GWM-HFN,一个灰白物质异质融合网络,用于功能连接体
Kang Hu1,2, Zhuo-Yan Cai3, Yun-Er Xu3
1School of Artificial Intelligence and Big Data, Wuhan Business University, Wuhan, PR China.
Communications biology
|November 22, 2025
概括
这项研究引入了一种新的大脑网络模型,即灰白物质异质融合网络 (GWM-HFN),该网络集成白物质信号,以进行更完整的大脑连接分析. GWM-HFN揭示了对衰老,自闭症和认知功能的独特见解.
科学领域:
- 神经科学是一个神经科学.
- 脑部成像 脑部成像
- 在Connectomics上,我们提供了连接.
背景情况:
- 休息状态功能连接 (FC) 研究传统上侧重于灰质 (GM),忽视了白质 (WM) 对大脑网络的贡献.
- 现有的将WM集成到功能连接分析中的方法在评估全球网络属性和统一连接组模型方面存在局限性.
研究的目的:
- 引入灰白物质异质融合网络 (GWM-HFN) 框架,以便更全面地分析大脑功能连接.
- 通过多个数据集验证GWM-HFN并评估其可靠性,拓特征以及对个体差异和临床条件的敏感性.
主要方法:
- 开发了GWM-HFN框架,通过通过互动配置文件的共变性来定义GM-GM的功能链接,并从标准化地图上与WM捆绑开发了GWM-HFN框架.
- 在六个独立的数据集中验证了GWM-HFN,评估了测试重试可靠性,拓性质和捕获的独特差异.
- 分析了寿命效应,自闭症谱系障碍 (ASD) 的临床差异以及与认知表现的相关性.
主要成果:
- GWM-HFN证明了公平的测试-重新测试可靠性,与基于GM的FC可比,并表现出明显的拓特征,如增强的模块分离.
- 该框架捕获了超过40%的独特变异,显示了对个体差异的敏感性,与年龄相关的下降 (峰值连接率~34岁),以及ASD特定的超连接性.
- GWM-HFN连接预测了认知表现,特别是在语言任务中,并且在区分患有自闭症的个体方面显示出比GM-GM FC更大的灵敏度.
结论:
- GWM-HFN框架提供了一种强大的方法,可以在GM和WM之间整合功能信号,从而更全面地了解大脑网络.
- GWM-HFN揭示了对WM介导的神经通信,衰老效应和ASD等神经精神疾病的独特见解.
- 这种方法对开发用于衰老和神经和精神疾病的新型神经成像生物标志物具有前景.
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