Wearable sensor-driven responsive deep brain stimulation for essential tremor

Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation

Deep brain stimulation (DBS), a proven treatment for movement disorders, also holds promise for the treatment of psychiatric and cognitive conditions. However, for DBS to be clinically effective, it may require DBS technology that can alter or trigger stimulation in response to changes in biomarkers sensed from the patient's brain. A growing body of evidence suggests that such adaptive DBS is feasible, it might achieve clinical effects that are not possible with standard continuous DBS and that some of the best biomarkers are signals from the cerebral cortex. Yet capturing those markers requires the placement of cortex-optimized electrodes in addition to standard electrodes for DBS. In this Perspective we argue that the need for cortical biomarkers in adaptive DBS and the unfortunate convergence of regulatory and financial factors underpinning the unavailability of cortical electrodes for chronic uses threatens to slow down or stall research on adaptive DBS and propose public-private partnerships as a potential solution to such a critical technological gap.

Closing the loop in DBS: A data-driven approach

Deep brain stimulation (DBS) has transformed the treatment of movement disorders like Parkinson's Disease (PD). Innovations in DBS technology and experimentation have fostered adaptive DBS (aDBS), which employs a closed-loop system that senses physiological biomarkers to inform precise neuromodulation and personalized therapy. This review analyzes several promising advances in aDBS, including biomarker detection, control policies, mechanisms of efficacy, and a data-driven approach using artificial intelligence to decode motor states from neural signals. Investigations into data-driven approaches have expanded biomarker detection beyond subcortical beta oscillations, leveraging other neural and kinematic signals. Future aDBS systems that accommodate multi-modal inputs have the potential to bolster therapeutic efficacy and address symptoms not addressed by beta-driven aDBS. Continuing investigation is necessary to address existing technical and computational challenges for further clinical translation.

Technological interventions for the suppression of hand tremors: A literature review

A tremor is a neurological disorder that results in trembling or shaking in one or more body parts. A thorough literature review was conducted to investigate the methods for suppressing tremors. We looked for articles published between 1995 and 2024 in the databases CINAHL (Cumulative Index to Nursing and Allied Health Literature), PubMed, Medline, Embase, Scopus, and Cochrane. Two thousand two hundred fifty distinct items were discovered after an extensive search. Based only on the title, 250 were included. Two hundred papers were deemed ineligible after the abstracts were assessed. The remaining 26 articles were shortlisted after screening titles and abstracts and categorized based on treatment methods for hand tremors. According to the study's findings, deep brain stimulation (DBS) and electrical stimulation both reduced tremors considerably. It was also evident that attenuation systems and passive devices lessen the effects of tremors; target tracking tasks can lessen physiological tremors in postural posture; ET may have better hand functions after cold water treatment than warm water or at baseline; and targeted ultrasound thalamotomy is an effective treatment for ET, as it improved quality of life (QoL) significantly. Additionally, the design, development, and evaluation of wearable devices and pharmaceutical interventions for tremor suppression were investigated in detail. The main objective was to perform a comparative analysis of the merits and demerits of both treatment methodologies in terms of functional outcomes, users' comfort, and side effects. The review highlights wearable devices as a beneficial option for tremor suppression, offering comfort, safety, and advanced technology over pharmaceutical intervention methodologies.

Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial

Deep brain stimulation (DBS) is a widely used therapy for Parkinson's disease (PD) but lacks dynamic responsiveness to changing clinical and neural states. Feedback control might improve therapeutic effectiveness, but the optimal control strategy and additional benefits of 'adaptive' neurostimulation are unclear. Here we present the results of a blinded randomized cross-over pilot trial aimed at determining the neural correlates of specific motor signs in individuals with PD and the feasibility of using these signals to drive adaptive DBS. Four male patients with PD were recruited from a population undergoing DBS implantation for motor fluctuations, with each patient receiving adaptive DBS and continuous DBS. We identified stimulation-entrained gamma oscillations in the subthalamic nucleus or motor cortex as optimal markers of high versus low dopaminergic states and their associated residual motor signs in all four patients. We then demonstrated improved motor symptoms and quality of life with adaptive compared to clinically optimized standard stimulation. The results of this pilot trial highlight the promise of personalized adaptive neurostimulation in PD based on data-driven selection of neural signals. Furthermore, these findings provide the foundation for further larger clinical trials to evaluate the efficacy of personalized adaptive neurostimulation in PD and other neurological disorders. ClinicalTrials.gov registration: NCT03582891 .

Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders

Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.

Responsive Versus Continuous Deep Brain Stimulation for Speech in Essential Tremor: A Pilot Study

BACKGROUND

Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET).

OBJECTIVE

To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression.

METHODS

Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS).

RESULTS

During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF.

CONCLUSIONS

rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.

Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Advancements in non-invasive microwave brain stimulation: A comprehensive survey

This survey provides a comprehensive insight into the world of non-invasive brain stimulation and focuses on the evolving landscape of deep brain stimulation through microwave research. Non-invasive brain stimulation techniques provide new prospects for comprehending and treating neurological disorders. We investigate the methods shaping the future of deep brain stimulation, emphasizing the role of microwave technology in this transformative journey. Specifically, we explore antenna structures and optimization strategies to enhance the efficiency of high-frequency microwave stimulation. These advancements can potentially revolutionize the field by providing a safer and more precise means of modulating neural activity. Furthermore, we address the challenges that researchers currently face in the realm of microwave brain stimulation. From safety concerns to methodological intricacies, this survey outlines the barriers that must be overcome to fully unlock the potential of this technology. This survey serves as a roadmap for advancing research in microwave brain stimulation, pointing out potential directions and innovations that promise to reshape the field.

Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?

Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.

Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity

OBJECTIVE

To distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity.

METHODS

We re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest.

RESULTS

Subthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off.

CONCLUSIONS

Electrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity.

SIGNIFICANCE

By combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease

Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Neurotechnology for Pain

Neurotechnologies for treating pain rely on electrical stimulation of the central or peripheral nervous system to disrupt or block pain signaling and have been commercialized to treat a variety of pain conditions. While their adoption is accelerating, neurotechnologies are still frequently viewed as a last resort, after many other treatment options have been explored. We review the pain conditions commonly treated with electrical stimulation, as well as the specific neurotechnologies used for treating those conditions. We identify barriers to adoption, including a limited understanding of mechanisms of action, inconsistent efficacy across patients, and challenges related to selectivity of stimulation and off-target side effects. We describe design improvements that have recently been implemented, as well as some cutting-edge technologies that may address the limitations of existing neurotechnologies. Addressing these challenges will accelerate adoption and change neurotechnologies from last-line to first-line treatments for people living with chronic pain.

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.

Insights and opportunities for deep brain stimulation as a brain circuit intervention

Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning

Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our framework's decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.

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.

Utilizing Data from Wearable Technologies in the Era of Telemedicine to Assess Patient Function and Outcomes in Neurosurgery: Systematic Review and Time-Trend Analysis of the Literature

BACKGROUND

The COVID-19 pandemic has driven the increased use of telemedicine and the adoption of wearable technology in neurosurgery. We reviewed studies exploring the use of wearables on neurosurgical patients and analyzed wearables' scientific production trends.

METHODS

The review encompassed PubMed, EMBASE, Web of Science, and Cochrane Library. Bibliometric analysis was performed using citation data of the included studies through Elsevier's Scopus database. Linear regression was utilized to understand scientific production trends. All analyses were performed on R 4.1.2.

RESULTS

We identified 979 studies. After screening, 49 studies were included. Most studies evaluated wearable technology use for patients with spinal pathology (n = 31). The studies were published over a 24-year period (1998-2021). Forty-seven studies involved wearable device use relevant to telemedicine. Bibliometric analysis revealed a compounded annual growth rate of 7.3%, adjusted for inflation, in annual scientific production from 1998 to 2021 (coefficient=1.3; 95% Confidence Interval = [0.7, 1.9], P < 0.01). Scientific production steadily increased in 2014 (n = 1) and peaked from 2019 (n = 8) to 2021 (n = 13) in correlation with the COVID-19 pandemic. Publications spanned 34 journals, averaged 24.4 citations per article, 3.0 citations per year per article, and 8.3 authors per article.

CONCLUSION

Wearables can provide clinicians with objective measurements to determine patient function and quality of life. The rise in articles related to wearables in neurosurgery demonstrates the increased adoption of wearable devices during the COVID-19 pandemic. Wearable devices appear to be a key component in this era of telemedicine and their positive utility and practicality are increasingly being realized in neurosurgery.

Unravelling delayed therapy escape after thalamic deep brain stimulation for essential tremor? - Additional clinical and neuroimaging evidence

BACKGROUND

Delayed therapy escape after thalamic deep brain stimulation (DBS) for essential tremor is a serious yet frequent condition. It is often difficult to detect this process at onset due to its gradual evolution.

OBJECTIVE

Here we aim to identify clinical and neuroimaging hallmarks of delayed therapy escape.

METHODS

We retrospectively studied operationalized and quantitative analyses of tremor and gait, as well as [¹⁸F]fluorodeoxyglucose (FDG) PET of 12 patients affected by therapy escape. All examinations were carried out with activated DBS (ON) and 72 h after deactivation (OFF_72h); gait and tremor were also analyzed directly after deactivation (OFF_0h). Changes of normalized glucose metabolism between stimulation conditions were assessed using within-subject analysis of variance and statistical parametric mapping. Additionally, a comparison to the [¹⁸F]FDG PET of an age-matched control group was performed. Exploratory correlation analyses were conducted with operationalized and parametric clinical data.

