神经桥:通过神经合和一致性引导的动态图形学习,将功能性和结构性大脑网络连接起来
Yueying Li1, Rui Dong1, Xiaoyun Liu2
1Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, China.
Medical image analysis
|February 18, 2026
概括
这项研究介绍了NeuroBridge,这是一种用于融合功能和结构大脑网络数据的新方法. 神经桥增强了多式联络脑网络分析,以改善疾病预测和分类任务.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 功能性大脑网络在休息状态或任务状态下映射连接.
- 结构性大脑网络揭示了宏观的途径完整性.
- 整合这些模式存在挑战,因为数据结构和分辨率不同.
研究的目的:
- 开发一种有效融合功能和结构大脑网络数据的方法.
- 提取有区别的联合表示,用于增强大脑网络分析.
- 在疾病分类等任务中提高预测准确度.
主要方法:
- 拟议的NeuroBridge框架用于多式联络大脑网络分析.
- 引入了用于结构功能合的神经协同合模块 (NeuSCM).
- 开发的一致性指导模块 (CAGM) 以实现语义连贯性.
- 利用动态关联解析模块 (DAP) 来捕获和预测关系.
主要成果:
- 与最先进的单模和多模方法相比,NeuroBridge实现了更高的预测准确度.
- 在疾病预测和性别分类任务中证明有效.
- 提供了对多式联络大脑网络分析的新见解.
结论:
- 神经桥有效地融合了多式联网大脑网络数据.
- 拟议的方法增强了用于临床应用的大脑网络的分析.
- 这种方法为未来的大脑网络分析研究提供了有希望的方向.
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