对大脑结构连接体的相对强度可变性测量及其与认知功能的关系.
Hon Wah Yeung1, Colin R Buchanan1, Joanna Moodie1
1Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
Human brain mapping
|August 6, 2025
概括
我们引入了新的大脑网络测量方法,相对强度可变性 (RSV) 和层次式RSV (hRSV),以分析加权连接. 较高的一般认知功能 (g) 与较低的RSV和hRSV相关,表明网络弹性和复杂性增加.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 认知科学 认知科学
背景情况:
- 了解人类大脑复杂的加权连接对于认知功能至关重要.
- 现有的图表测量可能无法完全捕捉加权脑网络的细微差别.
- 节点相对强度为网络拓和功能提供了新的视角.
研究的目的:
- 引入和验证两个新的加权图表指标:相对强度变化 (RSV) 和层次式RSV (hRSV).
- 评估这些新型措施与一般认知功能 (g) 之间的关系.
- 将RSV和hRSV的预测能力与已建立的网络指标进行比较.
主要方法:
- 基于加权网络中节点相对强度的RSV和hRSV的开发.
- 这些措施应用于英国生物银行的结构连接组,使用六种不同的网络权重.
- 相关性和回归分析以检查与其他图表测量和一般认知功能 (g) 的关联.
主要成果:
- RSV和hRSV与现有图表测量结果的相关性很低,这表明它们捕获了独特的网络信息.
- 较高的一般认知功能 (g) 与较低的RSV和hRSV显著相关.
- 与集群系数和全球效率等传统指标相比,这些新型指标显示了与g的更强烈和逐渐显著的关联.
结论:
- RSV和hRSV代表了一个新的加权网络测量类别,为大脑连接组组织提供了新的见解.
- 这些指标与一般的认知能力有关,更高的认知与更大的网络抵抗攻击和更低的统计复杂性有关.
- 拟议的指标显著提高了从加权结构连接组的一般认知的预测.
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