Washington University in St. Louis, 1 Brookings Dr. St Louis, MO, 63130, USA.
Washington University in St. Louis, 1 Brookings Dr. St Louis, MO, 63130, USA.
J Affect Disord. 2019 Jan 15;243:531-538. doi: 10.1016/j.jad.2018.09.078. Epub 2018 Sep 22.
We used network analyses to examine symptoms that may play a role in the co-occurrence of social anxiety disorder (SAD) and major depressive disorder (MDD). Whereas latent variable models examine relations among latent constructs, network analyses have the advantage of characterizing direct relations among the symptoms themselves.
We conducted network modeling on symptoms of social anxiety and depression in a clinical sample of 130 women who met criteria for SAD, MDD, both disorders, or had no lifetime history of mental illness.
In the resulting network, the core symptoms of social fear and depressed mood appeared at opposite ends of the network and were weakly related; so-called "bridges" between these symptoms appeared to occur via intervening variables. In particular, the worthless variable appeared to play a central role in the network.
Because our data were cross-sectional, we are unable to draw conclusions about the direction of these effects or whether these variables are related to each other prospectively.
Continued testing of these pathways using longitudinal data will help facilitate the development of more effective clinical interventions for these disorders.
我们使用网络分析来研究可能在社交焦虑障碍(SAD)和重度抑郁症(MDD)共病中起作用的症状。虽然潜变量模型检验了潜在结构之间的关系,但网络分析具有描述自身症状之间直接关系的优势。
我们对符合 SAD、MDD、两种疾病或无终生精神病史标准的 130 名女性的临床样本中的社交焦虑和抑郁症状进行了网络建模。
在得出的网络中,社交恐惧和抑郁情绪的核心症状出现在网络的两端,相关性较弱;这些症状之间的所谓“桥梁”似乎是通过中间变量发生的。特别是,无价值变量似乎在网络中起着核心作用。
由于我们的数据是横断面的,因此我们无法就这些影响的方向或这些变量是否具有前瞻性相关性得出结论。
使用纵向数据对这些途径进行进一步测试将有助于为这些疾病开发更有效的临床干预措施。