrict their use. Those limitations, which are more adverse in motor neuroprostheses, mostly arise from their bulkiness and the need to perform complex surgical implantation procedures. Alternatively, it has been proposed the development of distributed networks of intramuscular wireless microsensors and microstimulators that communicate with external systems for analyzing neuromuscular activity and performing stimulation or controlling external devices. This paradigm requires the development of miniaturized implants that can be wirelessly powered and operated by an external system. To accomplish this, we propose a wireless power transfer (WPT) and communications approach based on volume conduction of innocuous high frequency (HF) current bursts. The currents are applied through external textile electrodes and are collected by the wireless devices through two electrodes for powering and bidirectional digital communications. As these devices do not require bulky components for obtaining power, they may have a flexible threadlike conformation, facilitating deep implantation by injection.

METHODS

We report the design and evaluation of advanced prototypes based on the above approach. The system consists of an external unit, floating semi-implantable devices for sensing and stimulation, and a bidirectional communications protocol. The devices are intended for their future use in acute human trials to demonstrate the distributed paradigm. The technology is assayed in vitro using an agar phantom, and in vivo in hindlimbs of anesthetized rabbits.

RESULTS

The semi-implantable devices were able to power and bidirectionally communicate with the external unit. Using 13 commands modulated in innocuous 3 MHz HF current bursts, the external unit configured the sensing and stimulation parameters, and controlled their execution. Raw EMG was successfully acquired by the wireless devices at 1 ksps.

CONCLUSIONS

The demonstrated approach overcomes key limitations of existing neuroprostheses, paving the way to the development of distributed flexible threadlike sensors and stimulators. To the best of our knowledge, these devices are the first based on WPT by volume conduction that can work as EMG sensors and as electrical stimulators in a network of wireless devices.

Computer models evaluating hand tremors in Parkinson's disease patients

One of the most characteristic signs of Parkinson's disease (PD) is hand tremor. The MDS-UPDRS scale evaluates different aspects of the disease. The tremor score is a part of the MDS-UPDRS scale, which provides instructions for rating it, by observation, with an integer from 0 to 4. Nevertheless, this form of assessment is subjective and dependent on visual acuity, clinical judgment, and even the mood of the individual examiner. On the other hand, in many cases, existing computational models proposed to resolve the disadvantages of the MDS-UPDRS scale may have uncertainty in differentiating a category of a slight Parkinson tremor from voluntary movements. In this study, 554 measurements from Parkinson's patients, and 60 measurements from healthy subjects, were recorded with inertial sensors placed on the back of each hand. Five biomechanical indicators characterised the hand tremor. With these indicators, the three fuzzy inference models proposed can differentiate, in the first instance, the presence of postural or resting tremor from a normal movement of the hand, and if detected, to determine its severity. The fuzzy inference models allowed following the criteria of the MDS-UPDRS scale, providing an evaluation with an accuracy of two decimal digits and which, due to its simplicity, can be implemented in clinical environments. The assessments of three experts validated the computer model.

Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems

INTRODUCTION

Motor complication management is one of the main unmet needs in Parkinson's disease patients.

AREAS COVERED

Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology.

EXPERT OPINION

Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.

Deep brain stimulation: is it time to change gears by closing the loop?

Objective.Adaptive deep brain stimulation (aDBS) is a form of invasive stimulation that was conceived to overcome the technical limitations of traditional DBS, which delivers continuous stimulation of the target structure without considering patients' symptoms or status in real-time. Instead, aDBS delivers on-demand, contingency-based stimulation. So far, aDBS has been tested in several neurological conditions, and will be soon extensively studied to translate it into clinical practice. However, an exhaustive description of technical aspects is still missing.Approach.in this topical review, we summarize the knowledge about the current (and future) aDBS approach and control algorithms to deliver the stimulation, as reference for a deeper undestending of aDBS model.Main results.We discuss the conceptual and functional model of aDBS, which is based on the sensing module (that assesses the feedback variable), the control module (which interpretes the variable and elaborates the new stimulation parameters), and the stimulation module (that controls the delivery of stimulation), considering both the historical perspective and the state-of-the-art of available biomarkers.Significance.aDBS modulates neuronal circuits based on clinically relevant biofeedback signals in real-time. First developed in the mid-2000s, many groups have worked on improving closed-loop DBS technology. The field is now at a point in conducting large-scale randomized clinical trials to translate aDBS into clinical practice. As we move towards implanting brain-computer interfaces in patients, it will be important to understand the technical aspects of aDBS.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson's disease

BACKGROUND

Resting tremor is an essential characteristic in patients suffering from Parkinson's disease (PD).

