一个分水算法GUI为个性化的fMRI引导的RTMS目标
Zi-Jian Feng1, Ziyu Wei2, Liquan Hong3
1TMS center, The Third People's Hospital of Deqing (Deqing Hospital of Hangzhou Normal University), Deqing, Zhejiang, China; Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
NeuroImage
|January 22, 2026
概括
一种新的以流域为基础的方法改善了使用功能性MRI (fMRI) 进行个性化的重复性跨磁刺激 (rTMS) 的准. 与传统方法相比,这种方法提供了更可靠的刺激部位定位.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 个性化的重复性横磁性刺激 (rTMS) 依赖功能性MRI (fMRI) 来准确地定位目标.
- 目前基于fMRI的准方法经常使用主观值和地图表面具,导致个性化RTMS目标的变化.
- 开发fMRI引导的rTMS的客观和可重复的方法对于临床疗效至关重要.
研究的目的:
- 引入一种基于流域的新细分方法,用于独立于值的功能图像分析.
- 将这种方法实现在一个用户友好的图形界面中,用于RTMS目标定位.
- 评估这种新方法的性能与使用默认模式网络目标的传统方法相比,这些网络目标与阿尔茨海默病相关.
主要方法:
- 为fMRI数据开发基于流域的图像细分算法.
- 在图形用户界面 (GUI) 工具箱中实现算法.
- 使用21名健康参与者的休息状态fMRI数据进行验证,将后带膜皮质 (PCC) 和下叶 (IPL) 目标与传统的峰值发现策略进行比较.
主要成果:
- 区分区方法表明对后带皮层 (PCC) 目标具有很高的一致性.
- 与传统方法相比,在下顶叶 (IPL) 目标中观察到6.0毫米的中位空间位移.
- 定性分析表明,邻近集群,亚特拉斯边界和多个峰值的偏差减少了.
结论:
- 拟议的以流域为基础的方法为个性化fMRI引导的rTMS提供了可通用和可访问的工具.
- 这种独立于值的方法提高了rTMS目标选择的可靠性和客观性.
- 该框架有可能提高rTMS疗法的精度和有效性.
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