揭示疾病状态中的转变:随时间推移研究抑郁和焦虑症状网络
Minne Van Den Noortgate1, Manuel Morrens1,2, Albert M Van Hemert3
1Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Depression and anxiety
|April 14, 2025
概括
动态时间扭曲揭示了伴随性焦虑和抑郁障碍的症状网络密度更高. 这表明,更大的内部连接可能会推动复杂的心理健康系统的关键过渡.
科学领域:
- 精神病学和计算神经科学
- 心理健康研究 心理健康研究
- 复杂系统分析 复杂系统分析
背景情况:
- 大型抑郁症 (MDD) 和焦虑症 (AD) 经常同时发生,呈现重叠的症状.
- 在稀疏的数据面板中对不同抑郁和焦虑症状的纵向分析仍未得到充分探索.
- 动态时间扭曲 (DTW) 为研究症状网络动态提供了一种新的方法.
研究的目的:
- 在纵向数据上使用DTW分析共存的抑郁和焦虑症状.
- 探索症状网络特性及其与诊断和疾病状态稳定性的关系.
- 为了比较不同诊断组和疾病状态的网络密度.
主要方法:
- 利用了1649名参与者的荷兰抑郁和焦虑研究 (NESDA) 的数据.
- 在9年内通过自我报告问卷评估4-5次抑郁,焦虑和担心症状.
- 使用DTW计算了1649个距离矩阵,以生成症状网络并比较密度.
主要成果:
- 在焦虑和生理兴奋症状之间观察到最大的症状差异.
- 网络密度以对照组,AD,MDD和并发性AD-MDD患者的顺序增加.
- 较高的网络密度与随着时间的推移疾病状态的更大不稳定性相关 (β = -0.095,P < 0.001).
结论:
- 在稀疏的纵向数据中,DTW有效地可视化症状网络.
- 伴随性焦虑和抑郁障碍的网络密度最高,表明内部连接更强.
- 增加网络密度和疾病状态的不稳定性可能会促进心理健康状况的关键过渡.
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