自闭症谱系障碍预测残余图形变压器
Yibin Wang1, Haixia Long1, Tao Bo2
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
Computer methods and programs in biomedicine
|March 1, 2024
概括
一个新的残余图形转换器网络 (RGTNet) 通过使用大脑功能连接来改善自闭症谱系障碍 (ASD) 的预测. 这种方法增强了用于临床诊断的生物标志物发现.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 自闭症谱系障碍 (ASD) 诊断依赖于确定可靠的生物标志物.
- 休息状态功能磁共振成像 (rs-fMRI) 和大脑功能连接 (FC) 显示出ASD预测的希望.
- 现有的方法在捕捉复杂的大脑相互作用,深度网络表示和诊断解释性方面面临挑战.
研究的目的:
- 提出一种新的深度学习模型,即FC学习的剩余图形转换器网络 (RGTNet),用于改进ASD预测.
- 解决利用大脑区域相互作用,表示学习和诊断解释性方面的局限性.
- 开发一种用于识别ASD临床相关生物标志物的方法.
主要方法:
- 使用rs-fMRI数据计算大脑功能连接 (FC) 矩阵.
- 设计一个图形编码器来捕捉时间依赖性和模型可解释的FC矩阵.
- 实现一个剩余的技巧,以加深图形卷积网络 (GCN) 架构,用于更高层次的特征学习.
- 采用Graph Sparse Fitting和加权聚合来管理维度.
主要成果:
- 与现有方法相比,RGTNet在两个ABIDE数据集上表现出优异的性能.
- 在使用五倍交叉验证的AAL地图上实现了73.4%的准确性,超过了70.9%的基准值.
- 确定了与既定医学知识一致的生物标志物,支持临床相关性.
结论:
- 通过先进的功能连接分析,RGTNet提供了一种有前途的方法来增强ASD预测.
- 该模型的解释性和生物标志物识别能力为更可靠的ASD临床诊断铺平了道路.
- 开发的方法为推进精神病学中基于神经成像的诊断提供了可行的工具.
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