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dc.contributor.authorNordin, Teresa
dc.contributor.authorVogel, Dorian
dc.contributor.authorOsterlund, Erik
dc.contributor.authorJohansson, Johannes
dc.contributor.authorFytagoridis, Anders
dc.contributor.authorBlomstedt, Patric
dc.contributor.authorHemm-Ode, Simone
dc.contributor.authorWardell, Karin
dc.date.accessioned2022-10-13T12:25:05Z
dc.date.available2022-10-05T10:01:45Z
dc.date.available2022-10-13T12:25:05Z
dc.date.issued2022
dc.identifier.issn1935861X
dc.identifier.doi10.1016/j.brs.2022.08.010
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/33904
dc.identifier.urihttp://dx.doi.org/10.26041/fhnw-4327
dc.description.abstractBackground 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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofBrain Stimulationen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.accessRightsAnonymous*
dc.subjectDeep brain stimulation (DBS)en_US
dc.subjectFinite element method (FEM)en_US
dc.subjectElectric field simulationen_US
dc.subjectEssential tremoren_US
dc.subjectImprovement mapsen_US
dc.subjectSide effectsen_US
dc.subjectMRI templateen_US
dc.subject.ddc600 - Technik, Medizin, angewandte Wissenschaftenen_US
dc.subject.ddc500 - Naturwissenschaftenen_US
dc.titleProbabilistic maps for deep brain stimulation – Impact of methodological differencesen_US
dc.type01A - Beitrag in wissenschaftlicher Zeitschrift*
dc.volume15en_US
dc.issue5en_US
fhnw.publicationStatePublisheden_US
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publicationen_US
fhnw.InventedHereYesen_US
fhnw.pagination1139-1152en_US
fhnw.IsStudentsWorknoen_US
fhnw.openAccessCategoryGolden_US


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