罗图卷积网络量化了越来越多的结构-功能差异在认知衰退连续的结构-功能差异
Gurur Gamgam1, Zerrin Yıldırım2, Alkan Kabakçıoğlu3
1VAVlab, Department of Electrical And Electronics Eng., Bogazici University, Istanbul, 34342, Turkiye.
Computer methods and programs in biomedicine
|July 2, 2024
概括
结构功能差异学习网络 (sfDLN) 量化了大脑连接不匹配,超过了目前对阿尔茨海默氏症痴呆症 (ADD) 诊断的方法.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病痴呆症 (ADD) 改变了大脑的连接,但目前的措施缺乏诊断特异性.
- 图形神经网络 (GNN) 在大脑研究中表现有前途,但通常使用单模网络.
- 结构性和功能性大脑连接的相互依赖性至关重要,但在ADD研究中经常被忽视.
研究的目的:
- 开发一种新的深度学习模型,结构功能差异学习网络 (sfDLN),以量化阿尔茨海默病中大脑连接的破坏.
- 利用结构功能差异得分作为认知衰退的诊断生物标志物.
- 将sfDLN的诊断性能与现有的最先进的分类器进行比较.
主要方法:
- 采用了语GNN架构 (sfDLN),假设增加结构功能不匹配与认知衰退 (主观认知障碍 (SCI) 到轻度认知障碍 (MCI) 到ADD).
- 输入数据包括来自扩散MRI的结构性脑连接体 (sNET) 和来自fMRI的稀疏功能性脑连接体 (lNET).
- sfDLN被训练以提取连接组表示和差异得分,然后在MCI队列上盲目测试.
主要成果:
- sfDLN生成的差异得分显示了ADD和SCI受试者之间的显著差异.
- 对SCI与ADD的留下一个缺点分类实现了88%的准确性,超过了现有的基于GNN的方法.
- 对MCI受试者的盲目评估证实了sfDLN能够捕捉中间认知衰退阶段的能力.
- 在GNNExplainer分析中,在差异得分中确定了与ADD相关的神经学上相关的皮质区域.
结论:
- 随着认知衰退的增加,大脑结构-功能和降低,支持研究的假设.
- 量化的差异,植根于ADD相关的大脑区域,作为一个强大的诊断生物标志物.
- 与当前基于GNN的方法相比,sfDLN在分类ADD方面表现优越.
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