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在tES中用于剂量规划的组优化方法

R Salvador¹, J Zhou², B Manor²

¹Neuroelectrics, Barcelona, Spain.

Journal of neural engineering

|August 6, 2025

概括

一个新的跨电刺激 (tES) 团体级优化框架为个性化模型提供了一个可扩展的替代方案. 这种方法改善了电场 (E-field) 的定位,并减少了变异性,而不需要单独的MRI扫描.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
计算机建模计算建模

背景情况:

优化跨电刺激 (tES) 需要精确的电场 (E-field) 分布,以获得治疗效果.
基于MRI的个性化头部模型至关重要,但在临床和研究环境中限制了可扩展性.

研究的目的:

开发和评估tES参数的新型组级优化框架.
通过使用多个代表性的头部模型来克服基于MRI的个性化模型的局限性.

主要方法:

计算建模利用多个代表性头部模型来最大限度地减少组级错误.
通过对54名受试者数据的交叉验证进行评估.
分析解剖特征的有效性,概括性和预测效用.

主要成果:

群组优化协议在配对分析中显著优于基于标准模板的标准方法.
与模板相比,基于组的解决方案系统地展示了更好的定位和减少了糟糕的结果.
确定了解剖特征和E场参数之间的相关性,使得进一步优化成为可能.

结论:

拟议的群组优化框架是个性化tES模型的可扩展和强大的替代方案.
这种方法提高了基于模型的tES协议的可行性和可访问性.
它消除了先验选择单一代表性模板的需要.

关键词:

计算模型是计算模型.剂量参数剂量参数通过皮电刺激.

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