StimFit — A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming

Background: Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. Objective: We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. Methods: Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. Results: Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10-10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. Conclusion: We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future

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

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field

StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming

Background Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. Objective We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. Methods Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. Results Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10(-10)) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. Conclusion We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ

Temporal Stability of Lead Orientation in Directional Deep Brain Stimulation

Background: Directional deep brain stimulation (DBS) enlarges the therapeutic window by increasing side-effect thresholds and improving clinical benefits. To determine the optimal stimulation settings and interpret clinical observations, knowledge of the lead orientation in relation to the patient's anatomy is required. Objective: To determine if directional leads remain in a fixed orientation after implantation or whether orientation changes over time. Method: Clinical records of 187 patients with directional DBS electrodes were screened for CT scans in addition to the routine postoperative CT. The orientation angle of each electrode at a specific point in time was reconstructed from CT artifacts using the DiODe algorithm implemented in Lead-DBS. The orientation angles over time were compared with the originally measured orientations from the routine postoperative CT. Results: Multiple CT scans were identified in 18 patients and the constancy of the orientation angle was determined for 29 leads at 48 points in time. The median time difference between the observations and the routine postoperative CT scan was 82 (range 1-811) days. The mean difference of the orientation angles compared to the initial measurement was -1.1 +/- 3.9 degrees (range -7.6 to 8.7 degrees). Linear regression showed no relevant drift of the absolute value of the orientation angle over time (0.8 degrees/year, adjusted R-2: 0.040, p = 0.093). Conclusion: The orientation of directional leads was stable and showed no clinically relevant changes either in the first weeks after implantation or over longer periods of time

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging

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Probabilistic mapping of deep brain stimulation effects in essential tremor

Objective: To create probabilistic stimulation maps (PSMs) of deep brain stimulation (DBS) effects on tremor suppression and stimulation-induced side-effects in patients with essential tremor (ET). Method: Monopolar reviews from 16 ET-patients which consisted of over 600 stimulation settings were used to create PSMs. A spherical model of the volume of neural activation was used to estimate the spatial extent of DBS for each setting. All data was pooled and voxel-wise statistical analysis as well as nonparametric permutation testing was used to confirm the validity of the PSMs. Results: PSMs showed tremor suppression to be more pronounced by stimulation in the zona incerta (ZI) than in the ventral intermediate nucleus (VIM). Paresthesias and dizziness were most commonly associated with stimulation in the ZI and surrounding thalamic nuclei. Discussion: Our results support the assumption, that the ZI might be a very effective target for tremor suppression. However stimulation inside the ZI and in its close vicinity was also related to the occurrence of stimulation-induced side-effects, so it remains unclear whether the VIM or the ZI is the overall better target. The study demonstrates the use of PSMs for target selection and evaluation. While their accuracy has to be carefully discussed, they can improve the understanding of DBS effects and can be of use for other DBS targets in the therapy of neurological or psychiatric disorders as well. Furthermore they provide a priori information about expected DBS effects in a certain region and might be helpful to clinicians in programming DBS devices in the future

Potentials and Limitations of Directional Deep Brain Stimulation: A Simulation Approach

Background: Directional leads are increasingly used in deep brain stimulation. They allow shaping the electrical field in the axial plane. These new possibilities increase the complexity of programming. Thus, optimized programming approaches are needed to assist clinical testing and to obtain full clinical benefit. Objectives: This simulation study investigates to what extent the electrical field can be shaped by directional steering to compensate for lead malposition. Method: Binary volumes of tissue activated (VTA) were simulated, by using a finite element method approach, for different amplitude distributions on the three directional electrodes. VTAs were shifted from 0 to 2 mm at different shift angles with respect to the lead orientation, to determine the best compensation of a target volume. Results: Malpositions of 1 mm can be compensated with the highest gain of overlap with directional leads. For larger shifts, an improvement of overlap of 10-30% is possible, depending on the stimulation amplitude and shift angle of the lead. Lead orientation and shift determine the amplitude distribution of the electrodes. Conclusion: To get full benefit from directional leads, both the shift angle as well as the shift to target volume are required to choose the correct amplitude distribution on the electrodes. Current directional leads have limitations when compensating malpositions >1 mm; however, they still outperform conventional leads in reducing overstimulation. Further, their main advantage probably lies in the reduction of side effects. Databases like the one from this simulation could serve for optimized lead programming algorithms in the future