Deep brain stimulation of symptom-specific networks in Parkinson's disease.

Deep Brain Stimulation can improve tremor, bradykinesia, rigidity, and axial symptoms in patients with Parkinson's disease. Potentially, improving each symptom may require stimulation of different white matter tracts. Here, we study a large cohort of patients (N = 237 from five centers) to identify tracts associated with improvements in each of the four symptom domains. Tremor improvements were associated with stimulation of tracts connected to primary motor cortex and cerebellum. In contrast, axial symptoms are associated with stimulation of tracts connected to the supplementary motor cortex and brainstem. Bradykinesia and rigidity improvements are associated with the stimulation of tracts connected to the supplementary motor and premotor cortices, respectively. We introduce an algorithm that uses these symptom-response tracts to suggest optimal stimulation parameters for DBS based on individual patient's symptom profiles. Application of the algorithm illustrates that our symptom-tract library may bear potential in personalizing stimulation treatment based on the symptoms that are most burdensome in an individual patient.

Electrophysiological sweet spot mapping in deep brain stimulation for Parkinson's disease patients.

BACKGROUND: Subthalamic deep brain stimulation (STN-DBS) is a well-established therapy to treat Parkinson's disease (PD). However, the STN-DBS sub-target remains debated. Recently, a white matter tract termed the hyperdirect pathway (HDP), directly connecting the motor cortex to STN, has gained interest as HDP stimulation is hypothesized to drive DBS therapeutic effects. Previously, we have investigated EEG-based evoked potentials (EPs) to better understand the neuroanatomical origins of the DBS clinical effect. We found a 3-millisecond peak (P3) relating to clinical benefit, and a 10-millisecond peak (P10) suggesting nigral side effects. Here, we aimed to investigate the neuroanatomical origins of DBS EPs using probabilistic mapping. METHODS: EPs were recorded using EEG whilst low-frequency stimulation was delivered at all DBS-contacts individually. Next, EPs were mapped onto the patients' individual space and then transformed to MNI standard space. Using voxel-wise and fiber-wise probabilistic mapping, we determined hotspots/hottracts and coldspots/coldtracts for P3 and P10. Topography analysis was also performed to determine the spatial distribution of the DBS EPs. RESULTS: In all 13 patients (18 hemispheres), voxel- and fiber-wise probabilistic mapping resulted in a P3-hotspot/hottract centered on the posterodorsomedial STN border indicative of HDP stimulation, while the P10-hotspot/hottract covered large parts of the substantia nigra. CONCLUSION: This study investigated EP-based probabilistic mapping in PD patients during STN-DBS, revealing a P3-hotspot/hottract in line with HDP stimulation and P10-hotspot/hottract related to nigral stimulation. Results from this study provide key evidence for an electrophysiological measure of HDP and nigral stimulation.

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson's Disease.

Background: Conventional deep brain stimulation (DBS) programming via trial-and-error warrants improvement to ensure swift achievement of optimal outcomes. The definition of a sweet spot for subthalamic DBS in Parkinson's disease (PD-STN-DBS) may offer such advancement. Objective: This investigation examines the association of long-term motor outcomes with contact selection during monopolar review and different strategies for anatomically informed contact selection in a retrospective real-life cohort of PD-STN-DBS. Methods: We compared contact selection based on a monopolar review (MPR) to multiple anatomically informed contact selection strategies in a cohort of 28 PD patients with STN-DBS. We employed a commercial software package for contact selection based on visual assessment of individual anatomy following two predefined strategies and two algorithmic approaches with automatic targeting of either the sensorimotor STN or our previously published sweet spot. Similarity indices between chronic stimulation and contact selection strategies were correlated to motor outcomes at 12 months follow-up. Results: Lateralized motor outcomes of chronic DBS were correlated to the similarity between chronic stimulation and visual contact selection targeting the dorsal part of the posterior STN (rho = 0.36, p = 0.007). Similar relationships could not be established for MPR or any of the other investigated strategies. Conclusions: Our data demonstrates that a visual contact selection following a predefined strategy can be linked to beneficial long-term motor outcomes in PD-STN-DBS. Since similar correlations could not be observed for the other approaches to anatomically informed contact selection, we conclude that clear definitions and prospective validation of any approach to imaging-based DBS-programming is warranted.

Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation.

Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

Thalamic deep brain stimulation for tourette syndrome increases cortical beta activity.

