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机器学习应用于功能磁共振成像在焦虑障碍中的应用

Sahar Rezaei¹, Esmaeil Gharepapagh¹, Fatemeh Rashidi²

¹Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Nuclear Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran.

Journal of affective disorders

|September 8, 2023

概括

使用功能磁共振成像 (fMRI) 的机器学习技术可以将焦虑障碍与健康个体区分开来. 这些发现表明,基于fMRI的机器学习可能有助于诊断焦虑和开发有针对性的治疗方法.

科学领域:

神经科学是一个神经科学.
精神病学是一个精神病学.
计算机科学计算机科学

背景情况:

焦虑障碍表现出常见的大脑功能异常.
机器学习 (ML) 可以分析这些异常以进行诊断歧视.
功能磁共振成像 (fMRI) 是这个领域的一个关键的神经成像技术.

研究的目的:

用焦虑障碍的fMRI数据提供ML研究的全面概述.
探索ML在基于大脑功能的焦虑障碍和健康对照之间的区别方面的潜力.

主要方法:

在PubMed,科学网和Scopus上进行系统的文献搜索.
包括12项使用fMRI (休息状态和/或基于任务) 在患有焦虑障碍的患者中的研究.
对使用fMRI特征进行歧视的ML调查的分析.

主要成果:

在关键大脑区域 (例如,杏仁体,海马体,ACC) 的功能连接和神经激活的变化使焦虑患者与对照者有区别.
在各个研究中,歧视准确度从36%到94%不等.
确定的大脑网络包括默认模式,背部注意力,感官和情感网络.

结论:

利用fMRI特征的ML方法可以有效地将焦虑障碍患者与健康对照者区分开来.
这些技术有望成为用于焦虑障碍诊断的宝贵工具.
开发更有针对性的焦虑症治疗策略的潜力.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.恐慌失调是一种恐慌失调.休息状态的fMRI.社交焦虑障碍是一种社会焦虑障碍.基于任务的fMRI一般化焦虑障碍

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