Probabilistic analysis of activation volumes generated during deep brain stimulation

Precision of post-operative localization of deep brain stimulation electrodes

Tools for post-operative localization of deep brain stimulation (DBS) electrodes may be of major benefit in the evaluation of the stimulation area. However, little is known about their precision. This study compares 3 different software packages used for DBS electrode localization. T1-weighted MRI images before and after the implantation of the electrodes into the subthalamic nucleus for DBS in 105 Parkinson's disease patients were processed using the pipelines implemented in Lead-DBS, SureTune4, and Brainlab. Euclidean distance between active contacts determined by individual software packages and in repeated processing by the same and by a different operator was calculated. Furthermore, Dice coefficient for overlap of volume of tissue activated (VTA) was determined for Lead-DBS. Medians of Euclidean distances between estimated active contact locations in inter-software package comparison ranged between 1.5 mm and 2 mm. Euclidean distances in within-software package intra- and inter-rater assessments were 0.6-1 mm and 1-1.7 mm, respectively. Median intra- and inter-rater Dice coefficients for VTAs were 0.78 and 0.75, respectively. Since the median distances are close to the size of the target nucleus, any clinical use should be preceded by careful review of the outputs.

Optimal focused ultrasound lesion location in essential tremor

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is an effective treatment for medically refractory essential tremor. We investigate ablation sites and potential tracts associated with optimal tremor control and side effects based on the analysis of 351 cases from three international hospitals. Lesions were segmented on day 1 thin-cut T2 axial images, mapped to standard Montreal Neurological Institute space, and used to derive probabilistic maps and tracts associated with tremor improvement and side effects. Lesioning of a specific subregion within the ventral intermediate nucleus and the cerebellothalamic tract was associated with optimal tremor improvements. Some lesion locations and tracts were associated with differential side effects. Overlaps with the optimal tremor improvement sites accounted for variance in clinical improvements in out-of-sample cases. Efficacy of this location was further confirmed by test-retest cases that underwent two MRgFUS procedures. We identify and validate a target area for optimal tremor control and sites of avoidance associated with side effects.

Evaluation of DBS computational modeling methodologies using in-vivo electrophysiology in Parkinson's disease

Deep brain stimulation (DBS) is an effective therapy for Parkinson's disease (PD) and other neuropsychiatric disorders, but its outcomes vary due to differences in patient selection, electrode placement, and programming. Optimizing DBS parameter settings requires postoperative adjustments through a trial-and-error process, which is complex and time-consuming. As such, researchers have been developing patient-specific computational models to help guide DBS programming. Despite growing interest in image-guided DBS technology, and recent adoption into clinical practice, the direct validation of the prediction accuracy remains limited. The objective of this study was to establish a comparative framework for validating the accuracy of various DBS computational modeling methodologies in predicting the activation of clinically relevant pathways using in vivo measurements from PD patients undergoing subthalamic (STN) DBS surgery. Our prior work assessed the accuracy of driving force (DF) models in native space by predicting activation of the corticospinal/bulbar tract (CSBT) and cortico-subthalamic hyperdirect pathway (HDP) using very short- (<2 ms) and short-latency (2-4 ms) cortical evoked potentials (cEPs). In this study, we extended our previous work by comparing the accuracy of five computational modeling variations for predicting the activation of HDP and CSBT based on three key factors: modeling method (DF vs. Volume of Tissue Activated [VTA]), imaging space (native vs. normative), and anatomical representation (pathway vs. volume). The model performances were quantified using the coefficient of determination (R2) between the cEP amplitudes and percent pathway activation or percent volume (structure) overlap. We compared model accuracy for 11 PD patients. The DF-Native-Pathway model was the most accurate method for quantitatively predicting experimental subcortical pathway activations. Additionally, our analysis showed that using normative brain space, instead of native (i.e., patient-specific) space, significantly diminished the accuracy of model predictions. Although the DF and VTA modeling methods exhibited comparable accuracy for the hyperdirect pathway, they diverged significantly in their predictions for the corticospinal tract. In conclusion, we believe that the choice of methodology should depend on the specific application and the required level of precision in clinical, surgical, or research settings. These findings offer valuable guidance for developing more accurate models, facilitating reliable DBS outcome predictions, and advancing both clinical practice and research.

Statistical Multiscore Functional Atlas Creation for Image-Guided Deep Brain Stimulation

Deep brain stimulation is increasingly performed for patients who suffer from drug-resistant movement disorders. It still remains challenging to determine the optimal electrode contact location to obtain the optimal surgical outcome and simultaneously minimize adverse effects. This paper proposes to construct a new statistical functional atlas to guide electrode contact targeting during deep brain stimulation. The construction of the atlas consists of four main steps: 1) multimodal image segmentation and registration, 2) activation volume modeling, 3) computing and combining multiple functional scores, and 4) generation of multiscore functional atlas. Based on these steps, the statistical functional atlas is created by integrating anatomical information analysis with multiple clinical scores that postoperatively characterize stimulation efficacy (e.g., motor symptom) and adverse effect. We evaluated the created atlas on 40 subthalamic nucleus stimulated parkinsonian patient datasets. The experimental results show that the reproducibility of the created statistical functional atlas was more than 75% in the cross validation. In addition, the motor, neuropsychological, and health scores can be reproduced up to 77%, 82%, and 78%. Compared to the actually implanted electrode position, the atlas predicted and the manually planned electrode position errors were 2.89 mm and 2.38 mm, respectively. The constructed multiscore atlas provides an automatic and accurate electrode targeting strategy that potentially outperforms manually planned approaches.

