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图形规则化的多重意识条件瓦瑟斯坦GAN用于大脑功能连接的生成.

Yee-Fan Tan1, Fuad Noman1, Raphaël C-W Phan1

  • 1School of Information Technology, Monash University, Subang Jaya, Malaysia.

Human brain mapping
|August 18, 2025
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概括

这项研究介绍了一种新的生成对抗网络 (GAN) 用于大脑功能连接 (FC) 数据,保留其独特的结构. 该方法通过改进数据增强,提高了诸如主要抑郁症 (MDD) 等疾病的诊断准确性.

关键词:
里曼的几何学里曼的几何学脑部疾病 脑部疾病这是分类分类的分类.数据增强数据增强功能磁力共振成像 (fMRI) 是一种功能共振成像.功能连接性的功能连接性生成性的对抗性网络.

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科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 计算神经科学是一种神经科学.

背景情况:

  • 大脑功能连接 (FC) 数据,通常表示为协方差或相关性矩阵,具有独特的对称正确 (SPD) 结构,位于里曼的多重体上.
  • 标准生成对抗网络 (GAN) 通常无法捕捉这种固有的SPD结构,导致不切实际的FC数据生成,并忽视网络边缘的相互关系.

研究的目的:

  • 开发一种新的GAN模型,能够生成现实的多重价值FC数据,同时保持SPD的内在结构.
  • 改进类条件FC数据的生成,用于区分健康对照和脑疾病患者等应用.

主要方法:

  • 提出了一个图形规则化的多元体意识条件瓦斯斯坦GAN (GR-SPD-GAN),可以在SPD多元体上优化概括的瓦斯斯坦距离.
  • 纳入基于人口图的规范化,以保持学科间的相似性和稳定训练,避免模式崩.
  • 在类标签上调节GAN以生成特定类型的大脑网络数据.

主要成果:

  • 在评估主要抑郁症 (MDD) 数据时,GR-SPD-GAN成功生成了基于fMRI的更现实的FC样本,与最先进的GAN相比.
  • 使用GR-SPD-GAN的FC数据增强显著提高了MDD识别的分类准确性,优于其他基于GAN的增强方法.

结论:

  • 拟议的GR-SPD-GAN有效地生成高保真性SPD值的FC数据,保持全球网络结构和学科间关系.
  • 这种方法为FC数据增强提供了强大的工具,提高了神经科学研究中的诊断分类模型的性能.