Deep Brain Stimulation (DBS) is an effective treatment for movement disorders. Optimizing stimulation parameters remains, however, a trial-and-error process. Datadriven models leveraging Probabilistic Mapping have shown promise in predicting DBS outcomes, yet current studies rely on chronic stimulation data. This study explores the feasibility of using intra-operative stimulation test data for DBS effect prediction. Probabilistic volumes of beneficial and adverse effects were computed from intra-operative stimulation test data of 65 patients (23 with Essential Tremor + 42 with Parkinson’s Disease). A prediction dataset was generated including clinical, morphological, stimulation features along with features derived from probabilistic maps and simulated Volumes of Tissue Activated. Three machine learning models (Adaboost, Support Vector Classifier and Naïve Bayes) were implemented to predict stimulation effects in a classification task. The models were validated in a leave-one-out crossvalidation and their performances were compared. All the developed models were able to predict DBS outcome classes. The best predictive performance was achieved by the Adaboost model with a maximum balanced accuracy of 0.71 on 3 classes. These results show that intra-operative stimulation test data can predict DBS effects with a similar approach and comparable accuracy to post-operative monopolar review data.

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.

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.