Imaging-Guided Subthalamic Nucleus Deep Brain Stimulation Programming for Parkinson Disease: A Real-Life Pilot Study

Background and Objectives

Deep brain stimulation (DBS) is a well-established treatment for Parkinson disease (PD), with programming methods continually evolving. This study aimed to compare the efficacy and patient burden between conventional ring-mode programming (CP-RM) and image-guided volume of tissue activated (IG-VTA) programming for subthalamic nucleus (STN) DBS in PD.

Methods

In this retrospective study, patients with PD who underwent STN-DBS between 2011 and 2014 (CP-RM group) and 2019 and 2021 (IG-VTA group) were evaluated. The primary outcome was the improvement in the UPDRS III score from preoperative OFF to postoperative ON state without medication at one-year follow-up. Secondary outcomes included hospital stay duration and programming sessions.

Results

A total of 26 patients were analyzed (IG-VTA: n = 12, CP-RM: n = 14). Both groups showed similar improvements in UPDRS III scores (IG-VTA: 43.62, CP-RM: 41.29). However, the IG-VTA group experienced shorter immediate postoperative hospital stays and fewer hospitalizations after discharge.

Discussion

IG-VTA programming preserved the clinical efficacy of STN-DBS over 1 year and reduced the patient and clinician burden of hospital stay and programming sessions. However, conclusions drawn must consider the limitations of retrospective design, differing time epochs, and evolving clinical practices. Further multicentric and prospective studies are warranted to validate these findings in the evolving field of neurostimulation.

Trial Registration Information

The trial is registered on clinicaltrials.gov (NCT05103072).

Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation

Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions.

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS.

OBJECTIVE

The goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings.

MATERIALS AND METHODS

We used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm).

RESULTS

The recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities.

CONCLUSIONS

Subtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

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.

Neuropathologic changes associated with stereoelectroencephalography depth electrode placement

BACKGROUND

The aim of this study was to detail the neuropathologic changes resulting from the surgical placement of stereoelectroencephalography (SEEG) leads in an initial small group of epilepsy cases and to raise awareness of this iatrogenic pathology, especially to those medical providers who specialize in the care of epilepsy patients.

METHODS

Five consecutive patients who underwent epilepsy resection surgery following SEEG monitoring at OSF Saint Francis Medical Center were included in our report. Resection specimens were examined grossly and entirely submitted for microscopic evaluation by a neuropathologist. Seizure-related pathologies, as well as histologic changes related to SEEG electrode placement, were documented.

RESULTS

The patient cohort included two females and three males, with an age range of 9 to 47 years. Neuropathologic examination revealed one or more seizure-related pathologies in each patient's resection specimen. In addition, all brain resection specimens showed multiple microinfarcts, which appeared to correlate with the placement and size of SEEG electrodes. Patchy leptomeningeal chronic inflammation was also seen in most cases.

CONCLUSIONS

SEEG electrode placement is an effective procedure for determining epileptogenic regions and guiding subsequent resection surgeries in medically refractory epilepsy. Multiple microinfarcts and chronic inflammation are commonly seen in brain resection specimens following SEEG electrode insertion, but studies detailing these iatrogenic histopathologic changes are lacking. The clinical significance and long-term implications of multiple small foci of electrode-induced injury that remain in the patient's brain after resection of the epileptogenic focus are unknown and may provide a welcome area for future study.

Quantifying the Variability Associated with Postoperative Localization of Deep Brain Stimulation Electrodes

INTRODUCTION

Computational models of deep brain stimulation (DBS) have become common tools in clinical research studies that attempt to establish correlations between stimulation locations in the brain and behavioral outcome measures. However, the accuracy of any patient-specific DBS model depends heavily upon accurate localization of the DBS electrodes within the anatomy, which is typically defined via co-registration of clinical CT and MRI datasets. Several different approaches exist for this challenging registration problem, and each approach will result in a slightly different electrode localization. The goal of this study was to better understand how different processing steps (e.g., cost-function masking, brain extraction, intensity remapping) affect the estimate of the DBS electrode location in the brain.

METHODS

No "gold standard" exists for this kind of analysis, as the exact location of the electrode in the living human brain cannot be determined with existing clinical imaging approaches. However, we can estimate the uncertainty associated with the electrode position, which can be used to guide statistical analyses in DBS mapping studies. Therefore, we used high-quality clinical datasets from 10 subthalamic DBS subjects and co-registered their long-term postoperative CT with their preoperative surgical targeting MRI using 9 different approaches. The distances separating all of the electrode location estimates were calculated for each subject.

RESULTS

On average, electrodes were located within a median distance of 0.57 mm (0.49-0.74) of one another across the different registration approaches. However, when considering electrode location estimates from short-term postoperative CTs, the median distance increased to 2.01 mm (1.55-2.78).

CONCLUSIONS

The results of this study suggest that electrode location uncertainty needs to be factored into statistical analyses that attempt to define correlations between stimulation locations and clinical outcomes.

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.

In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control

Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.

