BGCSL:一个无监督的框架揭示了大规模全脑功能连接网络的基本结构
Hua Zhang1, Weiming Zeng1, Ying Li2
1Shanghai Maritime University, Shanghai 201306, China.
Computer methods and programs in biomedicine
|January 5, 2025
概括
这项研究介绍了BGCSL,这是一个新的框架,用于从没有标签的fMRI数据中发现大规模的大脑网络. BGCSL有效地揭示了功能性大脑连接,并提高了下游任务性能.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 功能磁共振成像 (fMRI) 对于通过大规模网络推断来理解大脑结构,功能和临床表型至关重要.
- 现有的方法在估计大规模大脑网络时面临局限性,特别是随着节点的增加,由于依赖标签,下游任务或稀疏的后处理.
研究的目的:
- 提出一个新的框架,BGCSL (脑图对比结构学习),用于揭示大规模的功能性脑连接网络.
- 通过开发一种无标签的脑网络估计方法来克服现有方法的局限性.
主要方法:
- BGCSL使用图形对比结构学习方法,包括稀疏图形结构学习器和图形对比学习 (GCL).
- 在 GCL 中的动态增强训练稀疏图形结构学习器以捕获内在数据结构而不需要标记信息.
主要成果:
- 在合成和现实世界fMRI数据集 (ABIDE-I,HCP-rest) 上进行了广泛的实验,证明了BGCSL的有效性.
- BGCSL在合成数据上实现了最先进的性能,即使节点功能不足.
- 该框架显著提高了基于GCN的下游模型的性能.
结论:
- BGCSL为大规模大脑网络估计和表示提供了一个有前途的方法.
- 该方法支持用于临床应用的更多细粒度生物标记物的识别.
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