OBJECTIVE

Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring.

METHODS

Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson's Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis.

RESULTS

The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities.

CONCLUSION

The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

Wearable sensor-driven responsive deep brain stimulation for essential tremor

BACKGROUND

Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET.

OBJECTIVE

The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals.

METHODS

Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states.

RESULTS

Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS.

CONCLUSIONS

The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.

Deep brain stimulation programming strategies: segmented leads, independent current sources, and future technology

Introduction: Advances in neuromodulation and deep brain stimulation (DBS) technologies have facilitated opportunities for improved clinical benefit and side effect management. However, new technologies have added complexity to clinic-based DBS programming.Areas covered: In this article, we review basic basal ganglia physiology, proposed mechanisms of action and technical aspects of DBS. We discuss novel DBS technologies for movement disorders including the role of advanced imaging software, lead design, IPG design, novel programming techniques including directional stimulation and coordinated reset neuromodulation. Additional topics include the use of potential biomarkers, such as local field potentials, electrocorticography, and adaptive stimulation. We will also discuss future directions including optogenetically inspired DBS.Expert opinion: The introduction of DBS for the management of movement disorders has expanded treatment options. In parallel with our improved understanding of brain physiology and neuroanatomy, new technologies have emerged to address challenges associated with neuromodulation, including variable effectiveness, side-effects, and programming complexity. Advanced functional neuroanatomy, improved imaging, real-time neurophysiology, improved electrode designs, and novel programming techniques have collectively been driving improvements in DBS outcomes.

Adaptive deep brain stimulation (aDBS)

Deep brain stimulation is an established technique for the treatment of movement disorders related to neurodegenerative diseases such as Parkinson's disease (PD) and essential tremor (ET). Its application seems also feasible for the treatment of neuropsychiatric disorders such as treatment resistant depression (TRD) and Tourette's syndrome (TS). In a typical deep brain stimulation system, the amount of current delivered to the patients is constant and regulated by the physician. Conversely, an adaptive deep brain stimulation system (aDBS) is a closed loop system that adjusts the stimulation parameters according to biomarkers which reflect the patient's clinical state. In this chapter, we examined the main issues related to aDBS systems, which are both clinical and technological in nature. From a clinical point of view, we have reported the major findings related to symptoms management using aDBS and principal findings in animal models, showing that the implementation of closed loop adaptive deep brain stimulation can ameliorate symptom management in neurodegenerative disorders. From the technological point of view, we reported the major advances related to aDBS system design and implementation, such as noise filtering methods, biomarkers recording and processing to adjust pulse delivery. To date, aDBS systems represent a major evolution in brain stimulation, further developments are needed to maximize the efficacy of this technique and to expand its use in a wide range of neuropsychiatric disorders.

Clinical perspectives of adaptive deep brain stimulation

BACKGROUND

The application of stimulators implanted directly over deep brain structures (i.e., deep brain stimulation, DBS) was developed in the late 1980s and has since become a mainstream option to treat several neurological conditions. Conventional DBS involves the continuous stimulation of the target structure, which is an approach that cannot adapt to patients' changing symptoms or functional status in real-time. At the beginning of 2000, a more sophisticated form of stimulation was conceived to overcome these limitations. Adaptive deep brain stimulation (aDBS) employs on-demand, contingency-based stimulation to stimulate only when needed. So far, aDBS has been tested in several pathological conditions in animal and human models.

OBJECTIVE

To review the current findings obtained from application of aDBS to animal and human models that highlights effects on motor, cognitive and psychiatric behaviors.

