Thalamocortical network activity enables chronic tic detection in humans with Tourette syndrome

Responsive deep brain stimulation for the treatment of Tourette syndrome

To report the results of 'responsive' deep brain stimulation (DBS) for Tourette syndrome (TS) in a National Institutes of Health funded experimental cohort. The use of 'brain derived physiology' as a method to trigger DBS devices to deliver trains of electrical stimulation is a proposed approach to address the paroxysmal motor and vocal tic symptoms which appear as part of TS. Ten subjects underwent bilateral staged DBS surgery and each was implanted with bilateral centromedian thalamic (CM) region DBS leads and bilateral M1 region cortical strips. A series of identical experiments and data collections were conducted on three groups of consecutively recruited subjects. Group 1 (n = 2) underwent acute responsive DBS using deep and superficial leads. Group 2 (n = 4) underwent chronic responsive DBS using deep and superficial leads. Group 3 (n = 4) underwent responsive DBS using only the deep leads. The primary outcome measure for each of the 8 subjects with chronic responsive DBS was calculated as the pre-operative baseline Yale Global Tic Severity Scale (YGTSS) motor subscore compared to the 6 month embedded responsive DBS setting. A responder for the study was defined as any subject manifesting a ≥ 30 points improvement on the YGTSS motor subscale. The videotaped Modified Rush Tic Rating Scale (MRVTRS) was a secondary outcome. Outcomes were collected at 6 months across three different device states: no stimulation, conventional open-loop stimulation, and embedded responsive stimulation. The experience programming each of the groups and the methods applied for programming were captured. There were 10 medication refractory TS subjects enrolled in the study (5 male and 5 female) and 4/8 (50%) in the chronic responsive eligible cohort met the primary outcome manifesting a reduction of the YGTSS motor scale of ≥ 30% when on responsive DBS settings. Proof of concept for the use of responsive stimulation was observed in all three groups (acute responsive, cortically triggered and deep DBS leads only). The responsive approach was safe and well tolerated. TS power spectral changes associated with tics occurred consistently in the low frequency 2-10 Hz delta-theta-low alpha oscillation range. The study highlighted the variety of programming strategies which were employed to achieve responsive DBS and those used to overcome stimulation induced artifacts. Proof of concept was also established for a single DBS lead triggering bi-hemispheric delivery of therapeutic stimulation. Responsive DBS was applied to treat TS related motor and vocal tics through the application of three different experimental paradigms. The approach was safe and effective in a subset of individuals. The use of different devices in this study was not aimed at making between device comparisons, but rather, the study was adapted to the current state of the art in technology. Overall, four of the chronic responsive eligible subjects met the primary outcome variable for clinical effectiveness. Cortical physiology was used to trigger responsive DBS when therapy was limited by stimulation induced artifacts.

From basic sciences and engineering to epileptology: A translational approach

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.

Tourette syndrome: clinical features, pathophysiology, and treatment

Tourette syndrome is a chronic neurodevelopmental disorder characterised by motor and phonic tics that can substantially diminish the quality of life of affected individuals. Evaluating and treating Tourette syndrome is complex, in part due to the heterogeneity of symptoms and comorbidities between individuals. The underlying pathophysiology of Tourette syndrome is not fully understood, but recent research in the past 5 years has brought new insights into the genetic variations and the alterations in neurophysiology and brain networks contributing to its pathogenesis. Treatment options for Tourette syndrome are expanding with novel pharmacological therapies and increased use of deep brain stimulation for patients with symptoms that are refractory to pharmacological or behavioural treatments. Potential predictors of patient responses to therapies for Tourette syndrome, such as specific networks modulated during deep brain stimulation, can guide clinical decisions. Multicentre data sharing initiatives have enabled several advances in our understanding of the genetics and pathophysiology of Tourette syndrome and will be crucial for future large-scale research and in refining effective treatments.

Theta Activity Dynamics during Embedded Response Plan Processing in Tourette Syndrome

Gilles de la Tourette syndrome (GTS) is a neuropsychiatric disorder. Because motor signs are the defining feature of GTS, addressing the neurophysiology of motor processes is central to understanding GTS. The integration of voluntary motor processes is subject to so-called "binding problems", i.e., how different aspects of an action are integrated. This was conceptualized in the theory of event coding, in which 'action files' accomplish the integration of motor features. We examined the functional neuroanatomical architecture of EEG theta band activity related to action file processing in GTS patients and healthy controls. Whereas, in keeping with previous data, behavioral performance during action file processing did not differ between GTS and controls, underlying patterns of neural activity were profoundly different. Superior parietal regions (BA7) were predominantly engaged in healthy controls, but superior frontal regions (BA9, BA10) in GTS indicated that the processing of different motor feature codes was central for action file processing in healthy controls, whereas episodic processing was more relevant in GTS. The data suggests a cascade of cognitive branching in fronto-polar areas followed by episodic processing in superior frontal regions in GTS. Patients with GTS accomplish the integration of motor plans via qualitatively different neurophysiological processes.

