University of California, Berkeley, USA.
University of California, Berkeley, USA.
Behav Res Ther. 2022 Jul;154:104105. doi: 10.1016/j.brat.2022.104105. Epub 2022 Apr 30.
The present study recruited psychologically healthy individuals and individuals with clinically-severe Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses, including generalized anxiety disorder, major depressive disorder, social anxiety disorder, posttraumatic stress disorder, panic disorder, persistent depressive disorder, and specific phobia. During the course of a structured clinical interview, 200 individuals provided continuous electrocardiogram and impedance cardiography data. Of these N = 150 were used for exploratory analyses and N = 50 for confirmatory analyses. From these time series, we modeled heart period (i.e. interbeat interval), pre-ejection period, respiratory sinus arrhythmia, and respiration rate. The group iterative multiple model estimation (GIMME) model was used to generate group and individual-level network models which, in turn, were used to conduct unsupervised classification of individual-level models into subgroups. Four subgroups were identified, comprising N = 22, N = 25, N = 26, and N = 61 individuals, with an additional 16 individuals left unclassified. The subgroup models were then used to estimate directed network models, from which out-degree and in-degree centrality were estimated for each group. Two groups, Group 2 and Group 4 exhibited elevated symptoms of depression and anxiety relative to the remaining sample. However, only one of these, Group 2, exhibited additional physiological risk features, including a significantly elevated average heart rate, and significantly reduced parasympathetic regulation (measured via respiratory sinus arrhythmia). We discuss the implications for utilizing network models for conducting systems-level analyses of physiological systems in clinically-distressed and psychologically healthy individuals.
本研究招募了心理健康个体和患有临床严重精神障碍诊断与统计手册第五版（Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition）诊断的个体，包括广泛性焦虑障碍、重度抑郁症、社交焦虑障碍、创伤后应激障碍、惊恐障碍、持续性抑郁障碍和特定恐惧症。在结构化临床访谈过程中，200 名个体提供了连续心电图和阻抗心动图数据。其中 N=150 用于探索性分析，N=50 用于验证性分析。从这些时间序列中，我们构建了心率（即心动周期）、射血前期、呼吸窦性心律失常和呼吸频率模型。采用组迭代多模型估计（group iterative multiple model estimation，GIMME）模型生成组和个体水平的网络模型，进而用于将个体水平的模型进行无监督分类，分为亚组。确定了四个亚组，包括 N=22、N=25、N=26 和 N=61 名个体，另外还有 16 名个体未分类。然后，使用亚组模型来估计有向网络模型，从中估计每个组的出度和入度中心度。两个组，即组 2 和组 4，与其余样本相比表现出更高的抑郁和焦虑症状。然而，只有其中一个，即组 2，表现出额外的生理风险特征，包括平均心率显著升高和副交感神经调节显著降低（通过呼吸窦性心律失常测量）。我们讨论了在患有临床困扰和心理健康的个体中，利用网络模型进行生理系统的系统水平分析的意义。