FINDINGS

while aDBS has shown promising results in the treatment of Parkinson's disease and essential tremor, the possibility of its use in less common DBS indications, such as cognitive and psychiatric disorders (Alzheimer's disease, obsessive-compulsive disorder, post-traumatic stress disorder) is still challenging.

CONCLUSIONS

While aDBS seems to be effective to treat movement disorders (Parkinson's disease and essential tremor), its role in cognitive and psychiatric disorders is to be determined, although neurophysiological assumptions are promising.

Wearable Peripheral Electrical Stimulation Devices for the Reduction of Essential Tremor: A Review

Essential tremor is the most common pathological tremor, with a prevalence of 6.3% in people over 65 years of age. This disorder interferes with a patient's ability to carry out activities of daily living independently, and treatment with medical and surgical interventions is often insufficient or contraindicated. Mechanical orthoses have not been widely adopted by patients due to discomfort and lack of discretion. Over the past 30 years, peripheral electrical stimulation has been investigated as a possible treatment for patients who have not found other treatment options to be satisfactory, with wearable devices revolutionizing this emerging approach in recent years. In this paper, an overview of essential tremor and its current medical and surgical treatment options are presented. Following this, tremor detection, measurement and characterization methods are explored with a focus on the measurement options that can be incorporated into wearable devices. Then, novel interventions for essential tremor are described, with a detailed review of open and closed-loop peripheral electrical stimulation methods. Finally, discussion of the need for wearable closed-loop peripheral electrical stimulation devices for essential tremor, approaches in their implementation, and gaps in the literature for further research are presented.

Digital Health Integration With Neuromodulation Therapies: The Future of Patient-Centric Innovation in Neuromodulation

Digital health can drive patient-centric innovation in neuromodulation by leveraging current tools to identify response predictors and digital biomarkers. Iterative technological evolution has led us to an ideal point to integrate digital health with neuromodulation. Here, we provide an overview of the digital health building-blocks, the status of advanced neuromodulation technologies, and future applications for neuromodulation with digital health integration.

Technology of deep brain stimulation: current status and future directions

Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.

Deep brain stimulation: a review of the open neural engineering challenges

OBJECTIVE

Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy.

APPROACH

We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS.

MAIN RESULTS

We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns.

SIGNIFICANCE

This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.

Non-invasive brain stimulation for Parkinson's disease: Clinical evidence, latest concepts and future goals: A systematic review

Parkinson's disease (PD) is becoming a major public-health issue in an aging population. Available approaches to treat advanced PD still have limitations; new therapies are needed. The non-invasive brain stimulation (NIBS) may offer a complementary approach to treat advanced PD by personalized stimulation. Although NIBS is not as effective as the gold-standard levodopa, recent randomized controlled trials show promising outcomes in the treatment of PD symptoms. Nevertheless, only a few NIBS-stimulation paradigms have shown to improve PD's symptoms. Current clinical recommendations based on the level of evidence are reported in Table 1 through Table 3. Furthermore, novel technological advances hold promise and may soon enable the non-invasive stimulation of deeper brain structures for longer periods.

A Soft Exoskeleton for Tremor Suppression Equipped with Flexible Semiactive Actuator

Pathological tremor is a kind of movement disorder that affects a wide range of patients with Parkinson's disease and essential tremor. Different from available clinical treatments for tremor, including drug and surgery therapy, a novel soft exoskeleton for tremor suppression (SETS) based on assistive technologies is proposed in this study. The SETS system is equipped with a controllable flexible semiactive actuator based on magnetorheological fluid. To overcome the drawbacks of traditional fluidic semiactive actuators, we devise a soft semiactive actuator that is a combination of cylinder-piston damper and elastic fluidic damper. The overall system is characterized with low mass, compact structure, comfortable wearability as well as real-time adjustability for tremor attenuation with varying intensity. The SETS can assist in suppressing tremor of wrist joint in three degrees of freedom. The prototype weighs about 255 g and can yield a maximum damping force of about 11 N. Simulation studies and experimental tests were carried out to evaluate the performance of the system. The results show that the SETS could reduce wrist tremor regarding magnitude of acceleration and angular velocity by 61.39% and 56.22%, respectively, which validate the manifest mechanical efficiency of this functional system.

