皮格莱特:一个用户友好的概率核密度估计内皮层和皮层下刺激部位
Andrea Bellacicca1, Marco Rossi1, Luca Viganò2
1MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milano 20122, Italy.
Journal of neuroscience methods
|May 25, 2024
概括
我们开发了Peaglet,这是一种用于在脑成像中绘制直接电刺激 (DES) 数据的新工具. 这种方法提高了DES结果的解剖特异性和可靠性,用于连接性分析.
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:
- 来自神经外科患者的直接电刺激 (DES) 数据越来越多地与神经成像用于连接学.
- 在神经成像工作流程中,DES数据的精确空间定位至关重要.
研究的目的:
- 开发一种可靠的方法,用于直接电刺激 (DES) 数据的空间定位.
- 将DES衍生信息集成到神经成像工作流程中,用于连接性分析.
主要方法:
- 核心密度估计 (KDE) 用于在MNI空间中建模DES站点分布.
- 一个基于MATLAB的用户界面,Peaglet,被开发用于对DES站点位置的概率估计.
- 皮质局部化使用最短路径计算; 皮质下局部化使用基于体积的方法.
主要成果:
- 皮格莱特被应用于脑瘤研究,生成皮质和皮质下刺激部位的NIfTI地图.
- 由此产生的地图使用开源神经成像工具进行了分析.
- 皮格莱特证明了DES结果的解剖特异性和可靠性增加.
结论:
- 皮格莱特提供了对DES位点分布的强有力的概率估计,尊重大脑几何.
- 该工具超越了兴趣地区的方法,以实现更精确的本地化.
- 结果很容易被整合到神经成像工作流程中,以促进连接性分析.
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