人类大脑功能多层网络的节点重新配置.
Tarmo Nurmi1, Pietro De Luca1, Maria Hakonen2,3
1Department of Computer Science, Aalto University School of Science, P.O. Box 15400, Aalto FI-00076, Finland.
ArXiv
|March 17, 2025
概括
使用静态感兴趣区域 (ROI) 定义大脑网络节点限制了准确性. 重新配置ROI可以动态地改善功能同质性,并产生更精确的脑网络模型,从而推进了chronnectome概念.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 功能性大脑网络分析依赖于定义网络节点,通常使用静态感兴趣区域 (ROI).
- 静态ROI,功能磁共振成像 (fMRI) 声器的集合,未能捕捉动态大脑活动和时间重新配置.
- 这种静态定义导致功能均性较低,并违反了网络分析中的假设,可能导致虚假的网络结构.
研究的目的:
- 为大脑功能网络引入一种新的节点重配置多层网络模型.
- 动态优化ROI边界,在每个时间窗口内实现高功能均性.
- 研究动态ROI重新配置对大脑网络建模和功能均性的影响.
主要方法:
- 开发了一个节点重新配置的多层网络模型,其中网络层代表时间窗口.
- 在每个时间窗口中优化了ROI边界,以实现最大的功能均性.
- 量化内层功能连接性和间层ROI在时间窗口中重叠.
主要成果:
- 与静态地图集相比,ROI优化方法实现了与静态地图集相比,高度均的ROI增加了10倍以上.
- 优化的ROI在短时间和长时间尺度上表现出非微不足道的重组.
- 投资回报率重组的中间水平与更强的内层功能连接 (枢纽) 相相关.
结论:
- 动态重新配置的分片可以提供更准确的脑功能网络模型.
- 这种方法支持chronnectome范式,将大脑视为不断重新配置的资源.
- 优化的ROI增强了功能均性,解决了fMRI网络分析中静态定义的局限性.
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