Postoperative management of deep brain stimulation in Parkinson's disease

Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

OBJECTIVES

Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming.

MATERIALS AND METHODS

A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months.

RESULTS

Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test).

CONCLUSIONS

Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

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

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

Therapeutic Neurostimulation in Obsessive-Compulsive and Related Disorders: A Systematic Review

Invasive and noninvasive neurostimulation therapies for obsessive-compulsive and related disorders (OCRD) were systematically reviewed with the aim of assessing clinical characteristics, methodologies, neuroanatomical substrates, and varied stimulation parameters. Previous reviews have focused on a narrow scope, statistical rather than clinical significance, grouped together heterogenous protocols, and proposed inconclusive outcomes and directions. Herein, a comprehensive and transdiagnostic evaluation of all clinically relevant determinants is presented with translational clinical recommendations and novel response rates. Electroconvulsive therapy (ECT) studies were limited in number and quality but demonstrated greater efficacy than previously identified. Targeting the pre-SMA/SMA is recommended for transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS). TMS yielded superior outcomes, although polarity findings were conflicting, and refinement of frontal/cognitive control protocols may optimize outcomes. For both techniques, standardization of polarity, more treatment sessions (>20), and targeting multiple structures are encouraged. A deep brain stimulation (DBS) 'sweet spot' of the striatum for OCD was proposed, and CBT is strongly encouraged. Tourette's patients showed less variance and reliance on treatment optimization. Several DBS targets achieved consistent, rapid, and sustained clinical response. Analysis of fiber connectivity, as opposed to precise neural regions, should be implemented for target selection. Standardization of protocols is necessary to achieve translational outcomes.

Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients

Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.

Apathy Induced by Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease: A Meta-Analysis

Apathy, the loss of motivation, is a common problem in Parkinson's disease (PD) and often observed following deep brain stimulation (DBS) of the subthalamic nucleus (STN). The aim of this meta-analysis was to determine the occurrence of apathy following STN DBS in literature. Relevant articles were searched in PubMed/Medline, SCOPUS, EMBASE, and Web of Sciences electronic databases. Studies were included if they reported apathy scores pre- and post-DBS or the cross-sectional difference between PD patients receiving STN DBS and patients receiving medication only. Thirty-three articles were included in the meta-analyses from 6,658 screened articles by two authors independently. A total of 1,286 patients were included with a mean age (±standard deviation [SD]) of 58.4 ± 8.5 years and a disease duration of 11.0 ± 5.8 years. The apathy score measured by means of the Apathy Evaluation Scale (AES), Starkstein Apathy Scale (SAS), and the Lille Apathy Rating Scale (LARS) was significantly higher after DBS than pre-operatively (g = 0.34, 95% confidence interval [CI] = 0.19-0.48, P < 0.001). An equal, significant difference in severity of apathy was found between STN DBS and medication only (g = 0.36, 95% CI = 0.03-0.65; P = 0.004). Statistical heterogeneity was moderately high, but the effects stood strong after multiple analyses and were independent of tapering off dopaminergic medication. The findings of this meta-analysis indicate that apathy is increased after STN DBS compared to the pre-operative state and to medication only (systematic review registration number: PROSPERO CRD42019133932). © 2020 Universiteit van Amsterdam. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Opportunities of connectomic neuromodulation

The process of altering neural activity - neuromodulation - has long been used to treat patients with brain disorders and answer scientific questions. Deep brain stimulation in particular has provided clinical benefit to over 150,000 patients. However, our understanding of how neuromodulation impacts the brain is evolving. Instead of focusing on the local impact at the stimulation site itself, we are considering the remote impact on brain regions connected to the stimulation site. Brain connectivity information derived from advanced magnetic resonance imaging data can be used to identify these connections and better understand clinical and behavioral effects of neuromodulation. In this article, we review studies combining neuromodulation and brain connectomics, highlighting opportunities where this approach may prove particularly valuable. We focus on deep brain stimulation, but show that the same principles can be applied to other forms of neuromodulation, such as transcranial magnetic stimulation and MRI-guided focused ultrasound. We outline future perspectives and provide testable hypotheses for future work.