The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation

Therapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.

Gait characterization for patients with orthostatic tremor

INTRODUCTION

Orthostatic tremor (OT) patients frequently report gait unsteadiness with the advancement of disease; however, there is little understanding of its physiology. We sought to examine in OT, the spatial and temporal characteristics of gait, and the relationship with tremor physiology.

METHODS

Gait parameters for OT (n = 16) were recorded with an instrumented Zeno walkway system. All participants complained of gait unsteadiness, especially during slow walking. In a subset of OT, recordings were synchronized with a wireless EMG system for tremor assessment and feet pressure recording. Gait assessments were performed at self-selected habitual, fast, and slow speeds.

RESULTS

Compared to data available for an age- and sex-matched healthy controls, OT patients had a significantly reduced step length, increased step width, and increased gait variability (p < 0.0001). Tremor discharges related to OT were consistently recorded across three different speeds of walking. These discharges persisted through all phases of the gait cycle, including the swing phase when the limb was not weight-bearing. The highest tremor amplitude was recorded in the single support phase, followed by double support, and least during the swing phase.

CONCLUSION

OT patients have distinct gait abnormalities similar to cerebellar disorders. Tremor discharges from the non-weight bearing leg in the swing phase suggests that muscle contractions, even when occurring without resistance, contribute to OT generation.

On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders

The results presented in this paper indicate that future on-demand Deep Brain Stimulation (DBS) systems for chronic use in patients with movement disorders should continuously and adaptively "learn" in order to maintain high symptom control efficacy. In this work, two machine learning algorithms-Decision Tree and LArge Memory STorage And Retrieval (LAMSTAR) neural network, both with surface Electromyography and accelerometry as control signals-are used to predict onset of tremor after DBS has been switched off in two patients, one suffering from Parkinson's disease and the other from essential tremor. The novelty of this work is that training and testing are done by using different data recorded during sessions at least one week apart. The question is whether the applied algorithms are robust to long-term operation (as patient's control signal may change over time due to disease progression, displacement of the wearable sensor, etc.). Various metrics are used to compare the performance of the proposed approach to those available in the literature, where training and testing are done on data from the same recording session. It is shown that a 100% sensitivity is achieved for training and testing over the same session; however, the sensitivity reduces when tested over a different session. The ratio of predicted stimulation-off time to observed stimulation-off time value is also found to be lower when training and testing on data from separate sessions. These results point to the need of adaptive learning in on-demand DBS systems.

Approaches to closed-loop deep brain stimulation for movement disorders

OBJECTIVE Deep brain stimulation (DBS) is a safe and effective therapy for movement disorders, such as Parkinson's disease (PD), essential tremor (ET), and dystonia. There is considerable interest in developing "closed-loop" DBS devices capable of modulating stimulation in response to sensor feedback. In this paper, the authors review related literature and present selected approaches to signal sources and approaches to feedback being considered for deployment in closed-loop systems. METHODS A literature search using the keywords "closed-loop DBS" and "adaptive DBS" was performed in the PubMed database. The search was conducted for all articles published up until March 2018. An in-depth review was not performed for publications not written in the English language, nonhuman studies, or topics other than Parkinson's disease or essential tremor, specifically epilepsy and psychiatric conditions. RESULTS The search returned 256 articles. A total of 71 articles were primary studies in humans, of which 50 focused on treatment of movement disorders. These articles were reviewed with the aim of providing an overview of the features of closed-loop systems, with particular attention paid to signal sources and biomarkers, general approaches to feedback control, and clinical data when available. CONCLUSIONS Closed-loop DBS seeks to employ biomarkers, derived from sensors such as electromyography, electrocorticography, and local field potentials, to provide real-time, patient-responsive therapy for movement disorders. Most studies appear to focus on the treatment of Parkinson's disease. Several approaches hold promise, but additional studies are required to determine which approaches are feasible, efficacious, and efficient.