Comparison of Anatomical Pathway Models with Tractography Estimates of the Pallidothalamic, Cerebellothalamic, and Corticospinal Tracts

Introduction: Models of structural connectivity in the human brain are typically simulated using tractographic approaches. However, the nonlinear fitting of anatomical pathway atlases to de novo subject brains represents a simpler alternative that is hypothesized to provide more anatomically realistic results. Therefore, the goal of this study was to perform a side-by-side comparison of the streamline estimates generated by either pathway atlas fits or tractographic reconstructions in the same subjects. Methods: Our analyses focused on reconstruction of the corticospinal tract (CST), cerebellothalamic (CBT), and pallidothalamic (PT) pathways using example datasets from the Human Connectome Project (HCP). We used MRtrix3 to explore whole brain, as well as manual seed-to-target, tractography approaches. In parallel, we performed nonlinear fits of an axonal pathway atlas to each HCP dataset using Advanced Normalization Tools (ANTs). Results: The different methods produced notably different estimates for each pathway in each subject. The fitted atlas pathways were highly stereotyped and exhibited low variability in their streamline trajectories. Manual tractography resulted in pathway estimates that generally corresponded with the fitted atlas pathways, but with a higher degree of variability in the individual streamlines. Pathway reconstructions derived from whole-brain tractography exhibited the highest degree of variability and struggled to create anatomically realistic representations for either the CBT or PT pathways. Conclusion: The speed, simplicity, reproducibility, and realism of anatomical pathway model fits makes them an appealing option for some forms of structural connectivity modeling in the human brain. Impact statement Axonal pathway modeling is an important component of deep brain stimulation (DBS) research studies that seek to identify the brain connections that are directly activated by stimulation. The corticospinal tract, cerebellothalamic (CBT), and pallidothalamic (PT) pathways are specifically relevant to the study of subthalamic DBS for the treatment of Parkinson's disease. Our results suggest that anatomical pathway model fits of the CBT and PT pathways to de novo subject brains represent a more anatomically realistic option than tractographic approaches when studying subthalamic DBS.

Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial

INTRODUCTION

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming.

METHODS

We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements.

RESULTS

We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months.

CONCLUSION

MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.

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.

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.

Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

Objective.Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS.Approach.We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths.Main results.The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models.Significance.We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.

Towards biomarker-based optimization of deep brain stimulation in Parkinson's disease patients

Background

Subthalamic deep brain stimulation (DBS) is an established therapy to treat Parkinson's disease (PD). To maximize therapeutic outcome, optimal DBS settings must be carefully selected for each patient. Unfortunately, this is not always achieved because of: (1) increased technological complexity of DBS devices, (2) time restraints, or lack of expertise, and (3) delayed therapeutic response of some symptoms. Biomarkers to accurately predict the most effective stimulation settings for each patient could streamline this process and improve DBS outcomes.

Objective

To investigate the use of evoked potentials (EPs) to predict clinical outcomes in PD patients with DBS.

Methods

In ten patients (12 hemispheres), a monopolar review was performed by systematically stimulating on each DBS contact and measuring the therapeutic window. Standard imaging data were collected. EEG-based EPs were then recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. Linear mixed models were used to assess how well both EPs and image-derived information predicted the clinical data.

Results

Evoked potential peaks at 3 ms (P3) and at 10 ms (P10) were observed in nine and eleven hemispheres, respectively. Clinical data were well predicted using either P3 or P10. A separate model showed that the image-derived information also predicted clinical data with similar accuracy. Combining both EPs and image-derived information in one model yielded the highest predictive value.

Conclusion

Evoked potentials can accurately predict clinical DBS responses. Combining EPs with imaging data further improves this prediction. Future refinement of this approach may streamline DBS programming, thereby improving therapeutic outcomes.

Clinical trial registration

ClinicalTrials.gov, identifier NCT04658641.

Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation-a more immediately translatable approach-targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency and pulse width, resulting in over 1000 different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in MATLAB with an Expected Improvement Plus acquisition function. We examined three different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Regardless of experimental setup, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.

Globus pallidus internus deep brain stimulation evokes resonant neural activity in Parkinson's disease

Globus pallidus internus deep brain stimulation is an established therapy for patients with medication-refractory Parkinson's disease. Clinical outcomes are highly dependent on applying stimulation to precise locations in the brain. However, robust neurophysiological markers are needed to determine the optimal electrode location and to guide postoperative stimulation parameter selection. In this study, we evaluated evoked resonant neural activity in the pallidum as a potential intraoperative marker to optimize targeting and stimulation parameter selection to improve outcomes of deep brain stimulation for Parkinson's disease. Intraoperative local field potential recordings were acquired in 22 patients with Parkinson's disease undergoing globus pallidus internus deep brain stimulation implantation (N = 27 hemispheres). A control group of patients undergoing implantation in the subthalamic nucleus (N = 4 hemispheres) for Parkinson's disease or the thalamus for essential tremor (N = 9 patients) were included for comparison. High-frequency (135 Hz) stimulation was delivered from each electrode contact sequentially while recording the evoked response from the other contacts. Low-frequency stimulation (10 Hz) was also applied as a comparison. Evoked resonant neural activity features, including amplitude, frequency and localization were measured and analysed for correlation with empirically derived postoperative therapeutic stimulation parameters. Pallidal evoked resonant neural activity elicited by stimulation in the globus pallidus internus or externus was detected in 26 of 27 hemispheres and varied across hemispheres and across stimulating contacts within individual hemispheres. Bursts of high-frequency stimulation elicited evoked resonant neural activity with similar amplitudes (P = 0.9) but a higher frequency (P = 0.009) and a higher number of peaks (P = 0.004) than low-frequency stimulation. We identified a 'hotspot' in the postero-dorsal pallidum where stimulation elicited higher evoked resonant neural activity amplitudes (P < 0.001). In 69.6% of hemispheres, the contact that elicited the maximum amplitude intraoperatively matched the contact empirically selected for chronic therapeutic stimulation by an expert clinician after 4 months of programming sessions. Pallidal and subthalamic nucleus evoked resonant neural activity were similar except for lower pallidal amplitudes. No evoked resonant neural activity was detected in the essential tremor control group. Given its spatial topography and correlation with postoperative stimulation parameters empirically selected by expert clinicians, pallidal evoked resonant neural activity shows promise as a potential marker to guide intraoperative targeting and to assist the clinician with postoperative stimulation programming. Importantly, evoked resonant neural activity may also have the potential to guide directional and closed-loop deep brain stimulation programming for Parkinson's disease.