Modeling of Electric Fields in Individual Imaging Atlas for Capsular Threshold Prediction of Deep Brain Stimulation in Parkinson's Disease: A Pilot Study

Background: Modeling of deep brain stimulation electric fields and anatomy-based software might improve post-operative management of patients with Parkinson's disease (PD) who have benefitted from subthalamic nucleus deep brain stimulation (STN-DBS).

Objective: We compared clinical and software-guided determination of the thresholds for current diffusion to the pyramidal tract, the most frequent limiting side effect in post-operative management of STN-DBS PD patients.

Methods: We assessed monopolar reviews in 16 consecutive STN-DBS PD patients and retrospectively compared clinical capsular thresholds, which had been assessed according to standard clinical practice, to those predicted by volume of tissue activated (VTA) model software. All the modeling steps were performed blinded from patients' clinical evaluations.

Results: At the group level, we found a significant correlation (p = 0.0001) when performing statistical analysis on the z-scored capsular thresholds, but with a low regression coefficient (r = 0.2445). When considering intra-patient analysis, we found significant correlations (p < 0.05) between capsular threshold as modeled with the software and capsular threshold as determined clinically in five patients (31.2%).

Conclusions: In this pilot study, the VTA model software was of limited assistance in identifying capsular thresholds for the whole cohort due to a large inter-patient variability. Clinical testing remains the gold standard in selecting stimulation parameters for STN-DBS in PD.

Illness Representations and Coping Strategies in Patients Treated with Deep Brain Stimulation for Parkinson's Disease

There is a debate on possible alterations of self-identity following deep brain stimulation for neurological disorders including Parkinson's disease. Among the psychological variables likely to undergo changes throughout such a medical procedure, illness representations and coping strategies have not been the target of much research to this day. In order to remedy this, we investigated the dynamics of illness representations and coping strategies in an 18-month longitudinal study involving 45 patients undergoing deep brain stimulation for idiopathic Parkinson's disease. Two research hypotheses were formulated and investigated through repeated measures of ANOVAs and structural equation modelling with full information maximum likelihood and Bayesian estimations. Representations of Parkinson's disease as a cyclical condition and perception of control over the disease diminished after surgery. Use of instrumental coping strategies was not modified after deep brain stimulation. These changes were identified by SEM but not ANOVAs; their magnitude was nevertheless relatively small, implying general stability in representations. These findings suggest that psychological variables do not undergo major changes after deep brain stimulation for Parkinson's disease.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

Automated Deep Brain Stimulation Programming for Tremor

Deep brain stimulation (DBS) programming, the systematic selection of fixed electrical stimulation parameters that deliver maximal therapeutic benefit while limiting side effects, poses several challenges in the treatment of movement disorders. DBS programming requires the expertise of trained neurologists or nurses who assess patient symptoms according to standardized clinical rating scales and use patient reports of DBS-related side effects to adjust stimulation parameters and optimize therapy. In this paper, we describe and validate an automated software platform for DBS programming for tremor associated with Parkinson's disease and essential tremor. DBS parameters are changed automatically through a direct computer interface with implanted neurostimulators. Each tested DBS setting is ranked according to its effect on tremor, which is assessed using smartwatch inertial measurement unit data, and side effects, which are reported through a user interface. Blinded neurologist assessments showed the automated programming method performed at least as well as clinician mediated programming in selecting the optimal settings for tremor therapy. This proof of concept study describes a novel DBS programming paradigm that may improve programming efficiency and outcomes, increase access to programming outside specialty clinics, and aid in the development of adaptive and closed-loop DBS strategies.

