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用深度学习技术对阿尔茨海默病和前性痴呆症进行分类的Muti-band Morlet相互信息功能连接

Mingkan Shen¹, Peng Wen², Bo Song³

¹School of Engineering, University of Southern Queensland, Toowoomba, Australia; Neurosciences Centre, Mater Hospital, South Brisbane, Australia; Mater Research Institute, University of Queensland, South Brisbane, Australia.

Computers in biology and medicine

|September 3, 2025

概括

这项研究使用脑电图 (EEG) 功能连接的深度学习来区分阿尔茨海默病 (AD) 和前性痴呆症 (FTD) 与健康对照. 该模型实现了高精度,识别了早期诊断的关键EEG生物标志物.

科学领域:

神经科学
人工智能
医疗诊断

背景情况:

准确区分阿尔茨海默病 (AD),前性痴呆 (FTD) 和健康对照 (HC) 对于及时干预至关重要.
现有的诊断方法可能具有侵入性或缺乏早期神经退行性疾病的敏感性.

研究的目的:

开发和验证使用脑电图 (EEG) 衍生功能连接 (FC) 来分类AD和FTD的深度学习框架.
确定基于EEG的新生物标志物,表明AD和FTD.

主要方法:

使用多波段Morlet波段相互信息 (MMMIFC) 来计算高分辨率的FC矩阵 (1-20 Hz).
使用基于修改的VGG架构进行分类的3D卷积神经网络 (3D-CNN).
应用图形理论指标来分析默认模式网络中的网络属性.

主要成果:

达到了高分类准确度:AD与HC的90.77%和FTD与HC的90.38%.
在AD和FTD分类中表现出高灵敏度和特异性.
在阿尔茨海默病和FTD患者的delta和theta频段中发现了降低的总效率,扩散效率和聚类系数,而deta频段在AD和delta频段中最为突出.

结论:

对EEG衍生多带功能连接的深度学习分析提供了一个有希望的,非侵入性的方法来区分AD和FTD与HC.
低频EEG网络动态的特定变化可以作为AD和FTD的潜在生物标志物.

关键词:

在3D-CNN 美国默认模式网络美国电力一个FTD 图形理论一个MMMIFC

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