Myoclonus and other jerky movement disorders

Myoclonus and other jerky movements form a large heterogeneous group of disorders. Clinical neurophysiology studies can have an important contribution to support diagnosis but also to gain insight in the pathophysiology of different kind of jerks. This review focuses on myoclonus, tics, startle disorders, restless legs syndrome, and periodic leg movements during sleep. Myoclonus is defined as brief, shock-like movements, and subtypes can be classified based the anatomical origin. Both the clinical phenotype and the neurophysiological tests support this classification: cortical, cortical-subcortical, subcortical/non-segmental, segmental, peripheral, and functional jerks. The most important techniques used are polymyography and the combination of electromyography-electroencephalography focused on jerk-locked back-averaging, cortico-muscular coherence, and the Bereitschaftspotential. Clinically, the differential diagnosis of myoclonus includes tics, and this diagnosis is mainly based on the history with premonitory urges and the ability to suppress the tic. Electrophysiological tests are mainly applied in a research setting and include the Bereitschaftspotential, local field potentials, transcranial magnetic stimulation, and pre-pulse inhibition. Jerks due to a startling stimulus form the group of startle syndromes. This group includes disorders with an exaggerated startle reflex, such as hyperekplexia and stiff person syndrome, but also neuropsychiatric and stimulus-induced disorders. For these disorders polymyography combined with a startling stimulus can be useful to determine the pattern of muscle activation and thus the diagnosis. Assessment of symptoms in restless legs syndrome and periodic leg movements during sleep can be performed with different validated scoring criteria with the help of electromyography.

Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording

Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads.

Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S^® (Medtronic) or Percept PC^°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects.

Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network.

Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.

European clinical guidelines for Tourette syndrome and other tic disorders-version 2.0. Part IV: deep brain stimulation

In 2011 the European Society for the Study of Tourette Syndrome (ESSTS) published its first European clinical guidelines for the treatment of Tourette Syndrome (TS) with part IV on deep brain stimulation (DBS). Here, we present a revised version of these guidelines with updated recommendations based on the current literature covering the last decade as well as a survey among ESSTS experts. Currently, data from the International Tourette DBS Registry and Database, two meta-analyses, and eight randomized controlled trials (RCTs) are available. Interpretation of outcomes is limited by small sample sizes and short follow-up periods. Compared to open uncontrolled case studies, RCTs report less favorable outcomes with conflicting results. This could be related to several different aspects including methodological issues, but also substantial placebo effects. These guidelines, therefore, not only present currently available data from open and controlled studies, but also include expert knowledge. Although the overall database has increased in size since 2011, definite conclusions regarding the efficacy and tolerability of DBS in TS are still open to debate. Therefore, we continue to consider DBS for TS as an experimental treatment that should be used only in carefully selected, severely affected and otherwise treatment-resistant patients.

Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4-8 Hz) and enhanced beta (12-30 Hz) and gamma (30-100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.

Increased scale-free and aperiodic neural activity during sensorimotor integration-a novel facet in Tourette syndrome

Tourette syndrome is a common neurodevelopmental disorder defined by multiple motor and phonic tics. Tics in Tourette syndrome resemble spontaneously occurring movements in healthy controls and are therefore sometimes difficult to distinguish from these. Tics may in fact be mis-interpreted as a meaningful action, i.e. a signal with social content, whereas they lack such information and could be conceived a surplus of action or 'motor noise'. These and other considerations have led to a 'neural noise account' of Tourette syndrome suggesting that the processing of neural noise and adaptation of the signal-to-noise ratio during information processing is relevant for the understanding of Tourette syndrome. So far, there is no direct evidence for this. Here, we tested the 'neural noise account' examining 1/f noise, also called scale-free neural activity as well as aperiodic activity, in n = 74 children, adolescents and adults with Tourette syndrome and n = 74 healthy controls during task performance using EEG data recorded during a sensorimotor integration task. In keeping with results of a previous study in adults with Tourette syndrome, behavioural data confirmed that sensorimotor integration was also stronger in this larger Tourette syndrome cohort underscoring the relevance of perceptual-action processes in this disorder. More importantly, we show that 1/f noise and aperiodic activity during sensorimotor processing is increased in patients with Tourette syndrome supporting the 'neural noise account'. This implies that asynchronous/aperiodic neural activity during sensorimotor integration is stronger in patients with Tourette syndrome compared to healthy controls, which is probably related to abnormalities of GABAergic and dopaminergic transmission in these patients. Differences in 1/f noise and aperiodic activity between patients with Tourette syndrome and healthy controls were driven by high-frequency oscillations and not lower-frequency activity currently discussed to be important in the pathophysiology of tics. This and the fact that Bayesian statistics showed that there is evidence for the absence of a correlation between neural noise and clinical measures of tics, suggest that increased 1/f noise and aperiodic activity are not directly related to tics but rather represents a novel facet of Tourette syndrome.

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.

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

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.

Distinct Brain-Oscillatory Neuroanatomical Architecture of Perception-Action Integration in Adolescents With Tourette Syndrome

Background

Gilles de la Tourette Syndrome (GTS) is a neurodevelopmental disorder with a peak of symptom severity around late childhood and early adolescence. Previous findings in adult GTS suggest that changes in perception-action integration, as conceptualized in the theory of event coding framework, are central for the understanding of GTS. However, the neural mechanisms underlying these processes in adolescence are elusive.

Methods

A total of 59 children/adolescents aged 9 to 18 years (n = 32 with GTS, n = 27 typically developing youths) were examined using a perception-action integration task (event file task) derived from the theory of event coding. Event-related electroencephalogram recordings (theta and beta band activity) were analyzed using electroencephalogram–beamforming methods.

Results

Behavioral data showed robust event file binding effects in both groups without group differences. Neurophysiological data showed that theta and beta band activity were involved in event file integration in both groups. However, the functional neuroanatomical organization was markedly different for theta band activity between the groups. The typically developing group mainly relied on superior frontal regions, whereas the GTS group engaged parietal and inferior frontal regions. A more consistent functional neuroanatomical activation pattern was observed for the beta band, engaging inferior parietal and temporal regions in both groups.

