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使用高频和低频特征融合框架自动诊断主要抑郁症

Junyu Wang^1,2, Tongtong Li^1,2, Qi Sun^1,2

¹School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

|November 25, 2023

概括

这项研究通过整合多模式神经成像数据,引入了主要抑郁障碍 (MDD) 的新型诊断模型. 该模型有效地结合了扩散张力成像,结构性MRI和功能性MRI,以改进MDD评估.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
计算精神病学是一种计算精神病学.

背景情况:

大型抑郁症 (MDD) 是一种普遍存在的精神疾病,与免疫功能障碍和自杀念头有关.
神经成像为MDD诊断提供了定量方法,但目前的方法通常是单独分析数据.
现有的计算机辅助诊断模型可能会忽略有价值的见解,因为它们不能协同整合各种数据源.

研究的目的:

开发和评估一种新的MDD诊断模型.
整合来自多种神经成像模式的高频和低频信息.
通过使用多模式方法,提高MDD诊断的准确性和全面性.

主要方法:

提出了一种综合扩散张力成像 (DTI),结构磁共振成像 (sMRI) 和功能磁共振成像 (fMRI) 的诊断模型.
开发了用于DTI和sMRI数据的低频 (MLFE) 和高频 (MHFE) 编码器.
采用多层感知器 (MLP) 进行fMRI特征提取,并采用集体学习值投票方法进行最终诊断.

主要成果:

综合模型实现了0.724的精度,0.750的精度和0.882.8的特异性.
该模型还报告了F1得分为0.600,MCC为0.421,AUC为0.667.
这些结果表明了整合多模式神经成像数据用于MDD诊断的潜力.

结论:

拟议的模型有效地整合了多模式神经成像数据 (DTI,sMRI,fMRI) 用于MDD诊断.
这种协同方法为计算机辅助诊断MDD的研究提供了一个有希望的新方向.
调查结果强调了将不同数据频率和数据源相结合的重要性,以便进行更全面的评估.

关键词:

深度学习是一种深度学习.功能融合功能融合功能高频和低频两种频率.磁共振成像技术的使用大型抑郁症是主要的抑郁症.多式联运是多式联运.

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