Probabilistic maps for deep brain stimulation - Impact of methodological differences

BACKGROUND

Group analysis of patients with deep brain stimulation (DBS) has the potential to help understand and optimize the treatment of patients with movement disorders. Probabilistic stimulation maps (PSM) are commonly used to analyze the correlation between tissue stimulation and symptomatic effect but are applied with different methodological variations.

OBJECTIVE

To compute a group-specific MRI template and PSMs for investigating the impact of PSM model parameters.

METHODS

Improvement and occurrence of dizziness in 68 essential tremor patients implanted in caudal zona incerta were analyzed. The input data includes the best parameters for each electrode contact (screening), and the clinically used settings. Patient-specific electric field simulations (n = 488) were computed for all DBS settings. The electric fields were transformed to a group-specific MRI template for analysis and visualization. The different comparisons were based on PSMs representing occurrence (N-map), mean improvement (M-map), weighted mean improvement (wM-map), and voxel-wise t-statistics (p-map). These maps were used to investigate the impact from input data (clinical/screening settings), clustering methods, sampling resolution, and weighting function.

RESULTS

Screening or clinical settings showed the largest impacts on the PSMs. The average differences of wM-maps were 12.4 and 18.2% points for the left and right sides respectively. Extracting clusters based on wM-map or p-map showed notable variation in volumes, while positioning was similar. The impact on the PSMs was small from weighting functions, except for a clear shift in the positioning of the wM-map clusters.

CONCLUSION

The distribution of the input data and the clustering method are most important to consider when creating PSMs for studying the relationship between anatomy and DBS outcome.

Patient-specific structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy

OBJECTIVE

Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes.

METHODS

We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort.

RESULTS

Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction.

SIGNIFICANCE

Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.

The Contribution of Subthalamic Nucleus Deep Brain Stimulation to the Improvement in Motor Functions and Quality of Life

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) effectively treats motor symptoms and quality of life (QoL) of advanced and fluctuating early Parkinson's disease. Little is known about the relation between electrode position and changes in symptom control and ultimately QoL.

OBJECTIVES

The relation between the stimulated part of the STN and clinical outcomes, including the motor score of the Unified Parkinson's Disease Rating Scale (UPDRS) and the quality-of-life questionnaire, was assessed in a subcohort of the EARLYSTIM study.

METHODS

Sixty-nine patients from the EARLYSTIM cohort who underwent DBS, with a comprehensive clinical characterization before and 24 months after surgery, were included. Intercorrelations of clinical outcome changes, correlation between the affected functional parts of the STN, and changes in clinical outcomes were investigated. We further calculated sweet spots for different clinical parameters.

RESULTS

Improvements in the UPDRS III and Parkinson's Disease Questionnaire (PDQ-39) correlated positively with the extent of the overlap with the sensorimotor STN. The sweet spots for the UPDRS III (x = 11.6, y = -13.1, z = -6.3) and the PDQ-39 differed (x = 14.8, y = -12.4, z = -4.3) ~3.8 mm.

CONCLUSIONS

The main influence of DBS on QoL is likely mediated through the sensory-motor basal ganglia loop. The PDQ sweet spot is located in a posteroventral spatial location in the STN territory. For aspects of QoL, however, there was also evidence of improvement through stimulation of the other STN subnuclei. More research is necessary to customize the DBS target to individual symptoms of each patient. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Selective activation of central thalamic fiber pathway facilitates behavioral performance in healthy non-human primates

Central thalamic deep brain stimulation (CT-DBS) is an investigational therapy to treat enduring cognitive dysfunctions in structurally brain injured (SBI) patients. However, the mechanisms of CT-DBS that promote restoration of cognitive functions are unknown, and the heterogeneous etiology and recovery profiles of SBI patients contribute to variable outcomes when using conventional DBS strategies,which may result in off-target effects due to activation of multiple pathways. To disambiguate the effects of stimulation of two adjacent thalamic pathways, we modeled and experimentally compared conventional and novel 'field-shaping' methods of CT-DBS within the central thalamus of healthy non-human primates (NHP) as they performed visuomotor tasks. We show that selective activation of the medial dorsal thalamic tegmental tract (DTTm), but not of the adjacent centromedian-parafascicularis (CM-Pf) pathway, results in robust behavioral facilitation. Our predictive modeling approach in healthy NHPs directly informs ongoing and future clinical investigations of conventional and novel methods of CT-DBS for treating cognitive dysfunctions in SBI patients, for whom no therapy currently exists.

Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation

Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.

Translational considerations for the design of untethered nanomaterials in human neural stimulation

Neural stimulation is a powerful tool to study brain physiology and an effective treatment for many neurological disorders. Conventional interfaces use electrodes implanted in the brain. As these are often invasive and have limited spatial targeting, they carry a potential risk of side-effects. Smaller neural devices may overcome these obstacles, and as such, the field of nanoscale and remotely powered neural stimulation devices is growing. This review will report on current untethered, injectable nanomaterial technologies intended for neural stimulation, with a focus on material-tissue interface engineering. We will review nanomaterials capable of wireless neural stimulation, and discuss their stimulation mechanisms. Taking cues from more established nanomaterial fields (e.g., cancer theranostics, drug delivery), we will then discuss methods to modify material interfaces with passive and bioactive coatings. We will discuss methods of delivery to a desired brain region, particularly in the context of how delivery and localization are affected by surface modification. We will also consider each of these aspects of nanoscale neurostimulators with a focus on their prospects for translation to clinical use.

Deep Brain Stimulation for Tremor: Update on Long-Term Outcomes, Target Considerations and Future Directions

Deep brain stimulation (DBS) of the thalamic ventral intermediate nucleus is one of the main advanced neurosurgical treatments for drug-resistant tremor. However, not every patient may be eligible for this procedure. Nowadays, various other functional neurosurgical procedures are available. In particular cases, radiofrequency thalamotomy, focused ultrasound and radiosurgery are proven alternatives to DBS. Besides, other DBS targets, such as the posterior subthalamic area (PSA) or the dentato-rubro-thalamic tract (DRT), may be appraised as well. In this review, the clinical characteristics and pathophysiology of tremor syndromes, as well as long-term outcomes of DBS in different targets, will be summarized. The effectiveness and safety of lesioning procedures will be discussed, and an evidence-based clinical treatment approach for patients with drug-resistant tremor will be presented. Lastly, the future directions in the treatment of severe tremor syndromes will be elaborated.

Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial

Importance

The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy.

Objective

To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access.

Design, Settings, and Participants

This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain.

Interventions

The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care.

Main Outcomes and Measures

The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III.

Results

Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices.

Conclusions and Relevance

This study provides evidence supporting the safety and feasibility of postoperative home health DBS management.

Trial Registration

ClinicalTrials.gov Identifier: NCT02474459.

Analysis of Deep Brain Stimulation Lead Targeting in the Stimulation of Anterior Nucleus of the Thalamus for Epilepsy Clinical Trial

BACKGROUND

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is an effective therapy for patients with drug-resistant focal epilepsy. Best practices for surgical targeting of the ANT can be refined as new information becomes available regarding effective stimulation sites.

OBJECTIVE

To conduct a retrospective analysis of the relationship between outcomes (seizure reduction during year 1) and DBS lead locations in subjects from the SANTÉ pivotal trial (Stimulation of ANT for Epilepsy) based upon recent clinical findings.

METHODS

Postoperative images from SANTÉ subjects (n = 101) were evaluated with respect to lead trajectory relative to defined anatomic landmarks. A qualitative scoring system was used to rate each lead placement for proximity to an identified target region above the junction of the mammillothalamic tract with the ANT. Each subject was assigned a bilateral lead placement score, and these scores were then compared to clinical outcomes.

RESULTS

Approximately 70% of subjects had “good” bilateral lead placements based upon location with respect to the defined target. These subjects had a much higher probability of being a clinical responder (>50% seizure reduction) than those with scores reflecting suboptimal lead placements (43.5% vs 21.9%, P < .05).

CONCLUSION

Consistent with experience from more established DBS indications, our findings and other recent reports suggest that there may be specific sites within the ANT that are associated with superior clinical outcomes. It will be important to continue to evaluate these relationships and the evolution of other clinical practices (eg, programming) to further optimize this therapy.

Stimulation Sweet Spot in Subthalamic Deep Brain Stimulation - Myth or Reality? A Critical Review of Literature

INTRODUCTION

While deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been extensively used for more than 20 years in Parkinson's disease (PD), the optimal area of stimulation to relieve motor symptoms remains elusive.

OBJECTIVE

We aimed at localizing the sweet spot within the subthalamic region by performing a systematic review of the literature.

METHOD

PubMed database was searched for published studies exploring optimal stimulation location for STN DBS in PD, published between 2000 and 2019. A standardized assessment procedure based on methodological features was applied to select high-quality publications. Studies conducted more than 3 months after the DBS procedure, employing lateralized scores and/or stimulation condition, and reporting the volume of tissue activated or the position of the stimulating contact within the subthalamic region were considered in the final analysis.

RESULTS

Out of 439 references, 24 were finally retained, including 21 studies based on contact location and 3 studies based on volume of tissue activated (VTA). Most studies (all VTA-based studies and 13 of the 21 contact-based studies) suggest the superior-lateral STN and the adjacent white matter as the optimal sites for stimulation. Remaining contact-based studies were either inconclusive (5/21), favoured the caudal zona incerta (1/21), or suggested a better outcome of STN stimulation than adjacent white matter stimulation (2/21).

