Hemm-Ode, Simone

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Hemm-Ode
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Simone
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Hemm-Ode, Simone

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Autor:in: Vogel, Dorian × Item-Typ: Publikation × Thema: 500 - Naturwissenschaften ×

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  • Vorschaubild
    Publikation
    Deep brain stimulation: emerging tools for simulation, data analysis, and visualization
    (Frontiers, 11.04.2022) Wårdell, Karin; Nordin, Teresa; Zsigmond, Peter; Westin, Carl-Fredrik; Hariz, Marwan; Vogel, Dorian; Hemm-Ode, Simone [in: Frontiers in Neuroscience]
    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.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Probabilistic maps for deep brain stimulation – Impact of methodological differences
    (Elsevier, 2022) Nordin, Teresa; Vogel, Dorian; Osterlund, Erik; Johansson, Johannes; Fytagoridis, Anders; Blomstedt, Patric; Hemm-Ode, Simone; Wardell, Karin [in: Brain Stimulation]
    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 = 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-statistics (p-map). These maps were used to investigate the impact from input data (clinical/screening settings), 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.
    01A - Beitrag in wissenschaftlicher Zeitschrift