Conclusions

Perception-action integration processes lag behind in persisting GTS but not in the GTS population as a whole, underscoring differences in developmental trajectories and the importance of longitudinal investigations for the understanding of GTS. The findings corroborate known differences in the functional/structural brain organization in GTS and suggest an important role of theta band activity in these patients.

Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor

Deep brain stimulation (DBS) is an approved therapy for the treatment of medically refractory and severe movement disorders. However, most existing neurostimulators can only apply continuous stimulation [open-loop DBS (OL-DBS)], ignoring patient behavior and environmental factors, which consequently leads to an inefficient therapy, thus limiting the therapeutic window. Here, we established the feasibility of a self-adjusting therapeutic DBS [closed-loop DBS (CL-DBS)], fully embedded in a chronic investigational neurostimulator (Activa PC + S), for three patients affected by essential tremor (ET) enrolled in a longitudinal (6 months) within-subject crossover protocol (DBS OFF, OL-DBS, and CL-DBS). Most patients with ET experience involuntary limb tremor during goal-directed movements, but not during rest. Hence, the proposed CL-DBS paradigm explored the efficacy of modulating the stimulation amplitude based on patient-specific motor behavior, suppressing the pathological tremor on-demand based on a cortical electrode detecting upper limb motor activity. Here, we demonstrated how the proposed stimulation paradigm was able to achieve clinical efficacy and tremor suppression comparable with OL-DBS in a range of movements (cup reaching, proximal and distal posture, water pouring, and writing) while having a consistent reduction in energy delivery. The proposed paradigm is an important step toward a behaviorally modulated fully embedded DBS system, capable of delivering stimulation only when needed, and potentially mitigating pitfalls of OL-DBS, such as DBS-induced side effects and premature device replacement.

Tic Detection in Tourette Syndrome Patients Based on Unsupervised Visual Feature Learning

A clinical diagnosis of tic disorder involves several complex processes, among which observation and evaluation of patient behavior usually require considerable time and effective cooperation between the doctor and the patient. The existing assessment scale has been simplified into qualitative and quantitative assessments of movements and sound twitches over a certain period, but it must still be completed manually. Therefore, we attempt to find an automatic method for detecting tic movement to assist in diagnosis and evaluation. Based on real clinical data, we propose a deep learning architecture that combines both unsupervised and supervised learning methods and learns features from videos for tic motion detection. The model is trained using leave-one-subject-out cross-validation for both binary and multiclass classification tasks. For these tasks, the model reaches average recognition precisions of 86.33% and 86.26% and recalls of 77.07% and 78.78%, respectively. The visualization of features learned from the unsupervised stage indicates the distinguishability of the two types of tics and the nontic. Further evaluation results suggest its potential clinical application for auxiliary diagnoses and evaluations of treatment effects.

A neural noise account of Gilles de la Tourette syndrome

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A neural noise account on Tourette syndrome is conceptualized.

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We outline how neurophysiological methods can be used to test this account.

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The neural noise account may lead to novel treatment options.

Tics, often preceded by premonitory urges, are the clinical hallmark of Tourette syndrome. They resemble spontaneous movements, but are exaggerated, repetitive and appear misplaced in a given communication context. Given that tics often go unnoticed, it has been suggested that they represent a surplus of action, or motor noise. In this conceptual position paper, we propose that tics and urges, but also patterns of the cognitive profile in Tourette syndrome might be explained by the principle of processing of neural noise and adaptation to it during information processing. We review evidence for this notion in the light of Tourette pathophysiology and outline why neurophysiological and imaging approaches are central to examine a possibly novel view on Tourette syndrome. We discuss how neurophysiological data at multiple levels of inspections, i.e., from local field potentials using intra-cranial recording to scalp-measured EEG data, in combination with imaging approaches, can be used to examine the neural noise account in Tourette syndrome. We outline what signal processing methods may be suitable for that. We argue that, as a starting point, the analysis of 1/f neural noise or scale-free activity may be suitable to investigate the role of neural noise and its adaptation during information processing in Tourette syndrome. We outline, how the neural noise perspective, if substantiated by further neurophysiological studies and re-analyses of existing data, may pave the way to novel interventions directly targeting neural noise levels and patterns in Tourette syndrome.

Thalamic Local Field Potentials Are Related to Long-Term DBS Effects in Tourette Syndrome

Background: Local field potential (LFP) recordings helped to clarify the pathophysiology of Tourette syndrome (TS) and to define new strategies for deep brain stimulation (DBS) treatment for refractory TS, based on the delivery of stimulation in accordance with changes in the electrical activity of the DBS target area. However, there is little evidence on the relationship between LFP pattern and DBS outcomes in TS.

Objective: To investigate the relationship between LFP oscillations and DBS effects on tics and on obsessive compulsive behavior (OCB) comorbidities.

Methods: We retrospectively analyzed clinical data and LFP recordings from 17 patients treated with DBS of the centromedian-parafascicular/ventralis oralis (CM-Pf/VO) complex, and followed for more several years after DBS in the treating center. In these patients, LFPs were recorded either in the acute setting (3–5 days after DBS electrode implant) or in the chronic setting (during impulse generator replacement surgery). LFP oscillations were correlated with the Yale Global Tic Severity Scale (YGTSS) and the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS) collected at baseline (before DBS surgery), 1 year after DBS, and at the last follow-up available.

