Deep Brain Stimulation Atlases in Movement Disorders : from Patient-Specific to Group Analysis

Abstract: deep brain stimulation (DBS) is an established method for symptom control in movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). The treatment consists in delivering electrical stimulation in the deep brain via multi-contact electrodes. The potential for improving quality of life is important and the precision of the electrode implantation is crucial. Despite the success of the therapy, mechanisms of action of DBS are still an intense topic of study. One promising approach is to analyze the effect of stimulation relative to the anatomy. In the clinical process, the effect of stimulation on the symptoms is evaluated at different stages, sometimes during surgery (intraoperative) and systematically in the months following implantation (postoperative). This data can be combined with magnetic resonance imaging (MRI) acquired as part of the clinical routine for individuals or groups of patients to create probabilistic stimulation maps (PSMs). The work in this thesis aimed at developing image and data processing workflows to analyze and visualize DBS outcome in the ventro-intermediate nucleus of the thalamus (Vim) and the Zona Incerta (Zi) which are the two typical ET targets. This was done first in a patient-specific manner, and then at a group level using data collected intra- and postoperatively. Computer electrical field modeling was used to estimate the extent of stimulation within brain tissue and to compare intraoperative and postoperative stimulation (Paper I). The simulation method was then applied to clinical data, combining quantitative tremor assessment collected using acceleration sensors during intraoperative testing in the Vim, to create patient-specific stimulation maps based on high resolution objective data (Paper II). Stimulation maps constitute a partial answer to the aim by providing a visual summary of individual patients’ stimulation tests that can be useful in the clinic. In order to summarize several patients’ data, neuroimaging workflows were developed, and a range of non-linear image registration tools were evaluated to create group-specific anatomical templates from MRI using data from 19 patients (Paper III). The normalization workflow was improved and the settings optimized, resulting in the creation of an anatomical atlas defining the outline of 58 structures of the deep brain (Paper IV). The normalization method was transferred to a second group of 77 patients with postoperative stimulation tests in Zi, and combined with patient specific electric field modeling to create PSMs. The influence of different methodological choices such as input data type and voxel clustering method on the resulting maps were investigated (Paper V). The deep brain atlas from the first group was then combined with electric field simulations and the acceleration-sensor-based quantitative tremor assessment. A stimulation atlas based on intraoperative test data in Vim was created (Paper VI) to evaluate of the potential of intraoperative test stimulation for PSM creation.In conclusion, reproducible pipelines for the generation of group-specific brain templates and high precision PSMs were developed and applied to two different patient groups. The template created for one group resulted in a deep brain atlas defining 58 anatomical structures. The PSMs created for both groups combine group-specific MRI templates, patient-specific electric field simulation and symptom assessment for two common DBS targets that are challenging to locate. The PSMs have the potential to improve the understanding of the mechanisms of action, support clinical planning and aid follow-up by providing visualizations of the therapeutic effect of stimulation in relation to anatomy. In the future, the pipeline will be applied to more patients and diseases. The resulting PSMs will be integrated in a visualization tool for intuitive data exploration and used to predict programming settings.

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