CONCLUSION

Using a standardized methodological approach, our review supports the presence of a sweet spot located within the supero-lateral STN and extending to the adjacent white matter.

Directional Deep Brain Stimulation for Parkinson's Disease: Results of an International Crossover Study With Randomized, Double-Blind Primary Endpoint

OBJECTIVE

Published reports on directional deep brain stimulation (DBS) have been limited to small, single-center investigations. Therapeutic window (TW) is used to describe the range of stimulation amplitudes achieving symptom relief without side effects. This crossover study performed a randomized double-blind assessment of TW for directional and omnidirectional DBS in a large cohort of patients implanted with a DBS system in the subthalamic nucleus for Parkinson's disease.

MATERIALS AND METHODS

Participants received omnidirectional stimulation for the first three months after initial study programming, followed by directional DBS for the following three months. The primary endpoint was a double-blind, randomized evaluation of TW for directional vs. omnidirectional stimulation at three months after initial study programming. Additional data recorded at three- and six-month follow-ups included stimulation preference, therapeutic current strength, Unified Parkinson's Disease Rating Scale (UPDRS) part III motor score, and quality of life.

RESULTS

The study enrolled 234 subjects (62 ± 8 years, 33% female). TW was wider using directional stimulation in 183 of 202 subjects (90.6%). The mean increase in TW with directional stimulation was 41% (2.98 ± 1.38 mA, compared to 2.11 ± 1.33 mA for omnidirectional). UPDRS part III motor score on medication improved 42.4% at three months (after three months of omnidirectional stimulation) and 43.3% at six months (after three months of directional stimulation) with stimulation on, compared to stimulation off. After six months, 52.8% of subjects blinded to stimulation type (102/193) preferred the period with directional stimulation, and 25.9% (50/193) preferred the omnidirectional period. The directional period was preferred by 58.5% of clinicians (113/193) vs. 21.2% (41/193) who preferred the omnidirectional period.

CONCLUSION

Directional stimulation yielded a wider TW compared to omnidirectional stimulation and was preferred by blinded subjects and clinicians.

Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease

BACKGROUND

Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient's stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed.

METHODS

To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient's optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient's preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10-185Hz (total 30-36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant's preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency.

RESULTS

The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants' pGP models indicate a preference at frequencies between 70-110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method.

CONCLUSIONS

These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.

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

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

Electrophysiological signatures predict clinical outcomes after deep brain stimulation of the globus pallidus internus in Meige syndrome

OBJECTIVE

Deep brain stimulation (DBS) of the globus pallidus internus (GPi) has been shown to be a safe and effective alternative therapy for ameliorating medically refractory primary Meige syndrome. However, the associations between DBS target position and surrounding electrophysiological properties as well as patients' clinical outcomes remains largely unknown. In a large number of patients, we investigated electrophysiological features around stimulation targets and explored their roles in predicting clinical outcomes following bilateral GPi-DBS.

METHODS

The locations of DBS active contacts along the long axis of the GPi in a standard space were calculated and compared among three groups with different clinical outcomes. The firing rates of individual neurons within the GPi were calculated for each patient and compared across the three groups.

RESULTS

Compared with the bad group (poor clinical outcome), active contacts in the good group (good clinical outcome) and the best group (best clinical outcome) were located in the more posterior GPi. The average firing rates in the good and best groups were significantly higher than in the bad group, and this difference was pronounced within the ventral GPi. For the bad group, the average firing rates were significantly lower in the ventral than in the dorsal GPi.

CONCLUSIONS

This study suggests that DBS of the posterior GPi may produce better clinical outcomes during primary Meige syndrome treatment and that higher GPi neuronal activity, particularly within the ventral part, can be used as a biomarker to guide DBS electrode implantation during surgery.

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

OBJECTIVE

Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

METHODS

Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

RESULTS

Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

CONCLUSIONS

Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

Sign-specific stimulation 'hot' and 'cold' spots in Parkinson's disease validated with machine learning

Deep brain stimulation of the subthalamic nucleus has become a standard therapy for Parkinson’s disease. Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific Parkinsonian signs when informed by precise locations of stimulation. We studied 275 Parkinson’s disease patients who underwent subthalamic nucleus deep brain stimulation between 2003 and 2018. We selected pre-deep brain stimulation and best available post-deep brain stimulation scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to deep brain stimulation. Volumes of tissue activated were computed and weighted by (i) tremor, (ii) rigidity, (iii) bradykinesia and (iv) axial signs changes. Then, sign-specific sites of optimal (‘hot spots’) and suboptimal efficacy (‘cold spots’) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n = 51 subthalamic nucleus deep brain stimulation patients from another institution). Tremor and rigidity hot spots were largely located outside and dorsolateral to the subthalamic nucleus whereas hot spots for bradykinesia and axial signs had larger overlap with the subthalamic nucleus. Using volume of tissue activated overlap with sign-specific hot and cold spots, support vector machine classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68–98%) when only considering volume of tissue activated overlap with hot spots but was markedly lower (41–72%) when only using cold spots. The model also performed poorly (44–48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using volume of tissue activated overlap with the sign-specific hot and cold spots. In two independent datasets, distinct brain areas could predict sign-specific clinical changes in Parkinson’s disease patients with subthalamic nucleus deep brain stimulation. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement.

