Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures

Abstract

Objective Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.Methods We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.Results In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.Conclusion Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

“…The dynamic fluctuations of central symptoms during a psychotherapeutic process have been highlighted by previous studies [54] and our results suggest that assessing and intervening in which symptom is central at any given time-point might produce faster recoveries. Consequently, the estimation at a single time-point of centrality measures to establish treatment targets for intervention might not be the most effective procedure to promote changes in the network structure.…”

“…However, evidence for this hypothesis is still scarce with studies focusing on cross-sectional networks and grounding the identification of possible therapeutic targets on the initial estimations of centrality measures [21][22][23]27]. Due to these inconclusive results, it has been recognized that there are changes in symptoms centrality that occur during treatment [54] and that idiographic networks might be more appropriate to identify treatment targets [35]. This might have important implications for treatment personalization.…”

Symptom-specific improvement across therapies and their putative mediators: a mediation network intervention analysis

“…Network analysis provides a tool to investigate the associations among multiple variables (Borsboom et al, 2021). A systematic review on intervention studies that used network analysis showed that most of the studies on patients with depression indicated that symptom associations and the connectedness of individual symptoms are different before and after treatment (Madhoo & Levine, 2016;McElroy et al, 2019;Park et al, 2021). However, these studies solely included depression symptoms in the networks (Blanco et al, 2020;Carney et al, 2018;Kaiser et al, 2021;Madhoo & Levine, 2016;McElroy et al, 2019).…”

An Exploratory Graphical Analysis of the Montgomery-Åsberg Depression Rating Scale Pre- and Post-Treatment using Pooled Antidepressant Trial Secondary Data

“…These indices can be used to identify the most central symptoms in a network model, with the aim of developing treatment interventions targeted at these specific symptoms (Borsboom 2017;Borsboom & Cramer 2013). Studies using clinical trial data support this contention (Park et al 2021;Bringmann et al 2015, Maciaszek et al 2023. For example, a network analysis of 88 outpatient clinic attendees showed that duloxetine may result in better outcomes, in terms of efficacy and tolerability, for patients with high levels of depressed mood.…”

“…While existing research (Park et al 2021;Bringmann et al 2015, Maciaszek et al 2023 provides support for the assertion that NA can be used to inform targeted treatment plans (Borsboom 2017;Borsboom & Cramer 2013;Maciaszek et al 2023), these studies have some critical limitations. For example, Whiston et al ( 2022) and An et al (2019) only examined pre-treatment data, at one time-point.…”