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具有多种类型的功能连接的机器学习揭示了初级视角闭眼的全脑网络中断

Guangxiang Chen¹, Dekai Hu¹, Xin Huang²

¹School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China.

Brain informatics

|December 27, 2025

概括

机器学习有效地检测到脑网络中断在初级角闭眼光眼 (PACG),一个不可逆转的失明的疾病. 这项研究使用静止状态fMRI确定了关键的连接性变化,为PACG诊断提供了潜在的神经成像生物标志物.

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:

主要视角闭眼 (PACG) 是一种不可逆转的失明状况,导致视网膜质细胞损伤和视神经缩.
PACG显著影响大脑的功能网络,需要先进的诊断工具.

研究的目的:

通过静止状态功能磁共振成像 (rs-fMRI) 来研究PACG患者的大脑功能网络的改变.
评估机器学习模型在检测PACG特定的神经成像生物标志物的有效性.

主要方法:

分析了来自34名PACG患者和34名健康对照者的rs-fMRI数据.
使用AAL90图谱提取了四个连接特征 (静态FC,动态FC,EC,dEC).
使用弹性网特征选择和十个机器学习模型,逻辑回归 (LR) 显示最佳性能.

主要成果:

基于FC的LR模型实现了高诊断精度 (0.92) 和AUC (0.96).
莎普利添加式扩展 (SHAP) 确定了20个关键连接,在视觉,注意力和感觉运动网络中具有异常模式.
在PACG患者中观察到VSN-SMN和VSN-DAN连接性降低,以及增强的DAN-thalamus连接性.

结论:

机器学习可以有效地检测PACG相关的大脑网络中断.
特定的连接性改变作为潜在的神经成像生物标志物用于PACG检测.

关键词:

机器学习是机器学习.主要关闭角度的玻璃眼 (Glaucoma)静止状态功能磁共振成像技术在SHAP分析中,我们分析了SHAP.沃克塞尔智能功能连接 Voxel智能功能连接

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