Results: We found that, at baseline, in the acute setting, the power of the oscillations included in the 5–15-Hz band, previously identified as TS biomarker, is correlated with the pathophysiology of tics, being significantly correlated with total YGTSS before DBS (Spearman's ρ = 0.701, p = 0.011). The power in the 5–15-Hz band was also correlated with the improvement in Y-BOCS after 1 year of DBS (Spearman's ρ = −0.587, p = 0.045), thus suggesting a relationship with the DBS effects on OCB comorbidities.

Conclusions: Our observations confirm that the low-frequency (5–15-Hz) band is a significant biomarker of TS, being related to the severity of tics and, also to the long-term response on OCBs. This represents a step toward both the understanding of the mechanisms underlying DBS effects in TS and the development of adaptive DBS strategies.

Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome

Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate 'reverse' tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.

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.

Researcher Perspectives on Ethical Considerations in Adaptive Deep Brain Stimulation Trials

Interest and investment in closed-loop or adaptive deep brain stimulation (aDBS) systems have quickly expanded due to this neurotechnology's potential to more safely and effectively treat refractory movement and psychiatric disorders compared to conventional DBS. A large neuroethics literature outlines potential ethical concerns about conventional DBS and aDBS systems. Few studies, however, have examined stakeholder perspectives about ethical issues in aDBS research and other next-generation DBS devices. To help fill this gap, we conducted semi-structured interviews with researchers involved in aDBS trials (n = 23) to gain insight into the most pressing ethical questions in aDBS research and any concerns about specific features of aDBS devices, including devices' ability to measure brain activity, automatically adjust stimulation, and store neural data. Using thematic content analysis, we identified 8 central themes in researcher responses. The need to measure and store neural data for aDBS raised concerns among researchers about data privacy and security issues (noted by 91% of researchers), including the avoidance of unintended or unwanted third-party access to data. Researchers reflected on the risks and safety (83%) of aDBS due to the experimental nature of automatically modulating then observing stimulation effects outside a controlled clinical setting and in relation to need for surgical battery changes. Researchers also stressed the importance of ensuring informed consent and adequate patient understanding (74%). Concerns related to automaticity and device programming (65%) were discussed, including current uncertainties about biomarker validity. Additionally, researchers discussed the potential impacts of automatic stimulation on patients' autonomy and control over stimulation (57%). Lastly, researchers discussed concerns related to patient selection (defining criteria for candidacy) (39%), challenges of ensuring post-trial access to care and device maintenance (39%), and potential effects on personality and identity (30%). To help address researcher concerns, we discuss the need to minimize cybersecurity vulnerabilities, advance biomarker validity, promote the balance of device control between patients and clinicians, and enhance ongoing informed consent. The findings from this study will help inform policies that will maximize the benefits and minimize potential harms of aDBS and other next-generation DBS devices.

Simultaneous cortical and subcortical recordings in humans with movement disorders: Acute and chronic paradigms

Invasive basal ganglia recordings in humans have significantly advanced our understanding of the neurophysiology of movement disorders. A recent technical advance has been the addition of electrocorticography to basal ganglia recording, for evaluating distributed motor networks. Here we review the rationale, results, and ethics of this multisite recording technique in movement disorders, as well as its application in chronic recording paradigms utilizing implantable neural interfaces that include a sensing function.

Brain functional connectivity in chronic tic disorders and Gilles de la Tourette syndrome

The pathophysiology of chronic tic disorder (cTD) and Gilles de la Tourette syndrome (GTS) is characterized by the dysfunction of both motor and non - motor cortico - striatal - thalamo - cortical (CSTC) circuitries, which leads to tic release and comorbids. A role of fronto - parietal network (FPN) connectivity breakdown has been postulated for tic pathogenesis, given that the FPN entertain connections with limbic, paralimbic, and CSTC networks. Our study was aimed at characterizing the FPN functional connectivity in cTD and GTS in order to assess the role of its deterioration in tic severity and the degree of comorbids. We recorded scalp EEG during resting state in patients with cTD and GTS. The eLORETA current source densities were analyzed, and the lagged phase synchronization (LPS) was calculated to estimate nonlinear functional connectivity between cortical areas. We found that the FPN functional connectivity in delta band was more detrimental in more severe GTS patients. Also, the sensorimotor functional connectivity in beta2 band was stronger in more severe cTD and GTS patients. FPN functional connectivity deterioration correlated with comorbids presence and severity in patients with GTS. Our data suggest that a FPN disconnection may contribute to the motoric symptomatology and comorbid severity in GTS, whereas sensorimotor disconnection may contribute to tic severity in cTD and GTS. Although preliminary, our study points out a differently disturbed brain connectivity between patients with cTD and GTS. This may serve as diagnostic marker and potentially interesting base to develop pharmacological and noninvasive neuromodulation trials aimed at reducing tic symptomatology.

Lead Repositioning Guided by Both Physiology and Atlas Based Targeting in Tourette Deep Brain Stimulation

Background:

The centromedian (CM) region of the thalamus is a common target for deep brain stimulation (DBS) treatment for Tourette Syndrome (TS). However, there are currently no standard microelectrode recording or macrostimulation methods to differentiate CM thalamus from other nearby structures and nuclei.

Case Report:

Here we present a case of failed conventional stereotactic targeting in TS DBS. Postoperative local field potential recordings (LFPs) showed features including beta power desynchronization during voluntary movement and thalamo-cortical phase amplitude coupling at rest. These findings suggested that the DBS lead was suboptimally placed in the ventral intermediate (VIM) nucleus of the thalamus rather than the intended CM region. Due to a lack of clinical improvement in tic severity scales three months following the initial surgery, the patient underwent lead revision surgery. Slight repositioning of the DBS leads resulted in a remarkably different clinical outcome. Afterwards, LFPs revealed less beta desynchronization and disappearance of the thalamo-cortical phase amplitude coupling. Follow-up clinical visits documented improvement of the patient’s global tic scores.

