Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, Zurich, Switzerland.
Division of Mental Health, Medical Corps, Israel Defense Forces, Tel Aviv, Israel.
BMC Med. 2020 Oct 12;18(1):297. doi: 10.1186/s12916-020-01740-5.
In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).
Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).
Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.
The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.
在精神病理学的网络方法中,精神障碍被认为是因果活跃症状(节点)的网络,节点中心度假设反映了症状在网络中的因果影响。因此,基于激活高度中心节点会扩散到网络中的其他节点,从而破坏网络结构并减轻整体精神病理学(即中心度假设)的假设,中心度测量已被用于许多基于网络的横断面研究中,以确定特定的治疗目标。
在这里,我们总结了与精神病理学中心度假设相关的三种类型的证据。首先,我们讨论了精神病理学中心度假设的理论假设的有效性。然后,我们总结了使用中心度测量作为治疗后症状变化预测因子的现有研究的方法学方面,同时描述了它们的主要发现和几个局限性。最后,使用创伤后应激障碍(PTSD)的 710 名治疗寻求者的特定数据集作为示例,我们从实证上检验了节点中心度作为治疗变化的预测因子,复制了以前研究采用的方法,同时解决了其中的一些局限性。具体来说,我们研究了三个预处理中心度指数(强度、可预测性和预期影响)是否与症状变化与网络中所有其他症状严重程度变化之间的关联强度显著相关,从治疗前到治疗后(Δ节点-Δ网络关联)。使用类似的分析,我们还检查了两个简单的非因果节点属性(平均症状严重程度和症状出现频率)的预测有效性。
在三种中心度测量中,只有预期影响成功地预测了节点/症状的变化与网络中其余节点/症状的变化之间的关联强度。重要的是,当从网络分析中排除遗忘症节点时,遗忘症是 PTSD 现象学中众所周知的异常值,测试的中心度测量都没有预测症状变化。相反,平均症状严重程度和症状出现频率这两个标准的非网络衍生指数,比预期影响更具预测性,并且在从网络分析中排除遗忘症后仍然具有显著的预测性。
以目前的形式,中心度假设定义不明确,在横断面、个体间网络的背景下没有一致的支持证据。