BACKGROUND: Deep brain stimulation (DBS) of the thalamus can effectively reduce tics in severely affected patients with Tourette syndrome (TS). Its effect on cortical oscillatory activity is currently unknown. OBJECTIVE: We assessed whether DBS modulates beta activity at fronto-central electrodes. We explored concurrent EEG sources and probabilistic stimulation maps. METHODS: Resting state EEG of TS patients treated with thalamic DBS was recorded in repeated DBS-on and DBS-off states. A mixed linear model was employed for statistical evaluation. EEG sources were estimated with eLORETA. Thalamic probabilistic stimulation maps were obtained by assigning beta power difference scores (DBS-on minus DBS-off) to stimulation sites. RESULTS: We observed increased beta power in DBS-on compared to DBS-off states. Modulation of cortical beta activity was localized to the midcingulate cortex. Beta modulation was more pronounced when stimulating the thalamus posteriorly, peaking in the ventral posterior nucleus. CONCLUSION: Thalamic DBS in TS patients modulates beta frequency oscillations presumably important for sensorimotor function and relevant to TS pathophysiology.

Diffusion tensor imaging in pediatric patients with dystonia.

BACKGROUND: Childhood-onset dystonia is often progressive and severely impairs a child´s life. The pathophysiology is very heterogeneous and treatment responses vary in patients with dystonia. Factors influencing treatment effects remain to be elucidated. We hypothesize that differences in brain connectivity and fiber coherence contribute to the heterogeneity in treatment response among pediatric patients with inherited and acquired dystonia. METHODS: Twenty patients with childhood-onset dystonia were retrospectively recruited including twelve patients with inherited or idiopathic, and eight patients with acquired dystonia (mean age 10 years; 8 female/12 male). Fiber density between the internal part of the globus pallidus and selective target regions, as well as the diffusion measures of fractional anisotropy (FA) and mean diffusivity (MD) were analyzed and compared between different etiologies. RESULTS: Patients with acquired dystonia presented higher fiber density to the premotor cortex and putamen and lower FA values in the thalamus compared to patients with inherited/idiopathic dystonia. MD in the premotor cortex was higher in patients with acquired dystonia, while it was lower in the thalamus. CONCLUSION: Diffusion MRI reveals microstructural and network alterations in patients with dystonia of different etiologies.

Theta frequency deep brain stimulation in the subthalamic nucleus improves working memory in Parkinson's disease.

Most research in Parkinson's disease focuses on improving motor symptoms. Yet, up to 80% of patients present with non-motor symptoms that often have a large impact on patients' quality of life. Impairment in working memory, a fundamental cognitive process, is common in Parkinson's disease. While deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms in Parkinson's disease, its impact on cognitive functions is less well studied. Here, we examine the effect of DBS in the theta, beta, low and high gamma frequency on working memory in 20 Parkinson's disease patients with bilateral STN-DBS. A linear mixed effects model demonstrates that STN-DBS in the theta frequency improves working memory performance. This effect is frequency-specific and was absent for beta and gamma frequency stimulation. Further, this effect is specific to cognitive performance, as theta frequency DBS did not affect motor function. A non-parametric cluster-based permutation analysis of whole-brain normative structural connectivity shows that working memory enhancement by theta frequency stimulation is associated with higher connectivity between the stimulated subthalamic area and the right middle frontal gyrus. Again, this association is frequency- and task-specific. These findings highlight the potential of theta frequency STN-DBS as a targeted intervention to improve working memory in patients with Parkinson's disease.

General Algorithm Applicability in Determining DBS Lead Orientation: Adapting 2D and 3D X-Ray Techniques for SenSightTM Leads.

INTRODUCTION: With recent advancements in deep brain stimulation (DBS), directional leads featuring segmented contacts have been introduced, allowing for targeted stimulation of specific brain regions. Given that manufacturers employ diverse markers for lead orientation, our investigation focuses on the adaptability of the 2017 techniques proposed by the Cologne research group for lead orientation determination. METHODS: We tailored the two separate 2D and 3D X-ray-based techniques published in 2017 and originally developed for C-shaped markers, to the dual-marker of the Medtronic SenSight™ lead. In a retrospective patient study, we evaluated their feasibility and consistency by comparing the degree of agreement between the two methods. RESULTS: The Bland-Altman plot showed favorable concordance without any noticeable systematic errors. The mean difference was 0.79°, with limits of agreement spanning from 21.4° to -19.8°. The algorithms demonstrated high reliability, evidenced by an intraclass correlation coefficient of 0.99 (p < 0.001). CONCLUSION: The 2D and 3D algorithms, initially formulated for discerning the circular orientation of a C-shaped marker, were adapted to the marker of the Medtronic SenSight™ lead. Statistical analyses revealed a significant level of agreement between the two methods. Our findings highlight the adaptability of these algorithms to different markers, achievable through both low-dose intraoperative 2D X-ray imaging and standard CT imaging.

Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites.

BACKGROUND: Deep brain stimulation (DBS) is a promising treatment option for treatment-refractory obsessive-compulsive disorder (OCD). Several stimulation targets have been used, mostly in and around the anterior limb of the internal capsule and ventral striatum. However, the precise target within this region remains a matter of debate. METHODS: Here, we retrospectively studied a multicenter cohort of 82 patients with OCD who underwent DBS of the ventral capsule/ventral striatum and mapped optimal stimulation sites in this region. RESULTS: DBS sweet-spot mapping performed on a discovery set of 58 patients revealed 2 optimal stimulation sites associated with improvements on the Yale-Brown Obsessive Compulsive Scale, one in the anterior limb of the internal capsule that overlapped with a previously identified OCD-DBS response tract and one in the region of the inferior thalamic peduncle and bed nucleus of the stria terminalis. Critically, the nucleus accumbens proper and anterior commissure were associated with beneficial but suboptimal clinical improvements. Moreover, overlap with the resulting sweet- and sour-spots significantly estimated variance in outcomes in an independent cohort of 22 patients from 2 additional DBS centers. Finally, beyond obsessive-compulsive symptoms, stimulation of the anterior site was associated with optimal outcomes for both depression and anxiety, while the posterior site was only associated with improvements in depression. CONCLUSIONS: Our results suggest how to refine targeting of DBS in OCD and may be helpful in guiding DBS programming in existing patients.

EEG-based biomarkers for optimizing deep brain stimulation contact configuration in Parkinson's disease.

Objective: Subthalamic deep brain stimulation (STN-DBS) is a neurosurgical therapy to treat Parkinson's disease (PD). Optimal therapeutic outcomes are not achieved in all patients due to increased DBS technological complexity; programming time constraints; and delayed clinical response of some symptoms. To streamline the programming process, biomarkers could be used to accurately predict the most effective stimulation configuration. Therefore, we investigated if DBS-evoked potentials (EPs) combined with imaging to perform prediction analyses could predict the best contact configuration. Methods: In 10 patients, EPs were recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. In two patients, we recorded from both hemispheres, resulting in recordings from a total of 12 hemispheres. A monopolar review was performed by stimulating on each contact and measuring the therapeutic window. CT and MRI data were collected. Prediction models were created to assess how well the EPs and imaging could predict the best contact configuration. Results: EPs at 3 ms and at 10 ms were recorded. The prediction models showed that EPs can be combined with imaging data to predict the best contact configuration and hence, significantly outperformed random contact selection during a monopolar review. Conclusion: EPs can predict the best contact configuration. Ultimately, these prediction tools could be implemented into daily practice to ease the DBS programming of PD patients.

Accelerometric Classification of Resting and Postural Tremor Amplitude.

Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson's Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen's weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.

Parkinson's Disease Stigma Questionnaire (PDStigmaQuest): Development and Pilot Study of a Questionnaire for Stigma in Patients with Idiopathic Parkinson's Disease.

BACKGROUND: Stigma is significant in Parkinson's disease (PD). However, no specific tool is available to assess stigma in PD comprehensively. OBJECTIVE: This pilot study aimed to develop and test a stigma questionnaire specific to PD patients (PDStigmaQuest). METHODS: Based on a literature review, clinical experience, expert consensus, and patients' feedback, we developed the preliminary, patient-completed PDStigmaQuest in German language. It included 28 items covering five stigma domains: uncomfortableness, anticipated stigma, hiding, experienced stigma, and internalized stigma. In this pilot study, 81 participants (PD patients, healthy controls, caregivers, and health professionals) were included to investigate the acceptability, feasibility, comprehensibility, and psychometric properties of the PDStigmaQuest. RESULTS: The PDStigmaQuest showed 0.3% missing data points for PD patients and 0.4% for controls, suggesting high data quality. Moderate floor effects, but no ceiling effects were found. In the item analysis, most items met the standard criteria of item difficulty, item variance, and item-total correlation. Cronbach's alpha was > 0.7 for four of five domains. PD patients' domain scores were significantly higher than healthy controls' for uncomfortableness, anticipated stigma, and internalized stigma. Feedback to the questionnaire was predominantly positive. CONCLUSION: Our results indicate that the PDStigmaQuest is a feasible, comprehensive, and relevant tool to assess stigma in PD and helps to understand the construct of stigma in PD further. Based on our results, the preliminary version of the PDStigmaQuest was modified and is currently validated in a larger population of PD patients for use in clinical and research settings.