Modulation of beta bursts in subthalamic sensorimotor circuits predicts improvement in bradykinesia

No biomarker of Parkinson's disease exists that allows clinicians to adjust chronic therapy, either medication or deep brain stimulation, with real-time feedback. Consequently, clinicians rely on time-intensive, empirical, and subjective clinical assessments of motor behaviour and adverse events to adjust therapies. Accumulating evidence suggests that hypokinetic aspects of Parkinson's disease and their improvement with therapy are related to pathological neural activity in the beta band (beta oscillopathy) in the subthalamic nucleus. Additionally, effectiveness of deep brain stimulation may depend on modulation of the dorsolateral sensorimotor region of the subthalamic nucleus, which is the primary site of this beta oscillopathy. Despite the feasibility of utilizing this information to provide integrated, biomarker-driven precise deep brain stimulation, these measures have not been brought together in awake freely moving individuals. We sought to directly test whether stimulation-related improvements in bradykinesia were contingent on reduction of beta power and burst durations, and/or the volume of the sensorimotor subthalamic nucleus that was modulated. We recorded synchronized local field potentials and kinematic data in 16 subthalamic nuclei of individuals with Parkinson's disease chronically implanted with neurostimulators during a repetitive wrist-flexion extension task, while administering randomized different intensities of high frequency stimulation. Increased intensities of deep brain stimulation improved movement velocity and were associated with an intensity-dependent reduction in beta power and mean burst duration, measured during movement. The degree of reduction in this beta oscillopathy was associated with the improvement in movement velocity. Moreover, the reduction in beta power and beta burst durations was dependent on the theoretical degree of tissue modulated in the sensorimotor region of the subthalamic nucleus. Finally, the degree of attenuation of both beta power and beta burst durations, together with the degree of overlap of stimulation with the sensorimotor subthalamic nucleus significantly explained the stimulation-related improvement in movement velocity. The above results provide direct evidence that subthalamic nucleus deep brain stimulation-related improvements in bradykinesia are related to the reduction in beta oscillopathy within the sensorimotor region. With the advent of sensing neurostimulators, this beta oscillopathy combined with lead location could be used as a marker for real-time feedback to adjust clinical settings or to drive closed-loop deep brain stimulation in freely moving individuals with Parkinson's disease.

Sweetspot Mapping in Deep Brain Stimulation: Strengths and Limitations of Current Approaches

OBJECTIVES

Open questions remain regarding the optimal target, or sweetspot, for deep brain stimulation (DBS) in, for example, Parkinson's disease. Previous studies introduced different methods of mapping DBS effects to determine sweetspots. While having a direct impact on surgical targeting and postoperative programming in DBS, these methods so far have not been compared.

MATERIALS AND METHODS

This study investigated five previously published DBS mapping approaches regarding their potential to correctly identify a predefined target. Methods were investigated in silico in eight different use-case scenarios, which incorporated different types of clinical data, noise, and differences in underlying neuroanatomy. Dice coefficients were calculated to determine the overlap between identified sweetspots and the predefined target. Additionally, out-of-sample predictive capabilities were assessed using the amount of explained variance R² .

RESULTS

The five investigated methods resulted in highly variable sweetspots. Methods based on voxel-wise statistics against average outcomes showed the best performance overall. While predictive capabilities were high, even in the best of cases Dice coefficients remained limited to values around 0.5, highlighting the overall limitations of sweetspot identification.

CONCLUSIONS

This study highlights the strengths and limitations of current approaches to DBS sweetspot mapping. Those limitations need to be taken into account when considering the clinical implications. All future approaches should be investigated in silico before being applied to clinical data.

StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson's Disease

OBJECTIVE

Subthalamic deep brain stimulation (DBS) is an established therapy for Parkinson's disease. Connectomic DBS modeling is a burgeoning subfield of research aimed at characterizing the axonal connections activated by DBS. This article describes our approach and methods for evolving the StimVision software platform to meet the technical demands of connectomic DBS modeling in the subthalamic region.

MATERIALS AND METHODS

StimVision v2 was developed with Visualization Toolkit (VTK) libraries and integrates four major components: 1) medical image visualization, 2) axonal pathway visualization, 3) electrode positioning, and 4) stimulation calculation.

RESULTS

StimVision v2 implemented two key technological advances for connectomic DBS analyses in the subthalamic region. First was the application of anatomical axonal pathway models to patient-specific DBS models. Second was the application of a novel driving-force method to estimate the response of those axonal pathways to DBS. Example simulations with directional DBS electrodes and clinically defined therapeutic DBS settings are presented to demonstrate the general outputs of StimVision v2 models.

CONCLUSIONS

StimVision v2 provides the opportunity to evaluate patient-specific axonal pathway activation from subthalamic DBS using anatomically detailed pathway models and electrically detailed electric field distributions with interactive adjustment of the DBS electrode position and stimulation parameter settings.

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.

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (n_total = 482 patients; n_{Parkinson disease} = 303; n_dystonia = 64; n_tremor = 39; n_{treatment-resistant depression/anorexia nervosa} = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426-443.

Atlas-independent, N-of-1 tissue activation modeling to map optimal regions of subthalamic deep brain stimulation for Parkinson disease

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Neuroanatomical variations among patients are obscured in atlas-based VTA modeling.