Monitoring Parkinson's disease symptoms during daily life: a feasibility study

Parkinson's disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson's disease monitoring system, which aims to measure Parkinson's disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson's disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson's disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson's disease monitoring system is a feasible method to use in a diverse Parkinson's disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.

Emerging technologies for improved deep brain stimulation

Deep brain stimulation (DBS) is an effective treatment for common movement disorders and has been used to modulate neural activity through delivery of electrical stimulation to key brain structures. The long-term efficacy of stimulation in treating disorders, such as Parkinson's disease and essential tremor, has encouraged its application to a wide range of neurological and psychiatric conditions. Nevertheless, adoption of DBS remains limited, even in Parkinson's disease. Recent failed clinical trials of DBS in major depression, and modest treatment outcomes in dementia and epilepsy, are spurring further development. These improvements focus on interaction with disease circuits through complementary, spatially and temporally specific approaches. Spatial specificity is promoted by the use of segmented electrodes and field steering, and temporal specificity involves the delivery of patterned stimulation, mostly controlled through disease-related feedback. Underpinning these developments are new insights into brain structure-function relationships and aberrant circuit dynamics, including new methods with which to assess and refine the clinical effects of stimulation.

Toward a closed-loop deep brain stimulation in Parkinson's disease using local field potential in parkinsonian rat model

Deep brain stimulation (DBS) is an invasive method used for treating Parkinson's disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals.

METHODS

In the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm.

RESULTS

The results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42% which increased to 84.41% using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective high-frequency features to control the closed-loop DBS.

CONCLUSIONS

The ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.

Adaptive delivery of continuous and delayed feedback deep brain stimulation - a computational study

Adaptive deep brain stimulation (aDBS) is a closed-loop method, where high-frequency DBS is turned on and off according to a feedback signal, whereas conventional high-frequency DBS (cDBS) is delivered permanently. Using a computational model of subthalamic nucleus and external globus pallidus, we extend the concept of adaptive stimulation by adaptively controlling not only continuous, but also demand-controlled stimulation. Apart from aDBS and cDBS, we consider continuous pulsatile linear delayed feedback stimulation (cpLDF), specifically designed to induce desynchronization. Additionally, we combine adaptive on-off delivery with continuous delayed feedback modulation by introducing adaptive pulsatile linear delayed feedback stimulation (apLDF), where cpLDF is turned on and off using pre-defined amplitude thresholds. By varying the stimulation parameters of cDBS, aDBS, cpLDF, and apLDF we obtain optimal parameter ranges. We reveal a simple relation between the thresholds of the local field potential (LFP) for aDBS and apLDF, the extent of the stimulation-induced desynchronization, and the integral stimulation time required. We find that aDBS and apLDF can be more efficient in suppressing abnormal synchronization than continuous simulation. However, apLDF still remains more efficient and also causes a stronger reduction of the LFP beta burst length. Hence, adaptive on-off delivery may further improve the intrinsically demand-controlled pLDF.

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system.

Deep Brain Stimulation for Parkinson Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder and affects more than 1 million individuals in the United States. Deep brain stimulation (DBS) is one form of treatment of PD. DBS treatment is still evolving due to technological innovations that shape how this therapy is used.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Toward adaptive deep brain stimulation in Parkinson's disease: a review

Clinical deep brain stimulation (DBS) is now regarded as the therapeutic intervention of choice at the advanced stages of Parkinson's disease. However, some major challenges of DBS are stimulation induced side effects and limited pacemaker battery life. Side effects and shortening of pacemaker battery life are mainly as a result of continuous stimulation and poor stimulation focus. These drawbacks can be mitigated using adaptive DBS (aDBS) schemes. Side effects resulting from continuous stimulation can be reduced through adaptive control using closed-loop feedback, while those due to poor stimulation focus can be mitigated through spatial adaptation. Other advantages of aDBS include automatic, rather than manual, initial adjustment and programming, and long-term adjustments to maintain stimulation parameters with changes in patient's condition. Both result in improved efficacy. This review focuses on the major areas that are essential in driving technological advances for the various aDBS schemes. Their challenges, prospects and progress so far are analyzed. In addition, important advances and milestones in state-of-the-art aDBS schemes are highlighted – both for closed-loop adaption and spatial adaption. With perspectives and future potentials of DBS provided at the end.

