Deep brain stimulation (DBS) is a therapy for Parkinson's disease (PD) and essential tremor (ET). The mechanism of action of DBS is still incompletely understood. Retrospective group analysis of intra-operative data recorded from ET patients implanted in the ventral intermediate nucleus of the thalamus (Vim) is rare. Intra-operative stimulation tests generate rich data and their use in group analysis has not yet been explored. Objective. To implement, evaluate, and apply a group analysis workflow to generate probabilistic stimulation maps (PSMs) using intra-operative stimulation data from ET patients implanted in Vim. Approach. A group-specific anatomical template was constructed based on the magnetic resonance imaging scans of 6 ET patients and 13 PD patients. Intra-operative test data (total: n = 1821) from the 6 ET patients was analyzed: patient-specific electric field simulations together with tremor assessments obtained by a wrist-based acceleration sensor were transferred to this template. Occurrence and weighted mean maps were generated. Voxels associated with symptomatic response were identified through a linear mixed model approach to form a PSM. Improvements predicted by the PSM were compared to those clinically assessed. Finally, the PSM clusters were compared to those obtained in a multicenter study using data from chronic stimulation effects in ET. Main results. Regions responsible for improvement identified on the PSM were in the posterior sub-thalamic area (PSA) and at the border between the Vim and ventro-oral nucleus of the thalamus (VO). The comparison with literature revealed a center-to-center distance of less than 5 mm and an overlap score (Dice) of 0.4 between the significant clusters. Our workflow and intra-operative test data from 6 ET-Vim patients identified effective stimulation areas in PSA and around Vim and VO, affirming existing medical literature. Significance. This study supports the potential of probabilistic analysis of intra-operative stimulation test data to reveal DBS's action mechanisms and to assist surgical planning.

Deep brain stimulation (DBS) is an established method for symptom relief in movement disorders like Parkinson’s disease, essential tremor (ET), and dystonia. The therapy is based on implanting an electrode with four contacts in the deep brain structures where it provides electrical stimulation, mainly impacting the nerve tracts. Despite the evidence of DBS effectiveness, there are still questions regarding the optimal position of stimulation. With new technology, the possibility to customize the stimulation increases, which makes the programming session for each patient more complicated and tedious.

Different computational models have been developed to estimate the anatomical impact of stimulation. Patient‐specific electric field simulations can be used to estimate the spatial extent of the stimulation and superimpose on patient magnetic resonance imaging (MRI) for anatomical analysis. MRI weighted with water diffusion can be used for reconstructions of nerve tracts, a process called tractography. Tractography utilizes the fact that water can move unrestricted along the nerve trajectories, but the diffusion is restricted in the perpendicular direction, i.e., the diffusion is anisotropic. For tremor, the dentato‐rubro‐thalamic tract (DRT) has gained interest.

The electric conductivity has corresponding anisotropic characteristics as water diffusion in white brain tissue (nerve tracts). Diffusion MRI can therefore also be used to improve patientspecific simulations by including structure information, i.e., anisotropy. In this thesis, both a workflow for combining patient‐specific simulations with tractography of the DRT and a method for expanding the simulations with anisotropy were developed (Paper I). This was done using four patients with ET. The results show that including anisotropy will impact the simulation result in regions of dense nerve tracts (Paper I‐II). For the tractography, all patients’ estimated stimulation region intersected with the reconstructed DRT.

To analyze the optimal location for stimulation, group analysis is required. This can be achieved by combining the electric field simulations with the clinical effect to create probabilistic stimulation maps (PSM). Different methods of creating these maps have been presented in the literature, and this thesis includes developing a workflow for PSM computation and evaluating the effect of different method variations (Paper III‐V). The result shows that the number of simulations (Paper V), type of input data, and choice of clustering method for defining the stimulation effect influence the PSMs the most (Paper III‐IV). Other possible improvements include weighting functions and computing at a high spatial resolution but results in a small to negligible impact on the PSM (Paper IV).

In summary, two different workflows were developed in this thesis. One for anisotropic patient‐specific electric field simulations in combination with tractography reconstruction and one for group analysis using PSMs. The first part shows the feasibility of combining patientspecific simulations and tractography reconstruction of DRT. It also concludes that anisotropy impacts the electric field simulations if the DBS lead is implanted close to a larger nerve tract. The second part highlights the impact of different parameters when creating PSMs, where the number of patients, type of input data, and choice of clustering method should be carefully evaluated when designing a new study. In the future, these results can be used to develop models for predicting the effect of DBS in new patients. Predictive models can be a useful tool to aid the programming session and thereby ease the burden on both patients and healthcare.

Probabilistic stimulation maps of deep brain stimulation (DBS) effect based on voxel-wise statistics (p-maps) have increased in literature over the last decade. These p-maps require correction for Type-1 errors due to multiple testing based on the same data. Some analyses do not reach overall significance, and this study aims to evaluate the impact of sample size on p-map computation. A dataset of 61 essential tremor patients treated with DBS was used for the investigation. Each patient contributed with four stimulation settings, one for each contact. From the dataset, 5 to 61 patients were randomly sampled with replacement for computation of p-maps and extraction of high- and low-improvement volumes. For each sample size, the process was iterated 20 times with new samples generating in total 1140 maps. The overall p-value corrected for multiple comparisons, significance volumes, and dice coefficients (DC) of the volumes within each sample size were evaluated. With less than 30 patients (120 simulations) in the sample, the variation in overall significance was larger and the median significance volumes increased with sample size. Above 120 simulations, the trends stabilize but present some variations in cluster location, with a highest median DC of 0.73 for n = 57. The variation in location was mainly related to the region between the high- and low-improvement clusters. In conclusion, p-maps created with small sample sizes should be evaluated with caution, and above 120 simulations in single-center studies are probably required for stable results.