Establishing a Standard of Care for Deep Brain Stimulation Centers in Canada

During the “DBS Canada Day” symposium held in Toronto July 4-5, 2014, the scientific committee invited experts to share their knowledge regarding deep brain stimulation (DBS) management of movement disorders in three domains: (1) the programming algorithms, (2) the necessary team to run a neurosurgery program, and (3) the appropriate scales to better define in a more comprehensive fashion the effect of the brain surgery. Each presentation was followed by an open discussion, and this article reports on the conclusions of this meeting on these three questions. Concerning programming, the role of the pulse width and the switching off of the stimulation at night for thalamic stimulation for the control of tremor have been discussed. The algorithms proposed in the literature for programming in Parkinson’s disease (PD) need validation. In dystonia, the use of monopolar vs bipolar parameters, the use of low vs high frequencies and the use of smaller versus larger pulse widths all need to be examined properly. Concerning the necessary team to run a neurosurgical program, recommendations will follow the suggestions for standardized outcome measures. Regarding the outcome measures for DBS in PD, investigations need to focus on the non-motor aspects of PD. Identifying which nonmotor symptoms respond to DBS would allow a better screening before and satisfaction postoperatively. There is an important need for more data to determine the optimal programming protocol and the standard measures that should be performed routinely by all centers.

Deep Brain Stimulation Target Selection for Parkinson’s Disease

During the “DBS Canada Day” symposium held in Toronto July 4-5, 2014, the scientific committee invited experts to discuss three main questions on target selection for deep brain stimulation (DBS) of patients with Parkinson’s disease (PD). First, is the subthalamic nucleus (STN) or the globus pallidus internus (GPi) the ideal target? In summary, both targets are equally effective in improving the motor symptoms of PD. STN allows a greater medications reduction, while GPi exerts a direct antidyskinetic effect. Second, are there further potential targets? Ventral intermediate nucleus DBS has significant long-term benefit for tremor control but insufficiently addresses other motor features of PD. DBS in the posterior subthalamic area also reduces tremor. The pedunculopontine nucleus remains an investigational target. Third, should DBS for PD be performed unilaterally, bilaterally or staged? Unilateral STN DBS can be proposed to asymmetric patients. There is no evidence that a staged bilateral approach reduces the incidence of DBS-related adverse events.

Subthalamic stimulation may inhibit the beneficial effects of levodopa on akinesia and gait

BACKGROUND

Gait and akinesia deterioration in PD patients during the immediate postoperative period of DBS has been directly related to stimulation in the subthalamic region. The underlying mechanisms remain poorly understood. The aim of the present study was to clinically and anatomically describe this side effect.

METHODS

PD patients presenting with a worsening of gait and/or akinesia following STN-DBS, that was reversible on stimulation arrest were included. The evaluation included (1) a Stand Walk Sit Test during a monopolar survey of each electrode in the on-drug condition; (2) a 5-condition test with the following conditions: off-drug/off-DBS, off-drug/on-best-compromise-DBS, on-drug/off-DBS, on-drug/on-best-compromise-DBS, and on-drug/on-worsening-DBS, which utilized the contact inducing the most prominent gait deterioration. The following scales were performed: UPDRSIII subscores, Stand Walk Sit Test, and dyskinesia and freezing of gait scales. Localization of contacts was performed using a coregistration method.

RESULTS

Twelve of 17 patients underwent the complete evaluation. Stimulation of the most proximal contacts significantly slowed down the Stand Walk Sit Test. The on-drug/on-worsening-DBS condition compared with the on-drug/off-DBS condition worsened akinesia (P = 0.02), Stand Walk Sit Test (P = 0.001), freezing of gait (P = 0.02), and improved dyskinesias (P = 0.003). Compared with the off-drug/off-DBS condition, the on-drug/on-worsening-DBS condition improved rigidity (P = 0.007) and tremor (P = 0.007). Worsening contact sites were predominantly dorsal and anterior to the STN in the anterior zona incerta and Forel fields H2.

CONCLUSIONS

A paradoxical deterioration of gait and akinesia is a rare side effect following STN-DBS. We propose that this may be related to misplaced contacts, and we discuss the pathophysiology and strategies to identify and manage this complication. © 2016 International Parkinson and Movement Disorder Society.

