Computational Models in Deep Brain Stimulation : Patient‐Specific Simulations, Tractography, and Group Analysis

Abstract: 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.