Discussion:

This case provides preliminary evidence that combining physiology with atlas based targeting may possibly enhance outcomes in some cases of Tourette DBS. A larger prospective study will be required to confirm these findings.

Highlight:

This report demonstrates a case of failed centromedian nucleus region deep brain stimulation (DBS). We observed suboptimal tic improvement several months following DBS surgery and subsequent lead revision improved the outcome. The neurophysiology provided an important clue suggesting the possibility of suboptimally placed DBS leads. Repeat LFPs during lead revision revealed less beta desynchronization and disappearance of the thalamo-cortical phase amplitude coupling. There was improvement in tic outcome following slight repositioning during bilateral DBS lead revision. This case provides preliminary evidence supporting the use of physiology to augment the atlas based targeting of Tourette DBS cases.

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery

The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

Differentiating tic electrophysiology from voluntary movement in the human thalamocortical circuit

OBJECTIVES

Tourette syndrome is a neurodevelopmental disorder commonly associated with involuntary movements, or tics. We currently lack an ideal animal model for Tourette syndrome. In humans, clinical manifestation of tics cannot be captured via functional imaging due to motion artefacts and limited temporal resolution, and electrophysiological studies have been limited to the intraoperative environment. The goal of this study was to identify electrophysiological signals in the centromedian (CM) thalamic nucleus and primary motor (M1) cortex that differentiate tics from voluntary movements.

METHODS

The data were collected as part of a larger National Institutes of Health-sponsored clinical trial. Four participants (two males, two females) underwent monthly clinical visits for collection of physiology for a total of 6 months. Participants were implanted with bilateral CM thalamic macroelectrodes and M1 subdural electrodes that were connected to two neurostimulators, both with sensing capabilities. MRI scans were performed preoperatively and CT scans postoperatively for localisation of electrodes. Electrophysiological recordings were collected at each visit from both the cortical and subcortical implants.

RESULTS

Recordings collected from the CM thalamic nucleus revealed a low-frequency power (3-10 Hz) increase that was time-locked to the onset of involuntary tics but was not present during voluntary movements. Cortical recordings revealed beta power decrease in M1 that was present during tics and voluntary movements.

CONCLUSION

We conclude that a human physiological signal was detected from the CM thalamus that differentiated tic from voluntary movement, and this physiological feature could potentially guide the development of neuromodulation therapies for Tourette syndrome that could use a closed-loop-based approach.

Characteristics of globus pallidus internus local field potentials in generalized dystonia patients with TWNK mutation

OBJECTIVE

We focused on a rare gene mutation causing dystonia in two siblings who received globus pallidus internus deep brain stimulation (GPi-DBS). The aim was to characterize the relationship between neuronal activity patterns and clinical syndromes.

METHODS

Whole exome sequencing was applied to identify the TWNK (previous symbol C10orf2) mutation; Two siblings with TWNK mutation presented as generalized dystonia with rigidity and bradykinesia; four other sporadic generalized dystonia patients underwent GPi-DBS and local field potentials (LFPs) were recorded. Oscillatory activities were illustrated with power spectra and temporal dynamics measured by the Lempel-Ziv complexity (LZC).

RESULTS

Normalized power spectra of GPi LFPs differed between patients with TWNK mutation and dystonia over the low beta bands. Patients with TWNK mutation had higher low beta power (15-27 Hz, unpaired t-test, corrected P < 0.0022) and lower LZC (15-27 Hz, unpaired t-test, P < 0.01) than other patients with generalized dystonia. On the other hand, the TWNK mutation patients showed decreased low frequency and beta oscillation in the GPi after DBS, as well as improved movement performance.

CONCLUSION

The LFPs were different in TWNK mutation dystonia siblings than other patients with generalized dystonia, which indicate the abnormal LFPs were related to symptoms rather than specific disease. In addition, the inhibited effect on oscillations also provided a potential evidence for DBS treatment on rare movement disorders.

SIGNIFICANCE

This study could potentially aid in the future development of adaptive DBS via rare disease LFPs comparison.

Deep Brain Stimulation for Tourette Syndrome: Potential Role in the Pediatric Population

Tourette syndrome (TS) is a complex neuropsychiatric disorder. Despite an expected natural history of improvement with age, many individuals continue to have severe tics and remain refractory to the current best pharmacologic and nonpharmacologic treatments. Deep brain stimulation (DBS) has emerged as a potential treatment option. This article reviews the published reports on the use of deep brain stimulation in Tourette syndrome revealing that 2 anatomical targets have been most commonly used: the centromedian thalamus and the globus pallidus internus. The evidence supports a significant clinical improvement of tics with deep brain stimulation, though the data are limited by the small number of patients and variable methodology employed. To bridge these limitations, the international Tourette syndrome deep brain stimulation database and registry have been created, fostering collaboration among multiple centers from 10 countries. By standardizing data collection, the database and registry are providing valuable insights into deep brain stimulation for Tourette syndrome. In conclusion, deep brain stimulation offers significant promise for the management of tics.