Low beta-band suppression as a tool for DBS contact selection for akinetic-rigid symptoms in Parkinson's disease.

BACKGROUND: Suppression of pathologically altered activity in the beta-band has previously been suggested as a biomarker for feedback-based neurostimulation in subthalamic deep brain stimulation (STN-DBS) for Parkinson's Disease (PD). OBJECTIVE: To assess the utility of beta-band suppression as a tool for contact selection in STN-DBS for PD. METHODS: A sample of seven PD patients (13 hemispheres) with newly implanted directional DBS leads of the STN were recorded during a standardized monopolar contact review (MPR). Recordings were received from contact pairs adjacent to the stimulation contact. The degree of beta-band suppression for each investigated contact was then correlated to the respective clinical results. Additionally, we have implemented a cumulative ROC analysis, to test the predictive value of beta-band suppression on the clinical efficacy of the respective contacts. RESULTS: Stimulation ramping led to frequency-specific changes in the beta-band, while lower frequencies remained unaffected. Most importantly, our results showed that the degree of low beta-band suppression from baseline activity (stimulation off) served as a predictor for clinical efficacy of the respective stimulation contact. In contrast suppression of high beta-band activity yielded no predictive power. CONCLUSION: The degree of low beta-band suppression can serve as a time-saving, objective tool for contact selection in STN-DBS.

Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at -24 h and 0.88 at -48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.

Neuroimaging-based analysis of DBS outcomes in pediatric dystonia: Insights from the GEPESTIM registry.

INTRODUCTION: Deep brain stimulation (DBS) is an established treatment in patients of various ages with pharmaco-resistant neurological disorders. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures, and on electrode connectivity to a specific distribution pattern within brain networks. Such information is usually collected using group-level analysis, which relies on the availability of normative imaging resources (atlases and connectomes). Analysis of DBS data in children with debilitating neurological disorders such as dystonia would benefit from such resources, especially given the developmental differences in neuroimaging data between adults and children. We assembled pediatric normative neuroimaging resources from open-access datasets in order to comply with age-related anatomical and functional differences in pediatric DBS populations. We illustrated their utility in a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources. METHODS: An average pediatric brain template (the MNI brain template 4.5-18.5 years) was implemented and used to localize the DBS electrodes in 20 patients from the GEPESTIM registry cohort. A pediatric subcortical atlas, analogous to the DISTAL atlas known in DBS research, was also employed to highlight the anatomical structures of interest. A local pallidal sweetspot was modeled, and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcomes. Additionally, a pediatric functional connectome of 100 neurotypical subjects from the Consortium for Reliability and Reproducibility was built to allow network-based analyses and decipher a connectivity fingerprint responsible for the clinical improvements in our cohort. RESULTS: We successfully implemented a pediatric neuroimaging dataset that will be made available for public use as a tool for DBS analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). The functional connectivity fingerprint of DBS outcomes was determined to be a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003). CONCLUSIONS: Local sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcomes in dystonia using pediatric neuroimaging surrogate data. Implementation of this pediatric neuroimaging dataset might help to improve the practice and pave the road towards a personalized DBS-neuroimaging analyses in pediatric patients.

Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation.

Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.

Internal Capsule/Nucleus Accumbens Deep Brain Stimulation Increases Impulsive Decision Making in Obsessive-Compulsive Disorder.

BACKGROUND: Deep brain stimulation of the anterior limb of the internal capsule (ALIC)/nucleus accumbens is an effective treatment in patients with obsessive-compulsive disorder but may increase impulsive behavior. We aimed to investigate how active stimulation alters subdomains of impulsive decision making and whether respective effects depend on the location of stimulation sites. METHODS: We assessed 15 participants with obsessive-compulsive disorder performing the Cambridge Gambling Task during active and inactive ALIC/nucleus accumbens deep brain stimulation. Specifically, we determined stimulation-induced changes in risk adjustment and delay aversion. To characterize underlying neural pathways, we computed probabilistic stimulation maps and applied fiber filtering based on normative structural connectivity data to identify "hot" and "cold" spots/fibers related to changes in impulsive decision making. RESULTS: Active stimulation significantly reduced risk adjustment while increasing delay aversion, both implying increased impulsive decision making. Changes in risk adjustment were robustly associated with stimulation sites located in the central ALIC and fibers connecting the thalamus and subthalamic nucleus with the medial and lateral prefrontal cortex. Both hot spots and fibers for changes in risk adjustment were robust to leave-one-out cross-validation. Changes in delay aversion were similarly associated with central ALIC stimulation, but validation hereof was nonsignificant. CONCLUSIONS: Our findings provide experimental evidence that ALIC/nucleus accumbens stimulation increases impulsive decision making in obsessive-compulsive disorder. We show that changes in risk adjustment depend on the location of stimulation volumes and affected fiber bundles. The relationship between impulsive decision making and long-term clinical outcomes requires further investigation.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Brain Morphometry Associated With Response to Levodopa and Deep Brain Stimulation in Parkinson Disease.

