动态大脑网络的量化相似性:结构变化和时间进化的两个新指数
Xiaocheng Wang1, Yongquan He2, Tian Zhou1
1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
新的指数动态网络相似性 (DNS) 和动态网络演变相似性 (DNES) 有效地分析动态大脑网络. 这些工具为大脑连接研究提供了一种动态的替代静态方法.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 医疗成像医学成像
背景情况:
- 大脑的功能连接是动态的,随着发展,衰老,疾病和认知而变化.
- 传统的静态网络分析无法捕捉到这些关键的大脑动态.
研究的目的:
- 引入两个新的指数:动态网络相似性 (DNS) 和动态网络演变相似性 (DNES).
- 评估DNS和DNES在使用模拟和现实世界fMRI数据分析动态大脑网络中的有效性.
主要方法:
- 开发了DNS来测量时间和结构动态相似性.
- 开发了DNES,专门评估动态网络的时间演变.
- 经验证的指数与模拟数据 (变化 Δφ, λ, α, β) 和从中风患者接受跨直流刺激 (tDCS) 的fMRI数据.
主要成果:
- DNS对所有动态特征表现出敏感性,而DNES对相位 (Δφ) 和相对振幅 (λ) 的变化表现出敏感性.
- 无论是DNS还是DNES都成功地检测到了大脑网络动态的整体差异.
- 与不同疗法 (DT) 相比,在接受相同疗法 (ST) 的组中,指数显示了显著更高的相似性.
结论:
- DNS和DNES是研究动态演变的大脑网络的有效工具.
- 这些指数为分析大脑连接提供了与传统静态方法有价值的替代方案.
- 这些方法对于神经发育,衰老和疾病恢复的纵向神经成像研究特别有用.
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