Deep brain stimulation for Tourette's syndrome

Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. TS usually co-occurs with one or multiple psychiatric disorders. Although behavioral and pharmacological treatments for TS are available, some patients do not respond to the available treatments. For these patients, TS is a severe, chronic, and disabling disorder. In recent years, deep brain stimulation (DBS) of basal ganglia-thalamocortical networks has emerged as a promising intervention for refractory TS with or without psychiatric comorbidities. Three major challenges need to be addressed to move the field of DBS treatment for TS forward: (1) patient and DBS target selection, (2) ethical concerns with treating pediatric patients, and (3) DBS treatment optimization and improvement of individual patient outcomes (motor and phonic tics, as well as functioning and quality of life). The Tourette Association of America and the American Academy of Neurology have recently released their recommendations regarding surgical treatment for refractory TS. Here, we describe the challenges, advancements, and promises of the use of DBS in the treatment of TS. We summarize the results of clinical studies and discuss the ethical issues involved in treating pediatric patients. Our aim is to provide a better understanding of the feasibility, safety, selection process, and clinical effectiveness of DBS treatment for select cases of severe and medically intractable TS.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Deep brain stimulation modulates pallidal and subthalamic neural oscillations in Tourette's syndrome

INTRODUCTION

Previous studies found subthalamic nucleus deep brain stimulation (STN-DBS) has clinical effect on Parkinson's disease, dystonia, and obsessive compulsive disorder. It is noteworthy that only a few studies report the STN-DBS for Tourette's syndrome (TS). Globus pallidus interna (GPi)-DBS is the one of the most common targets for TS. So, this paper aims to investigate the neural oscillations in STN and GPi as well as the DBS effect between these two targets in same patients.

METHODS

The local field potentials (LFPs) were simultaneously recorded from the bilateral GPi and STN in four patients with TS. The LFPs were decomposed into neural oscillations, and the frequency and time-frequency characteristics of the neural oscillations were analyzed across the conditions of resting, poststimulation, and movement.

RESULTS

No difference of resting LFP was found between the two targets. The poststimulation period spectral power revealed the high beta and gamma oscillations were recovered after GPi-DBS but remained attenuated after STN-DBS. The STN beta oscillation has fewer changes during tics than voluntary movement, and the gamma oscillation was elevated when the tics appeared.

CONCLUSION

The high beta and gamma oscillations in GPi restored after GPi-DBS, but not STN-DBS. High beta and gamma oscillations may have physiological function in resisting tics in TS. The cortex compensation effect might be interfered by the STN-DBS due to the influence on the hyper-direct pathway but not GPi-DBS.

Image-based analysis and long-term clinical outcomes of deep brain stimulation for Tourette syndrome: a multisite study

BACKGROUND

Deep brain stimulation (DBS) can be an effective therapy for tics and comorbidities in select cases of severe, treatment-refractory Tourette syndrome (TS). Clinical responses remain variable across patients, which may be attributed to differences in the location of the neuroanatomical regions being stimulated. We evaluated active contact locations and regions of stimulation across a large cohort of patients with TS in an effort to guide future targeting.

METHODS

We collected retrospective clinical data and imaging from 13 international sites on 123 patients. We assessed the effects of DBS over time in 110 patients who were implanted in the centromedial (CM) thalamus (n=51), globus pallidus internus (GPi) (n=47), nucleus accumbens/anterior limb of the internal capsule (n=4) or a combination of targets (n=8). Contact locations (n=70 patients) and volumes of tissue activated (n=63 patients) were coregistered to create probabilistic stimulation atlases.

RESULTS

Tics and obsessive-compulsive behaviour (OCB) significantly improved over time (p<0.01), and there were no significant differences across brain targets (p>0.05). The median time was 13 months to reach a 40% improvement in tics, and there were no significant differences across targets (p=0.84), presence of OCB (p=0.09) or age at implantation (p=0.08). Active contacts were generally clustered near the target nuclei, with some variability that may reflect differences in targeting protocols, lead models and contact configurations. There were regions within and surrounding GPi and CM thalamus that improved tics for some patients but were ineffective for others. Regions within, superior or medial to GPi were associated with a greater improvement in OCB than regions inferior to GPi.

CONCLUSION

The results collectively indicate that DBS may improve tics and OCB, the effects may develop over several months, and stimulation locations relative to structural anatomy alone may not predict response. This study was the first to visualise and evaluate the regions of stimulation across a large cohort of patients with TS to generate new hypotheses about potential targets for improving tics and comorbidities.

Precision electronic medicine in the brain

Periodically throughout history developments from adjacent fields of science and technology reach a tipping point where together they produce unparalleled advances, such as the Allen Brain Atlas and the Human Genome Project. Today, research focused at the interface between the nervous system and electronics is not only leading to advances in fundamental neuroscience, but also unlocking the potential of implants capable of cellular-level therapeutic targeting. Ultimately, these personalized electronic therapies will provide new treatment modalities for neurodegenerative and neuropsychiatric illness; powerful control of prosthetics for restorative function in degenerative diseases, trauma and amputation; and even augmentation of human cognition. Overall, we believe that emerging advances in tissue-like electronics will enable minimally invasive devices capable of establishing a stable long-term cellular neural interface and providing long-term treatment for chronic neurological conditions.

Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis

•

Stability of recorded neural signals is crucial for the clinical viability of implantable brain-computer interfaces (BCIs).

•

A fully implantable BCI offers a long-term robust and durable control signal in a person with amyotrophic lateral sclerosis.

•

Increasing home use of the electrocorticography (ECoG)-BCI demonstrates user adoption of the BCI for communication.

Objective

We investigated the long-term functional stability and home use of a fully implanted electrocorticography (ECoG)-based brain-computer interface (BCI) for communication by an individual with late-stage Amyotrophic Lateral Sclerosis (ALS).

