人类大脑网络的拓嵌入与应用到叶的动力学
Moo K Chung1, Ji Bi Che2, Veena A Nair3
1Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
ArXiv
|May 27, 2024
概括
这项研究使用拓数据分析 (TDA) 来分析叶患者的脑网络动态,使用休息状态的fMRI. 与对照组相比,TLE患者表现出更严格的0D大脑网络拓学和更快的1D拓学变化.
科学领域:
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
- 医疗成像医学成像
背景情况:
- (TLE) 是一种影响大脑功能的神经系统疾病.
- 休息状态功能磁共振成像 (rs-fMRI) 对于理解大脑网络动态至关重要.
- 量化大脑网络中的动态变化对于诊断和治疗TLE至关重要.
结论:
- 在TLE患者中,大脑网络拓学在0D特征中表现出增加的刚性.
- 与对照组相比,TLE患者在1D拓特征中表现出更快的屈曲.
- 这种TDA方法为分析神经系统疾病中大脑网络动态提供了一种新方法.
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