OBJECTIVES: Whether treatment response in patients with Parkinson disease depends on brain atrophy is insufficiently understood. The goal of this study is to identify specific atrophy patterns associated with response to dopaminergic therapy and deep brain stimulation. MATERIALS AND METHODS: In this study, we analyzed the association of gray matter brain atrophy patterns, as identified by voxel-based morphometry, with acute response to levodopa (N = 118) and subthalamic nucleus deep brain stimulation (N = 39). Motor status was measured as a change in points on the Unified Parkinson's Disease Rating Scale III score. Baseline values were obtained before surgery, after cessation of dopaminergic medication for at least 12 hours; response to medication was assessed after administration of a standardized dose of levodopa. Response to deep brain stimulation was measured three months after surgery in the clinical condition after withdrawal of dopaminergic medication. RESULTS: Although frontoparietal brain gray matter loss was associated with subpar response to deep brain stimulation, there was no significant link between brain atrophy and response to levodopa. CONCLUSION: We conclude that response to deep brain stimulation relies on gray matter integrity; hence, gray matter loss may present a risk factor for poor response to deep brain stimulation and may be considered when making decision regarding clinical practice.

Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a data-driven algorithm.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in controlling motor symptoms in patients with Parkinson's disease. However, correct selection of stimulation parameters is pivotal to treatment success and currently follows a time-consuming and demanding trial-and-error process. We aimed to assess treatment effects of stimulation parameters suggested by a recently published algorithm (StimFit) based on neuroimaging data. METHODS: This double-blind, randomised, crossover, non-inferiority trial was carried out at Charité - Universitätsmedizin, Berlin, Germany, and enrolled patients with Parkinson's disease treated with directional octopolar electrodes targeted at the STN. All patients had undergone DBS programming according to our centre's standard of care (SoC) treatment before study recruitment. Based on perioperative imaging data, DBS electrodes were reconstructed and StimFit was applied to suggest optimal stimulation settings. Patients underwent motor assessments using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) during OFF-medication and in OFF-stimulation and ON-stimulation states under both conditions, StimFit and SoC parameter settings. Patients were randomly assigned (1:1) to receive either StimFit-programmed DBS first and SoC-programmed DBS second, or SoC-programmed DBS first and StimFit-programmed DBS second. The allocation schedule was generated using a computerised random number generator. Both the rater and patients were masked to the sequence of SoC and StimFit stimulation conditions. All patients who participated in the study were included in the analysis. The primary endpoint of this study was the absolute mean difference between MDS-UPDRS-III scores under StimFit and SoC stimulation, with a non-inferiority margin of 5 points. The study was registered at the German Register for Clinical Trials (DRKS00023115), and is complete. FINDINGS: Between July 10, 2020, and Oct 28, 2021, 35 patients were enrolled in the study; 18 received StimFit followed by SoC stimulation, and 17 received SoC followed by StimFit stimulation. Mean MDS-UPDRS-III scores improved from 47·3 (SD 17·1) at OFF-stimulation baseline to 24·7 (SD 12·4) and 26·3 (SD 12·4) under SoC and StimFit stimulation, respectively. Mean difference between motor scores was -1·6 (SD 7·1; 95% CI -4·0 to 0·9; superiority test psuperiority=0·20; n=35), establishing non-inferiority of StimFit stimulation at a margin of -5 points (non-inferiority test pnon-inferiority=0·0038). In six patients (17%), initial programming of StimFit settings resulted in acute side-effects and amplitudes were reduced until side-effects disappeared. INTERPRETATION: Automated data-driven algorithms can predict stimulation parameters that lead to motor symptom control comparable to SoC treatment. This approach could significantly decrease the time necessary to obtain optimal treatment parameters. FUNDING: Deutsche Forschungsgemeinschaft through NeuroCure Clinical Research Center and TRR 295.