基于非均的马尔科夫模型对泛性焦虑障碍异常动态连接机制的调查
Qin Tang1, Gan Zhang2, Yun-Shuang Fan2
1Department of Radiology, First Affiliated Hospital to Army Medical University, Chongqing 400038, China; The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; MOE Key Lab for Neuroinformation High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
Journal of affective disorders
|March 14, 2024
概括
通用焦虑障碍 (GAD) 显示了大脑网络动态的改变,异常的连接模式预测了焦虑的严重程度. 这些发现为GAD的神经支提供了新的见解.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 网络科学 网络科学
背景情况:
- 大脑功能网络表现出动态和等级性质,倾向于稳定状态 (吸引力).
- 在一般性焦虑障碍 (GAD) 中理解长期的大脑网络连接变化仍然有限.
研究的目的:
- 为了研究GAD患者的动态功能大脑网络连接.
- 探索这些动态如何与焦虑严重程度有关.
主要方法:
- 采用了可达到的概率方法,使用非均的马尔科夫模型和过渡概率.
- 在92名GAD患者和77名健康对照 (HC) 中分析了动态连接性和静止概率向量.
- 利用随机森林算法来预测焦虑的严重程度.
主要成果:
- 与HC相比,GAD患者在脑部区域 (如丘脑,PCC和SOG) 中显示出改变的融合模式.
- 在GAD中,特定的异常过渡概率模式与焦虑严重程度有关.
- 鉴定出异常的动态功能模式表明,在情绪调节中神经通信受到干扰.
结论:
- 揭示了GAD患者在情绪调节中的异常动态神经通信和集成.
- 提供了关于大脑功能动态在一般性焦虑障碍中的新见解.
- 突出了动态网络分析在理解精神疾病方面的潜力.
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