Methods

Data recorded from the cortical surface of the motor and prefrontal cortex with an implanted brain-computer interface device was evaluated for 36 months after implantation of the system in an individual with late-stage ALS. In addition, electrode impedance and BCI control accuracy were assessed. Key measures included frequency of use of the system for communication, user and system performance, and electrical signal characteristics.

Results

User performance was high consistently over the three years. Power in the high frequency band, used for the control signal, declined slowly in the motor cortex, but control over the signal remained unaffected by time. Impedance increased until month 5, and then remained constant. Frequency of home use increased steadily, indicating adoption of the system by the user.

Conclusions

The implanted brain-computer interface proves to be robust in an individual with late-stage ALS, given stable performance and control signal for over 36 months.

Significance

These findings are relevant for the future of implantable brain-computer interfaces along with other brain-sensing technologies, such as responsive neurostimulation.

Applying a Sensing-Enabled System for Ensuring Safe Anterior Cingulate Deep Brain Stimulation for Pain

Deep brain stimulation (DBS) of the anterior cingulate cortex (ACC) was offered to chronic pain patients who had exhausted medical and surgical options. However, several patients developed recurrent seizures. This work was conducted to assess the effect of ACC stimulation on the brain activity and to guide safe DBS programming. A sensing-enabled neurostimulator (Activa PC + S) allowing wireless recording through the stimulating electrodes was chronically implanted in three patients. Stimulation patterns with different amplitude levels and variable ramping rates were tested to investigate whether these patterns could provide pain relief without triggering after-discharges (ADs) within local field potentials (LFPs) recorded in the ACC. In the absence of ramping, AD activity was detected following stimulation at amplitude levels below those used in chronic therapy. Adjustment of stimulus cycling patterns, by slowly ramping on/off (8-s ramp duration), was able to prevent ADs at higher amplitude levels while maintaining effective pain relief. The absence of AD activity confirmed from the implant was correlated with the absence of clinical seizures. We propose that AD activity in the ACC could be a biomarker for the likelihood of seizures in these patients, and the application of sensing-enabled techniques has the potential to advance safer brain stimulation therapies, especially in novel targets.

Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples

This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain–machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.

A review of basal ganglia circuits and physiology: Application to deep brain stimulation

INTRODUCTION

Drawing on the seminal work of DeLong, Albin, and Young, we have now entered an era of basal ganglia neuromodulation. Understanding, re-evaluating, and leveraging the lessons learned from neuromodulation will be crucial to facilitate an increased and improved application of neuromodulation in human disease.

METHODS

We will focus on deep brain stimulation (DBS) - the most common form of basal ganglia neuromodulation - however, similar principles can apply to other neuromodulation modalities. We start with a brief review of DBS for Parkinson's disease, essential tremor, dystonia, and Tourette syndrome. We then review hallmark studies on basal ganglia circuits and electrophysiology resulting from decades of experience in neuromodulation. The organization and content of this paper follow Dr. Okun's Lecture from the 2018 Parkinsonism and Related Disorders World Congress.

RESULTS

Information gained from neuromodulation has led to an expansion of the basal ganglia rate model, an enhanced understanding of nuclei dynamics, an emerging focus on pathological oscillations, a revision of the tripartite division of the basal ganglia, and a redirected focus toward individualized symptom-specific stimulation. Though there have been many limitations of the basal ganglia "box model," the construct provided the necessary foundation to advance the field. We now understand that information in the basal ganglia is encoded through complex neural responses that can be reliably measured and used to infer disease states for clinical translation.

CONCLUSIONS

Our deepened understanding of basal ganglia physiology will drive new neuromodulation strategies such as adaptive DBS or cell-specific neuromodulation through the use of optogenetics.

Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders

Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.

Deep brain stimulation in Tourette's syndrome: evidence to date

Tourette's syndrome (TS) is a neurodevelopmental disorder that comprises vocal and motor tics associated with a high frequency of psychiatric comorbidities, which has an important impact on quality of life. The onset is mainly in childhood and the symptoms can either fade away or require pharmacological therapies associated with cognitive-behavior therapies. In rare cases, patients experience severe and disabling symptoms refractory to conventional treatments. In these cases, deep brain stimulation (DBS) can be considered as an interesting and effective option for symptomatic control. DBS has been studied in numerous trials as a therapy for movement disorders, and currently positive data supports that DBS is partially effective in reducing the motor and non-motor symptoms of TS. The average response, mostly from case series and prospective cohorts and only a few controlled studies, is around 40% improvement on tic severity scales. The ventromedial thalamus has been the preferred target, but more recently the globus pallidus internus has also gained some notoriety. The mechanism by which DBS is effective on tics and other symptoms in TS is not yet understood. As refractory TS is not common, even reference centers have difficulties in performing large controlled trials. However, studies that reproduce the current results in larger and multicenter randomized controlled trials to improve our knowledge so as to support the best target and stimulation settings are still lacking. This article will discuss the selection of the candidates, DBS targets and mechanisms on TS, and clinical evidence to date reviewing current literature about the use of DBS in the treatment of TS.

A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders

Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the Summit RC+S "neural coprocessor," that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. This paper describes the updated system architecture for the Summit RC+S system, the five custom integrated circuits required for bi-directional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked Activa PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols.

Pallidal and thalamic neural oscillatory patterns in tourette's syndrome

OBJECTIVE

Aberrant oscillatory activity has been hypothesized to play a role in the pathophysiology of Tourette's syndrome (TS). Deep brain stimulation (DBS) has recently been established as an effective treatment for severe TS. Modulation of symptom-specific oscillations may underlie the mechanism of action of DBS and could be used for adaptive neuromodulation to improve therapeutic efficacy. The objective of this study was to demonstrate a pathophysiological association of pallidal and thalamic local field potentials (LFPs) with TS.

