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解构Mapper算法,从功能神经成像数据中提取更丰富的拓和时间特征

Daniel Haşegan¹, Caleb Geniesse¹, Samir Chowdhury¹

¹Department of Psychiatry and Behavioral Sciences, Stanford University.

Network neuroscience (Cambridge, Mass.)

|December 30, 2024

概括

了解大脑活动动态是认知的关键. 这项研究探讨了Mapper算法的参数选择如何影响大脑数据分析,为使用拓数据分析的研究人员提供了指导.

科学领域:

神经科学是一个神经科学.
拓数据分析拓数据分析
认知科学认知科学

背景情况:

捕捉大规模的大脑活动动态对于理解认知至关重要.
拓数据分析 (TDA) 工具,特别是Mapper,已被应用于高分辨率的大脑活动数据.
映射算法对参数选择的敏感性给神经成像数据分析带来了挑战.

研究的目的:

调查参数选择对Mapper算法对大脑活动动态结果的影响.
为在神经成像研究中选择最佳Mapper参数提供指导和启发式.
为了促进Mapper应用到复杂的神经成像数据集.

主要方法:

对Mapper算法的每个步骤进行各种参数选择的探索.
利用具有已知的过渡结构的合成数据集来验证参数效应.
应用参数探索到真正的功能磁共振成像 (fMRI) 数据.

主要成果:

证明Mapper参数选择对分析大脑活动动态的显著影响.
识别了特定的参数设置,以提高潜在数据结构的揭示.
在Mapper分析中开发并发布了一个用于参数探索的软件工具箱.

结论:

彻底检查Mapper参数对于可靠分析神经成像数据至关重要.
这些发现为研究人员将TDA应用于大脑活动提供了实际指导.
发布的工具箱旨在提高基于Mapper的神经科学研究的可复制性和可访问性.

关键词:

大脑动态大脑动态卡片制造商的地图制造商神经成像是一种神经成像.在 TDA TDA 中.功能磁力共振成像 (fMRI) 是一种

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