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

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

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

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

Influence of different spike sorting algorithms on the detection of single unit spiking activity in thesubthalamic nucleus of patients with Parkinson’s disease

Objective: To determine the most suitable spike sorting algorithm for intraoperatively recordedextracellular currents in the subthalamic nucleus (STN) of patients with Parkinson’s disease (PD).Background: In PD the STN may play an important role in the generation of pathological oscillatoryactivity within the basal ganglia-cortex loop. Extracellular currents recorded from the STN of PDpatients, obtained during Deep Brain Stimulation surgery can provide important information aboutpathological spiking activity of STN neurons. A challenge in analyzing neural spike data is to assigndetected spike to individual neurons that contribute to the extracellularly recorded activity. To thisend, a group of algorithms commonly referred to as "spike sorting" is used. Today, numerous spikesorting algorithms are available, partly side-by-side in commercial spike sorting packages. However, itis unclear to which degree different algorithms yield similar sorting results.Methods: Spiking activity was recorded intraoperatively at multiple sites within the STN-area of 3awake PD patients. The recorded time series were sorted ("Plexon Offline-Sorter") by 2 semi-automatic (template-based (TB) and K-Means) and by 4 automatic algorithms (K-Means, Valley-Seeking (VS), standard- and t-distribution Expectation-Maximization). We took a multiple validationapproach by comparing the spike times in the detected single units (SU) to determine how thesorting procedure influences the subsequent spike train analysis.Results: Preliminary results demonstrated a wide variability in the number of detected SU betweenthe 6 sorting algorithms (26-62 SU in 21 channels). Additionally, the analysis of the spike trainsstatistics revealed a large variability in the inter-spike interval (between 52 +/-38 and 321 +/- 778(mean +/-SD in ms)) and the coefficient of variation (between 0.61 and 18.91). In summary, thoughnot optimal, TB- and VS-algorithms proved to be the most conservative among the sorting methodsinvestigated.Conclusion: Our results strongly argue for the need of a standardized validation procedure for spikesorting algorithms based on ground-truth data. Moreover, to ensure reproducibility of results andenable scientists to understand differences between results obtained from different experiments, adetailed description of spike sorting procedure becomes a necessity

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