Who May Benefit From On-Demand Control of Deep Brain Stimulation? Noninvasive Evaluation of Parkinson Patients

BACKGROUND

In closed-loop on-demand control (ODC) of deep brain stimulation (DBS), stimulation is applied only when symptoms appear. Following stimulation of a fixed duration, DBS is switched off until the symptoms reappear. By repeating these demand-driven cycles, the amount of stimulation delivered can be decreased, thereby reducing DBS side-effects and improving battery-life of the pulse-generator. This article introduces R_o metric for quantification of degree of benefit of ODC and explores candidate selection in tremor-dominant Parkinson's disease (PD).

METHOD

The study was performed on nine PD patients previously implanted with Medtronic DBS systems. Accelerometer sensor was placed on the tremor-dominant hand to detect onset of tremor. Fixed duration of stimulation (DS) of 20-80 sec was applied. Once the tremor was observed, stimulation was switched on. These trials were repeated during resting, postural, and kinetic conditions. R_o metric was calculated as the ratio of stimulation-off tremor-free period to the DS. R_o calculated at different DS were compared for each patient.

RESULTS

We found that for each patient, R_o varied with DS and an optimal DS^* gave a higher percentage of stimulation-off time. Average R_o at DS^* varied from 0.554 to 4.24 for eight patients giving 35%-80% stimulation-off time.

CONCLUSIONS

R_o values can be used for selection of optimal DS^* in ODC. Three of nine patients were found to be tremor-free without stimulation for >50% of total time with even up to 80% in one patient. Patients with low R_o may not benefit from ODC in DBS, where the trade-off between having side-effects and using ODC system will need to be assessed.

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.

DBS Programming: An Evolving Approach for Patients with Parkinson's Disease

Deep brain stimulation (DBS) surgery is a well-established therapy for control of motor symptoms in Parkinson's disease. Despite an appropriate targeting and an accurate placement of DBS lead, a thorough and efficient programming is critical for a successful clinical outcome. DBS programming is a time consuming and laborious manual process. The current approach involves use of general guidelines involving determination of the lead type, electrode configuration, impedance check, and battery check. However there are no validated and well-established programming protocols. In this review, we will discuss the current practice and the recent advances in DBS programming including the use of interleaving, fractionated current, directional steering of current, and the use of novel DBS pulses. These technological improvements are focused on achieving a more efficient control of clinical symptoms with the least possible side effects. Other promising advances include the introduction of computer guided programming which will likely impact the efficiency of programming for the clinicians and the possibility of remote Internet based programming which will improve access to DBS care for the patients.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy

Continuous high-frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof-of-principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Stimulating at the right time: phase-specific deep brain stimulation

SEE MOLL AND ENGEL DOI101093/AWW308 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Brain regions dynamically engage and disengage with one another to execute everyday actions from movement to decision making. Pathologies such as Parkinson's disease and tremor emerge when brain regions controlling movement cannot readily decouple, compromising motor function. Here, we propose a novel stimulation strategy that selectively regulates neural synchrony through phase-specific stimulation. We demonstrate for the first time the therapeutic potential of such a stimulation strategy for the treatment of patients with pathological tremor. Symptom suppression is achieved by delivering stimulation to the ventrolateral thalamus, timed according to the patient's tremor rhythm. Sustained locking of deep brain stimulation to a particular phase of tremor afforded clinically significant tremor relief (up to 87% tremor suppression) in selected patients with essential tremor despite delivering less than half the energy of conventional high frequency stimulation. Phase-specific stimulation efficacy depended on the resonant characteristics of the underlying tremor network. Selective regulation of neural synchrony through phase-locked stimulation has the potential to both increase the efficiency of therapy and to minimize stimulation-induced side effects.