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在深度大脑刺激中建立强大的概率图:探索患者数量,刺激计数和统计方法的影响
Vittoria Bucciarelli1,2, Dorian Vogel1, Karin Wårdell1,3
1Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.
Frontiers in computational neuroscience
|February 6, 2026
概括
本研究定义了在深度大脑刺激 (DBS) 中稳定的概率甜点 (PSS) 的数据要求. 与其他统计方法相比,贝叶斯式t-test方法需要较少的患者进行可靠的PSS映射.
科学领域:
- 神经科学是一个神经科学.
- 医疗工程 医疗工程
- 生物统计学 生物统计学
背景情况:
- 概率刺激图 (PSM) 对于在深度大脑刺激 (DBS) 中识别最佳治疗大脑区域至关重要.
- PSM的可靠性和通用性受到小患者队列和统计方法和数据的变化所限制.
- 调查概率甜点 (PSS) 的几何变性对于理解数据要求至关重要.
研究的目的:
- 根据患者数量 (nPat) 和每个患者的刺激 (nStim) 来调查概率甜点 (PSS) 的几何变异性.
- 建模一个稳定性边界,定义对几何稳定的PSS的最低数据要求.
- 为了比较不同统计方法在实现PSS稳定的性能.
主要方法:
- 应用了三种统计方法:贝叶斯式t测试,维尔科克森测试与FDR校正,和维尔科克森测试与排列校正.
- 利用两个患者队列:帕金森病 (PD,n=36) 和基本 (ET,n=61) 进行手术内和手术后的数据.
- 通过分析PSS体积范围和心心位置变异性来评估几何稳定性.
主要成果:
- 确定了nPat和nStim之间的非线性权衡,以实现稳定的PSS.
- 确定了一个稳定性边界,表示强大的PSS的最低nPat-nStim组合.
- 贝叶斯式t测试在较少的患者 (∼15) 中实现了稳定性,并且在所有队列中显示出一致的性能,与基于Wilcoxon的方法不同.
结论:
- PSS稳定性边界为设计DBS研究和概率映射协议提供了实用指南.
- 贝叶斯式t测试是DBS研究的可靠方法,样本大小有限,症状不同.
- 考虑队列大小和刺激计数对于准确的概率DBS映射至关重要.
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