多指标透中心得分:脑网络中枢分析的定量方法.
Hongzhou Wu1, Jinming Xiao2, Elijah Agoalikum3
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.; Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China.
NeuroImage
|February 13, 2026
概括
一个新的多指标透枢纽分数 (MIEHS) 使用多个指标量化大脑网络枢纽. 这种方法揭示了像默认模式网络这样的网络中的枢纽改变如何与认知功能和临床条件相关.
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:
- 人类大脑依赖于模块化网络中的动态交互,连接器和省级枢纽对于信息集成至关重要.
- 以前的枢纽识别方法经常使用单个指标,忽视了多指标集成和定量节点贡献.
- 为大脑中心建立强大的临床生物标志物仍然是一个挑战.
研究的目的:
- 引入和验证多指标值中心得分 (MIEHS) 以量化大脑网络中心属性.
- 调查连接器和省级枢纽的本地化和功能角色.
- 探索临床人群中枢变化和认知功能之间的关联.
主要方法:
- 通过整合六个图形理论指标,开发了多指标值中心评分 (MIEHS).
- 在基准网络,模拟数据和休息状态fMRI数据 (午夜扫描俱乐部) 上验证了MIEHS.
- 分析了中心分布,使用梯度映射的功能角色,以及使用部分最小平方分析在临床数据集 (UCLA) 中与认知措施的关联.
主要成果:
- MIEHS可靠地识别了跨不同网络类型和数据集的枢纽.
- 在注意网络中发现了得分高的连接器枢纽,弥合了单模和跨模区域,而省级枢纽集中在默认模式网络中,支持网络内部通信.
- 默认模式,突出,边缘和背部注意力网络中的枢纽改变与临床人群 (ADHD,BD,SCHZ) 的认知灵活性,抽象推理和口头表达有显著的关联.
结论:
- MIEHS为绘制大脑网络组织和特征功能重新配置提供了一个强大的和多功能框架.
- 调查结果强调了连接器和省级枢纽在信息处理中的不同角色.
- 枢纽变化代表神经和精神疾病中认知缺陷的潜在生物标志物.
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