Institut für Medizintechnik und Medizininformatik

Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/23

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Autor:in: search.filters.author.Blomstedt, PatricHochschule: search.filters.hochschule.Hochschule für Life Sciences

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  • Vorschaubild
    Publikation
    How sample size impacts probabilistic stimulation maps in deep brain stimulation
    (MDPI, 03.05.2023) Nordin, Teresa; Blomstedt, Patric; Hemm-Ode, Simone; Wårdell, Karin
    Probabilistic stimulation maps of deep brain stimulation (DBS) effect based on voxel-wise statistics (p-maps) have increased in literature over the last decade. These p-maps require correction for Type-1 errors due to multiple testing based on the same data. Some analyses do not reach overall significance, and this study aims to evaluate the impact of sample size on p-map computation. A dataset of 61 essential tremor patients treated with DBS was used for the investigation. Each patient contributed with four stimulation settings, one for each contact. From the dataset, 5 to 61 patients were randomly sampled with replacement for computation of p-maps and extraction of high- and low-improvement volumes. For each sample size, the process was iterated 20 times with new samples generating in total 1140 maps. The overall p-value corrected for multiple comparisons, significance volumes, and dice coefficients (DC) of the volumes within each sample size were evaluated. With less than 30 patients (120 simulations) in the sample, the variation in overall significance was larger and the median significance volumes increased with sample size. Above 120 simulations, the trends stabilize but present some variations in cluster location, with a highest median DC of 0.73 for n = 57. The variation in location was mainly related to the region between the high- and low-improvement clusters. In conclusion, p-maps created with small sample sizes should be evaluated with caution, and above 120 simulations in single-center studies are probably required for stable results.
    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
    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