Greater improvement in LRRK2 G2019S patients undergoing Subthalamic Nucleus Deep Brain Stimulation compared to non-mutation carriers

Background

Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) of parkinson’s disease (PD) patients has demonstrated to improve motor performance and to reduce dopa-induced dyskinesia. An association between the occurrence of dyskinesias and LRRK2 (leucine-rich repeat kinase 2) G2019S gene mutations has recently been suggested. The aim of this study is to discover the impact of the G2019S mutation (with high incidence in the authors’ native Algeria) on the symptom response of PD in patients who underwent STN-DBS.

Methods

We carried out a comparative statistical study for the clinical evaluation and neuropsychological assessment of 27 Algerian PD STN-DBS patients, both G2019S mutation carriers (MC) and non-carriers (NC). A multiple correspondence analysis (MCA) was then conducted to compare the results with those from groups of individuals with similar modalities.

Results

The MCA revealed that MC and NC PD patients showed two different patterns of clinical evaluations. The group of idiopathic patients showed some differences compared to the clinical evaluations, depending on gender. No association was found between the G2019S mutation and the Mini Mental State Examination scores (MMSE), and MC patients appeared more susceptible to dyskinesia than NC patients. In NC patients, we found two cases with Parkin mutations who had a different “honeymoon” period and different initial symptoms. The results showed considerable improvement of motor unified parkinson’s disease rating scale III (UPDRS-III) in a situation of stimulation without medication in the MC patients with a percentage of improvement (51.1 %) over the required 30 % compared to the NC patients (25.5 %). The same result was observed for the Schwab and England’s activities of daily living scale (S and E scale), which thus demonstrated a greater effectiveness of DBS for MC patients than for NC patients. However, the Hoehn and Yahr scale (H and Y Scale) showed the same significance in a situation of stimulation for MC and NC patients. In this later group, the best scores of UPDRS-III were observed for patients with the Parkin mutation before they underwent surgery.

Conclusions

This study shows that surgical treatment probably has a more significant impact on LRRK2 G2019S MC than on idiopathic patients.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization.

OBJECTIVE

Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication.

METHODS

Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest.

RESULTS

Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery.

CONCLUSIONS

Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients.

Apathy in Parkinson's disease: clinical features, neural substrates, diagnosis, and treatment

Normal maintenance of human motivation depends on the integrity of subcortical structures that link the prefrontal cortex with the limbic system. Structural and functional disruption of different networks within these circuits alters the maintenance of spontaneous mental activity and the capacity of affected individuals to associate emotions with complex stimuli. The clinical manifestations of these changes include a continuum of abnormalities in goal-oriented behaviours known as apathy. Apathy is highly prevalent in Parkinson's disease (and across many neurodegenerative disorders) and can severely affect the quality of life of both patients and caregivers. Differentiation of apathy from depression, and discrimination of its cognitive, emotional, and auto-activation components could guide an individualised approach to the treatment of symptoms. The opportunity to manipulate dopaminergic treatment in Parkinson's disease allows researchers to study a continuous range of motivational states, from apathy to impulse control disorders. Parkinson's disease can thus be viewed as a model that provides insight into the neural substrates of apathy.

Mood and behavioural effects of subthalamic stimulation in Parkinson's disease

Deep-brain stimulation (DBS) of the subthalamic nucleus (STN) is an established treatment for motor complications in Parkinson's disease. 20 years of experience with this procedure have contributed to improved understanding of the role of the STN in motor, cognitive, and emotional control. In Parkinson's disease, the pathological STN neuronal activity leads to motor, cognitive, and emotional inhibition. Deafferentation of the STN by DBS can reverse such behavioural inhibition. The release of this brake allows both motor and non-motor improvement, but can also be associated with excessive motor, cognitive, and emotional behavioural disinhibition. Conversely, the notable reduction in anti-parkinsonian drug dose allowed by motor improvement can unveil mesolimbic hypodopaminergic behaviours such as apathy, anxiety, or depression. Fine-tuning of stimulation parameters with dopaminergic drugs is necessary to prevent or improve pathological behaviours.