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焦虑和非焦虑抑郁症分类的MAMF-GCN模型以及神经成像标记识别标记

Shouying Wang¹, Jiyuan Zhang¹, Rui Zhang¹

¹School of Medical Engineering, Xinxiang Medical University, Xinxiang, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, the Second Affiliated Hospital of Xinxiang Medical University, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology

|November 27, 2025

概括

一个新的深度学习模型使用神经成像数据和临床信息准确地分类焦虑与非焦虑抑郁症. 这种方法还可以识别关键的大脑网络标记物,有助于抑郁症亚型和治疗.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
精神病学是一个精神病学.

背景情况:

抑郁症是一种广泛的心理健康障碍.
在焦虑和非焦虑抑郁症之间区分存在诊断挑战.
识别可靠的神经成像标志物对抑郁症亚型至关重要.

研究的目的:

开发和验证一个深度学习模型来分类焦虑和非焦虑抑郁症.
为了确定神经成像标记,区分这些抑郁症亚型.
利用成像和非成像数据来提高分类准确性.

主要方法:

使用了REST-meta-MDD公共数据集 (158名焦虑患者,108名非焦虑抑郁症患者).
计算机神经成像指标:fALFF,DC,ReHo,VMHC和FC. 这些指标包括:
开发了一个多尺度的自适应多通道融合深图卷积网络 (MAMF-GCN),集成临床数据 (地点,年龄,性别,HAMD).
应用梯度反向传播用于特征选和标记物识别.

主要成果:

MAMF-GCN模型实现了高分类性能:98.12%的准确性,99.41%的灵敏性,97.46%的特异性,98.38%的F1得分和0.9867的AUC.
与最先进的方法相比,证明了卓越的性能.
证实了整合非成像临床数据以提高分类的有效性.
确定小脑网络,视觉,听觉和运动控制网络对于区分抑郁症亚型至关重要.

结论:

提出的方法对于分类焦虑和非焦虑抑郁症非常有效.
成功确定了抑郁症亚型的潜在神经成像标记物.
这种方法对诊断和潜在治疗抑郁症亚型具有显著的临床价值.

关键词:

焦虑和非焦虑的人.抑郁症分类的分类抑郁症分类国际货币基金组织-GCN 标记器识别识别标记器休息状态的功能性MRI.

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