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使用MRI和临床审查数据进行患者特定和可解释的深度大脑刺激优化

Apostolos Mikroulis¹, Andrej Lasica², Pavel Filip^1,2

¹Analysis and Interpretation of Biomedical Data, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, Czechia.

Frontiers in neuroscience

|November 7, 2025

概括

这项研究介绍了一种基于几何学的方法,用于优化帕金森病中深度大脑刺激 (DBS) 设置. 与专家设置相比,自动化方法改善了目标覆盖范围,并减少了副作用.

科学领域:

神经外科神经外科
生物医学工程生物医学工程
计算神经科学是一种神经科学.

背景情况:

优化深度大脑刺激 (DBS) 对于治疗像帕金森病这样的运动障碍至关重要.
目前的数据密集型方法增加了临床工作流程的复杂性.
使用MRI数据的基于几何学的方法提供了一个简化的替代方案.

研究的目的:

开发和验证一种基于几何学的自动化方法,以优化DBS电极接触和电流选择.
为了提高DBS参数调节的精度和效率.
与现有的临床实践和工具无集成.

主要方法:

使用了重建数据和活化组织体积 (VTA) 模拟.
开发了一个用于自动联系和当前选择的跨平台工具.
嵌入的可选包含现有的电极接触评估用于微调.

主要成果:

与174个电极重建中的专家设置相比,该算法证明了优越的目标覆盖 (p < 5e-13) 和最小的电场泄漏 (p < 2e-10).
追溯分析预测了与专家设置相似的运动结果 (g = 0.05-0.08,p = 0.09-1).
算法选择的接触器在电场计算中表现优于手动选择.

结论:

自动化DBS优化方法在临床应用方面显示出显著的前景.
这种方法提高了电场分布,而不是在没有代程序的情况下手动选择.
这种方法很容易适用于现有的临床工作流程,以改善DBS治疗.

关键词:

这就是为什么MRI是MRI.帕金森病是帕金森氏症的一种疾病.计算建模计算建模深度大脑刺激刺激大脑优化优化优化优化亚体的细胞核.

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