连接梯度的个别独特性是由嵌入式网络的复杂性及其分散驱动的
Yvonne Serhan1,2, Shaymaa Darawshy1,2, Wei Wei3,4
1Department of Cognitive Sciences, School of Psychological Sciences, Faculty of Social Sciences, University of Haifa, Haifa, Israel.
Brain structure & function
|July 3, 2025
概括
大脑连接梯度随着时间的推移显示出稳定的个体模式,网络复杂性推动了独特性. 这些梯度平衡了与个体变异共享的大脑组织原则,帮助组对比和行为预测.
科学领域:
- 神经科学是一个神经科学.
- 脑部成像 脑部成像
- 网络科学 网络科学
背景情况:
- 功能性大脑组织通常使用连接梯度来研究.
- 个人独特性与共同的组织原则之间的平衡仍然不清楚.
研究的目的:
- 使用连接梯度研究大脑组织中的个人变异与共享模式.
- 评估前三个连接梯度的稳定性和个人识别实用性.
主要方法:
- 利用杭州测试复试数据集与一个月的休息状态fMRI扫描.
- 连接梯度的量化短期和长期稳定性.
- 应用了连接组指纹来确定个体识别率.
主要成果:
- 这三种连接梯度均表现出高的短期和长期相关性,证实了连接指纹的实用性.
- 网络复杂性,特别是在异模网络中,与更高的个体识别率相关.
- 与识别率相关的梯度系数分散,独立于梯度位置.
结论:
- 连接梯度是稳定的,并用于个人识别 (连接组指纹).
- 网络分散影响大脑组织中的个体独特性.
- 研究结果支持使用连接梯度用于组分析和个人行为预测.
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