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N-of-1 neuroanatomical and VTA modeling enables patient-level precision.

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Mean optimal stimulation is dorsomedial to the STN, near its posterior half.

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Individual VTAs deviate from optimal stimulation sites to varying degrees.

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Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap.

Background

Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement.

Methods

The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model.

Results

Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA.

Conclusion

Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.

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

OBJECTIVE

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

APPROACH

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

MAIN RESULTS

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

SIGNIFICANCE

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

Deep brain stimulation: Imaging on a group level

Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is under investigation for treatment in a growing number of other brain diseases. It has been shown that exact electrode placement crucially affects the efficacy of DBS and this should be considered when investigating novel indications or DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different centers, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson's disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. The manuscript is accompanied by a walkthrough tutorial which allows users to reproduce all main results presented here. All data and code needed to reproduce results are openly available.

Non-motor outcomes depend on location of neurostimulation in Parkinson's disease

Deep brain stimulation of the subthalamic nucleus is an effective and established therapy for patients with advanced Parkinson's disease improving quality of life, motor symptoms and non-motor symptoms. However, there is a considerable degree of interindividual variability for these outcomes, likely due to variability in electrode placement and stimulation settings. Here, we present probabilistic mapping data from a prospective, open-label, multicentre, international study to investigate the influence of the location of subthalamic nucleus deep brain stimulation on non-motor symptoms in patients with Parkinson's disease. A total of 91 Parkinson's disease patients undergoing bilateral deep brain stimulation of the subthalamic nucleus were included, and we investigated NMSScale, NMSQuestionnaire, Scales for Outcomes in Parkinson's disease-motor examination, -activities of daily living, and -motor complications, and Parkinson's disease Questionnaire-8 preoperatively and at 6-month follow-up after surgery. Leads were localized in standard space using the Lead-DBS toolbox and individual volumes of tissue activated were calculated based on clinical stimulation settings. Probabilistic stimulation maps and non-parametric permutation statistics were applied to identify voxels with significant above or below average improvement for each scale and analysed using the DISTAL atlas. All outcomes improved significantly at follow-up. Significant spatial distribution patterns of neurostimulation were observed for NMSScale total score and its mood/apathy and attention/memory domains. For both domains, voxels associated with below average improvement were mainly located dorsal to the subthalamic nucleus. In contrast, above average improvement for mood/apathy was observed in the ventral border region of the subthalamic nucleus and in its sensorimotor subregion and for attention/memory in the associative subregion. A trend was observed for NMSScale sleep domain showing voxels with above average improvement located ventral to the subthalamic nucleus. Our study provides evidence that the interindividual variability of mood/apathy, attention/memory, and sleep outcomes after subthalamic nucleus deep brain stimulation depends on the location of neurostimulation. This study highlights the importance of holistic assessments of motor and non-motor aspects of Parkinson's disease to tailor surgical targeting and stimulation parameter settings to patients' personal profiles.

The impact of modern-day neuroimaging on the field of deep brain stimulation

PURPOSE OF REVIEW

Deep brain stimulation (DBS) is an established but growing treatment option for multiple brain disorders. Over the last decade, electrode placement and their effects were increasingly analyzed with modern-day neuroimaging methods like spatial normalization, fibertracking, or resting-state functional MRI. Similarly, specialized basal ganglia MRI sequences were introduced and imaging at high field strengths has become increasingly popular.

RECENT FINDINGS

To facilitate the process of precise electrode localizations, specialized software pipelines were introduced. By those means, DBS targets could recently be refined and significant relationships between electrode placement and clinical improvement could be shown. Furthermore, by combining electrode reconstructions with network imaging methods, relationships between electrode connectivity and clinical improvement were investigated. This led to a broad series of imaging-based insights about DBS that are reviewed in the present work.

SUMMARY

The reviewed literature makes a strong case that brain imaging plays an increasingly important role in DBS targeting and programming. Furthermore, brain imaging will likely help to better understand the mechanism of action of DBS.

Latency of subthalamic nucleus deep brain stimulation-evoked cortical activity as a potential biomarker for postoperative motor side effects

OBJECTIVE

Here, we investigate whether cortical activation predicts motor side effects of deep brain stimulation (DBS) and whether these potential biomarkers have utility under general anesthesia.

METHODS

We recorded scalp potentials elicited by DBS during surgery (n = 11), both awake and under general anesthesia, and in an independent ambulatory cohort (n = 8). Across a range of stimulus configurations, we measured the amplitude and timing of short- and long-latency response components and linked them to motor side effects.

RESULTS

Regardless of anesthesia state, in both cohorts, DBS settings with capsular side effects elicited early responses with peak latencies clustering at <1 ms. This early response was preserved under anesthesia in all participants (11/11). In contrast, the long-latency components were suppressed completely in 6/11 participants. Finally, the latency of the earliest response could predict the presence of postoperative motor side effects both awake and under general anesthesia (84.8% and 75.8% accuracy, awake and under anesthesia, respectively).

CONCLUSION

DBS elicits short-latency cortical activation, both awake and under general anesthesia, which appears to reveal interactions between the stimulus and the corticospinal tract.

SIGNIFICANCE

Short-latency evoked cortical activity can potentially be used to aid both DBS lead placement and post-operative programming.