Bucciarelli Vittoria, Vogel Dorian, Nordin Teresa, Stawiski Marc, Coste Jérôme, Lemaire Jean-Jacques, Guzman Raphael, Hemm Simone
Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Hofackerstrasse 30, Muttenz, Switzerland; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Hofackerstrasse 30, Muttenz, Switzerland.
Neuroimage Clin. 2025 Jun 7;47:103820. doi: 10.1016/j.nicl.2025.103820.
Deep Brain Stimulation (DBS) is an established therapy for movement and neuropsychiatric disorders. Identifying brain regions (Probabilistic Sweet Spots, PSS) linked with the greatest symptom improvement is crucial for refining pre-operative targeting and post-operative programming. Probabilistic stimulation mapping is a powerful data-driven tool to delineate these regions. However, the chosen statistical methods can influence the identified PSS. A comprehensive evaluation of their impact is lacking in DBS research. The present study compares the PSS generated with four voxel-wise statistical approaches - t-test, Wilcoxon test, Linear Mixed Model, and Bayesian t-test - with the aim of assessing their influence on computed results on the same dataset. Intra-operative stimulation test data of 23 Essential Tremor (ET) patients was used to run patient-specific electric field simulations and to generate PSS in a group-specific anatomical template space. The PSS for the different statistical tests were first compared in terms of size and topography. Then, their correlation with clinical improvement was calculated in a leave-one-out cross-validation scheme and PSS consistency across datasets with different compositions was assessed. Our findings emphasize the impact of statistical test selection on both the anatomical location and volume of the extracted PSS, highlighting the importance of careful methodological choices in future DBS mapping studies. The Bayesian t-test and a voxel-wise application of nonparametric permutation testing, introduced for the first time in DBS research, showed promising results in identifying PSS representative of improvement and exhibited robustness to variations in the dataset.
深部脑刺激（DBS）是一种用于治疗运动和神经精神疾病的既定疗法。识别与症状改善最大相关的脑区（概率性最佳靶点，PSS）对于优化术前靶点定位和术后程控至关重要。概率性刺激映射是一种强大的数据驱动工具，用于描绘这些区域。然而，所选择的统计方法会影响所识别的PSS。在DBS研究中缺乏对其影响的全面评估。本研究比较了用四种体素级统计方法（t检验、威尔科克森检验、线性混合模型和贝叶斯t检验）生成的PSS，目的是评估它们对同一数据集计算结果的影响。使用23例特发性震颤（ET）患者的术中刺激测试数据进行患者特异性电场模拟，并在组特异性解剖模板空间中生成PSS。首先比较不同统计检验的PSS在大小和地形方面的差异。然后，在留一法交叉验证方案中计算它们与临床改善的相关性，并评估不同组成数据集之间PSS的一致性。我们的研究结果强调了统计检验选择对提取的PSS的解剖位置和体积的影响，突出了在未来DBS映射研究中谨慎选择方法的重要性。首次在DBS研究中引入的贝叶斯t检验和非参数置换检验的体素级应用，在识别代表改善的PSS方面显示出有前景的结果，并且对数据集的变化具有稳健性。
