Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Brain Stimul. 2022 Sep-Oct;15(5):1139-1152. doi: 10.1016/j.brs.2022.08.010. Epub 2022 Aug 18.
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.
To compute a group-specific MRI template and PSMs for investigating the impact of PSM model parameters.
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.
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.
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.
对深部脑刺激（DBS）患者进行组分析有助于了解和优化运动障碍患者的治疗。概率性刺激图（PSM）常用于分析组织刺激与症状效果之间的相关性，但应用时有不同的方法学变化。
计算特定于群组的 MRI 模板和 PSM，以研究 PSM 模型参数的影响。
分析了 68 例植入小脑下带的特发性震颤患者的头晕改善和发生情况。输入数据包括每个电极接触的最佳参数（筛选）和临床使用的设置。对所有 DBS 设置进行了患者特定的电场模拟（n=488）。将电场转换为用于分析和可视化的群组特定 MRI 模板。不同的比较基于表示发生（N 图）、平均改善（M 图）、加权平均改善（wM 图）和体素-wise t 统计量（p 图）的 PSM。这些图用于研究输入数据（临床/筛选设置）、聚类方法、采样分辨率和加权函数的影响。
筛选或临床设置对 PSM 的影响最大。左右侧 wM 图的平均差异分别为 12.4%和 18.2%。基于 wM 图或 p 图提取的聚类显示体积有明显差异，而定位相似。加权函数对 PSM 的影响较小，除了 wM 图聚类的定位明显移位外。
在创建用于研究解剖结构与 DBS 结果之间关系的 PSM 时，输入数据的分布和聚类方法是最重要的考虑因素。