RESULTS

Of the immediately accessible parametric tremor data (i.e. ON or OFF_0h) only the rebound (i.e. OFF_0h) frequency of postural tremor showed possible correlations with signs of ataxia at ON. Regional glucose metabolism was significantly increased bilaterally in the thalamus and dentate nucleus in ON compared to OFF_72h. No differences in regional glucose metabolism were found in patients in ON and OFF_72h compared with the healthy controls.

CONCLUSIONS

Rebound frequency of postural tremor seems to be a good diagnostic marker for delayed therapy escape. Regional glucose metabolism suggests that this phenomenon may be associated with increased metabolic activity in the thalamus and dentate nucleus possibly due to antidromic stimulation effects. We see reasons to interpret the delayed therapy escape phenomenon as being related to long term and chronic DBS.

A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor

Background

Ordinal scales based on qualitative observation are the mainstay in the clinical assessment of tremor, but are limited by inter-rater reliability, measurement precision, range, and ceiling effects. Quantitative tremor evaluation is well-developed in research, but clinical application has lagged, in part due to cumbersome mathematical application and lack of established standards.

Objectives

To develop a novel method for evaluating tremor that integrates a standardized clinical exam, wrist-watch accelerometers, and a software framework for data analysis that does not require advanced mathematical or computing skills. The utility of the method was tested in a sequential cohort of patients with predominant postural and action tremor presenting to a specialized surgical clinic with the presumptive diagnosis of Essential Tremor (ET).

Methods

Wristwatch accelerometry was integrated with a standardized clinical exam. A MATLAB application was developed for automated data analysis and graphical representation of tremor. Measures from the power spectrum of acceleration of tremor in different upper limb postures were derived in 25 consecutive patients. The linear results from accelerometry were correlated with the commonly used non-linear Clinical Rating Scale for Tremor (CRST).

Results

The acceleration power spectrum was reliably produced in all consecutive patients. Tremor frequency was stable in different postures and across patients. Both total and peak power of acceleration during postural conditions correlated well with the CRST. The standardized clinical examination with integrated accelerometry measures was therefore effective at characterizing tremor in a population with predominant postural and action tremor. The protocol is also illustrated on repeated measures in an ET patient who underwent Magnetic Resonance-Guided Focused Ultrasound thalamotomy.

Conclusion

Quantitative assessment of tremor as a continuous variable using wristwatch accelerometry is readily applicable as a clinical tool when integrated with a standardized clinical exam and a user-friendly software framework for analysis. The method is validated for patients with predominant postural and action tremor, and can be adopted for characterizing tremor of different etiologies with dissemination in a wide variety of clinical and research contexts in ageing populations.

Which one is the superior target? A comparison and pooled analysis between posterior subthalamic area and ventral intermediate nucleus deep brain stimulation for essential tremor

Background/Aims

The efficacy and safety of posterior subthalamic area (PSA) and ventral intermediate nucleus (VIM) deep brain stimulation (DBS) in the treatment of essential tremor (ET) have not been compared in large‐scale studies. We conducted a secondary analysis to identify the superior target of ET‐DBS treatment.

Methods

PubMed, Embase, Cochrane Library, and Google Scholar were searched for relevant studies before September 2021. The tremor‐suppression efficacy and rate of stimulation‐related complications (SRCR) after PSA‐DBS and VIM‐DBS treating ET were quantitatively compared. Secondary outcomes, including tremor subitem scores and quality of life results, were also analyzed. Subgroup analyses were further conducted to stratify by follow‐up (FU) periods and stimulation lateralities. This study was registered in Open Science Framework (DOI: 10.17605/OSF.IO/7VJQ8).

Results

A total of 23 studies including 122 PSA‐DBS patients and 326 VIM‐DBS patients were analyzed. The average follow‐up time was 12.81 and 14.66 months, respectively. For the percentage improvement of total tremor rating scale (TRS) scores, PSA‐DBS was significantly higher, when compared to VIM‐DBS in the sensitivity analysis (p = 0.030) and main analysis (p = 0.043). The SRCR after VIM‐DBS was higher than that of PSA‐DBS (p = 0.022), and bilateral PSA‐DBS was significantly superior to both bilateral and unilateral VIM‐DBS (p = 0.001).

Conclusions

This study provided level IIIa evidence that PSA‐DBS was more effective and safer for ET than VIM‐DBS in 12–24 months, although both PSA‐DBS and VIM‐DBS were effective in suppressing tremor in ET patients. Further prospective large‐scale randomized clinical trials are warranted in the future.

Diving into the Subcortex: The Potential of Chronic Subcortical Sensing for Unravelling Basal Ganglia Function and Optimization of Deep Brain STIMULATION

Subcortical structures are a relative neurophysiological 'terra incognita' owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing -i.e., adaptive deep brain stimulation (aDBS)- are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.