METHODS

Nine medication-refractory TS patients were included in the study. Intracerebral LFPs were recorded simultaneously from bilateral pallidal and thalamic DBS electrodes. Spectral and temporal dynamics of pallidal and thalamic oscillations were characterized and correlated with preoperative Yale Global Tic Severity Scale (YGTSS) scores.

RESULTS

Peaks of activity in the theta (3-12Hz) and beta (13-35Hz) were present in pallidal and thalamic recordings from all patients (3 women/6 men; mean age, 29.8 years) and coupled through coherence across targets. Presence of prolonged theta bursts in both targets was associated with preoperative motor tic severity. Total preoperative YGTSS scores (mean, 38.1) were correlated with pallidal and thalamic LFP activity using multivariable linear regression (R² = 0.96; p = 0.02).

INTERPRETATION

Our findings suggest that pallidothalamic oscillations may be implicated in the pathophysiology of TS. Furthermore, our results highlight the utility of multisite and -spectral oscillatory features in severely affected patients for future identification and clinical use of oscillatory physiomarkers for adaptive stimulation in TS. Ann Neurol 2018;84:505-514.

Totally Implantable Bidirectional Neural Prostheses: A Flexible Platform for Innovation in Neuromodulation

Implantable neural prostheses are in widespread use for treating a variety of brain disorders. Until recently, most implantable brain devices have been unidirectional, either delivering neurostimulation without brain sensing, or sensing brain activity to drive external effectors without a stimulation component. Further, many neural interfaces that incorporate a sensing function have relied on hardwired connections, such that subjects are tethered to external computers and cannot move freely. A new generation of neural prostheses has become available, that are both bidirectional (stimulate as well as record brain activity) and totally implantable (no externalized connections). These devices provide an opportunity for discovering the circuit basis for neuropsychiatric disorders, and to prototype personalized neuromodulation therapies that selectively interrupt neural activity underlying specific signs and symptoms.

Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing

OBJECTIVE

Contemporary deep brain stimulation (DBS) for Parkinson's disease is delivered continuously, and adjustments based on patient's changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is 'adaptive' DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects. Here we show the feasibility of adaptive DBS using a fully implanted neural prosthesis.

APPROACH

We demonstrate adaptive deep brain stimulation in two patients with Parkinson's disease using a fully implanted neural prosthesis that is enabled to utilize brain sensing to control stimulation amplitude (Activa PC  +  S). We used a cortical narrowband gamma (60-90 Hz) oscillation related to dyskinesia to decrease stimulation voltage when gamma oscillatory activity is high (indicating dyskinesia) and increase stimulation voltage when it is low.

MAIN RESULTS

We demonstrate the feasibility of 'adaptive deep brain stimulation' in two patients with Parkinson's disease. In short term in-clinic testing, energy savings were substantial (38%-45%), and therapeutic efficacy was maintained.

SIGNIFICANCE

This is the first demonstration of adaptive DBS in Parkinson's disease using a fully implanted device and neural sensing. Our approach is distinct from other strategies utilizing basal ganglia signals for feedback control.

A marked point process approach for identifying neural correlates of tics in Tourette Syndrome

We propose a novel interpretation of local field potentials (LFP) based on a marked point process (MPP) framework that models relevant neuromodulations as shifted weighted versions of prototypical temporal patterns. Particularly, the MPP samples are categorized according to the well known oscillatory rhythms of the brain in an effort to elucidate spectrally specific behavioral correlates. The result is a transient model for LFP. We exploit data-driven techniques to fully estimate the model parameters with the added feature of exceptional temporal resolution of the resulting events. We utilize the learned features in the alpha and beta bands to assess correlations to tic events in patients with Tourette Syndrome (TS). The final results show stronger coupling between LFP recorded from the centromedian-paraficicular complex of the thalamus and the tic marks, in comparison to electrocorticogram (ECoG) recordings from the hand area of the primary motor cortex (M1) in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Longitudinal Follow-up of Impedance Drift in Deep Brain Stimulation Cases

Background

Impedance is an integral property of neuromodulation devices that determines the current delivered to brain tissue. Long-term variability in therapeutic impedance following deep brain stimulation (DBS) has not been extensively investigated across different brain targets. The aim was to evaluate DBS impedance drift and variability over an extended postoperative period across common DBS targets.

Methods

Retrospective data from 1,764 electrode leads were included and drawn from 866 DBS patients enrolled in the University of Florida Institutional Review Board-approved INFORM database and analyzed up to 84 months post implantation. An exploratory analysis was conducted to identify trends in impedances using a Mann–Kendall test of trend.

Results

There were 866 patients and 1,764 leads available for analysis. The majority of subjects had Parkinson’s disease (60.7%). The mean age at implantation was 58.7 years old and the mean follow-up time was 36.8 months. There were significant fluctuations in the mean impedance of all electrodes analyzed that largely stabilized by 6 months except for the subthalamic nucleus (STN) target, in which fluctuations persisted throughout the duration of follow-up with a continued downward trend (p < 0.001).

Discussion

The drift in impedance observed primarily within the first 6 months is in keeping with prior studies and is likely due to surgical micro-lesioning effects and brain parenchyma remodeling at the electrode–tissue interface, typically at values approximating 1,000 Ω. The differences in impedance trends over time in the various DBS targets may be due to underlying differences in structure and tissue composition.