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

Objective To evaluate the effects of bilateral caudal zona incerta (cZi) deep brain stimulation (DBS) for Parkinsons disease (PD) one year after surgery and to create anatomical improvement maps based on patient-specific simulation of the electric field. Materials and Methods We report the one-year results of bilateral cZi-DBS in 15 patients with PD. Patients were evaluated on/off medication and stimulation using the Unified Parkinsons Disease Rating Scale (UPDRS). Main outcomes were changes in motor symptoms (UPDRS-III) and quality of life according to Parkinsons Disease Questionnaire-39 (PDQ-39). Secondary outcomes included efficacy profile according to sub-items of UPDRS-III, and simulation of the electric field distribution around the DBS lead using the finite element method. Simulations from all patients were transformed to one common magnetic resonance imaging template space for creation of improvement maps and anatomical evaluation. Results Median UPDRS-III score off medication improved from 40 at baseline to 21 on stimulation at one-year follow-up (48%, p < 0.0005). PDQ-39 summary index did not change but the subdomains activities of daily living (ADL) and stigma improved (25%, p < 0.03 and 75%, p < 0.01), whereas communication worsened (p < 0.03). For UPDRS-III sub-items, stimulation alone reduced median tremor score by 9 points, akinesia by 3, and rigidity by 2 points at one-year follow-up in comparison to baseline (90%, 25%, and 29% respectively, p < 0.01). Visual analysis of the anatomical improvement maps based on simulated electrical fields showed no evident relation with the degree of symptom improvement and neither did statistical analysis show any significant correlation. Conclusions Bilateral cZi-DBS alleviates motor symptoms, especially tremor, and improves ADL and stigma in PD patients one year after surgery. Improvement maps may be a useful tool for visualizing the spread of the electric field. However, there was no clear-cut relation between anatomical location of the electric field and the degree of symptom relief.

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 1/4 488) 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-statis-tics (p-map). These maps were used to investigate the impact from input data (clinical/screening set-tings), 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. (C) 2022 The Authors. Published by Elsevier Inc.

Deep brain stimulation (DBS) is an intervention used for several neurological conditions such as Parkinson’s disease. To evaluate the clinical response in relation to anatomical location, electric field simulation using the finite element method is commonly used. The models presented in different studies are varying in complexity and this study aims to evaluate the effect of including anisotropy in the tissue model using homogenous tissue with varying level of anisotropy both parallel and perpendicular to the DBS lead. As a benchmark, data from one patient was included and simulations was performed in zona incerta (Zi) and the internal capsule (IC). The parameters investigated were impedance, volume within the 0.2 V/mm isosurface, radial and longitudinal expansion as well as visual representation of the isosurface. The investigations show that both the impedance and volume are increasing with increasing anisotropy together with the electric field isosurface in the principal direction of the anisotropy. When comparing different stimulation modes, current control (CC) stimulation had a steeper increase with increasing anisotropy for all parameters compared to voltage control (VC) stimulation. This could be due to a joint effect of the anisotropy and the increasing impedance. The result from the patient simulations are in the anisotropy range where simulations from the homogenous models starts to have a higher slope for all parameters. This indicates that including anisotropy in computer models will be of importance in areas of high anisotropy.

Objective

Deep brain stimulation (DBS) in zona incerta (Zi) is used for symptom alleviation in essential tremor (ET). Zi is positioned along the dentato-rubro-thalamic tract (DRT). Electric field simulations with the finite element method (FEM) can be used for estimation of a volume where the stimulation affects the tissue by applying a fixed isolevel (V_DBS). This work aims to develop a workflow for combined patient-specific electric field simulation and white matter tracing of the DRT, and to investigate the influence on the V_DBS from different brain tissue models, lead design and stimulation modes. The novelty of this work lies in the combination of all these components.

Method

Patients with ET were implanted in Zi (lead 3389, n = 3, voltage mode; directional lead 6172, n = 1, current mode). Probabilistic reconstruction from diffusion MRI (dMRI) of the DRT (n = 8) was computed with FSL Toolbox. Brain tissue models were created for each patient (two homogenous, one heterogenous isotropic, one heterogenous anisotropic) and the respective V_DBS (n = 48) calculated from the Comsol Multiphysics FEM simulations. The DRT and V_DBS were visualized with 3DSlicer and superimposed on the preoperative T2 MRI, and the common volumes calculated. Dice Coefficient (DC) and level of anisotropy were used to evaluate and compare the brain models.

Result

Combined patient-specific tractography and electric field simulation was designed and evaluated, and all patients showed benefit from DBS. All V_DBS overlapped the reconstructed DRT. Current stimulation showed prominent difference between the tissue models, where the homogenous grey matter deviated most (67 < DC < 69). Result from heterogenous isotropic and anisotropic models were similar (DC > 0.95), however the anisotropic model consistently generated larger volumes related to a greater extension of the electric field along the DBS lead. Independent of tissue model, the steering effect of the directional lead was evident and consistent.

Conclusion

A workflow for patient-specific electric field simulations in combination with reconstruction of DRT was successfully implemented. Accurate tissue classification is essential for electric field simulations, especially when using the current control stimulation. With an accurate targeting and tractography reconstruction, directional leads have the potential to tailor the electric field into the desired region.