A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder

Jones, Payton J.; Mair, Patrick; Riemann, Bradley C.; Mugno, Beth L.; McNally, Richard J.

doi:10.1016/j.janxdis.2017.09.008

Cited by 151 publications

(80 citation statements)

References 43 publications

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“…Contrary to these findings, in a sample of 5952 Han Chinese women with recurrent MDD, psychomotor changes, hopelessness and decreased self-confidence were found to be the most central symptoms and among the least central was loss of interest [16]. Jones and others [17] concluded that concentration impairment, sadness, and fatigue were the most central nodes among individuals with obsessive-compulsive disorder with comorbid depression. These differences might be explained by variability in the samples, designs, and depression inventories used [12].…”

Section: Introductionmentioning

confidence: 70%

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study

Hakulinen

Fried

Pulkki-Råbäck

et al. 2020

Soc Psychiatry Psychiatr Epidemiol

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Purpose Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points. Methods Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD−) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients. Results Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among thosewith DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient. Conclusions Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.

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Section: Introductionmentioning

confidence: 70%

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study

Hakulinen

Fried

Pulkki-Råbäck

et al. 2020

Soc Psychiatry Psychiatr Epidemiol

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“…We found similar connections between obsession‐related interference and other OCD features as did McNally et al () in a Bayesian network analysis involving adults with OCD. However, a similar analysis involving adolescents with OCD did not (Jones, Mair, Riemann, Mugno, & McNally, ).…”

Section: Discussionmentioning

confidence: 89%

Obsessive–compulsive symptoms in eating disorders: A network investigation

Meier

Kossakowski

Jones

et al. 2019

Intl J Eating Disorders

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Objective Eating disorders (EDs) are complex, heterogeneous, and severe psychiatric syndromes. They are highly comorbid with obsessive–compulsive disorder (OCD) which exacerbates the course of illness and impedes treatment. However, the direct functional relations between EDs and OCD symptoms remain largely unexplored. Hence, using network analysis, we investigated the relationship between ED and OCD at the level of symptoms in a heterogeneous clinical sample. Method We used cross sectional data of 303 treatment‐seeking patients with clinically relevant ED and OCD pathology. We constructed a regularized partial correlation network that featured both ED and OCD symptoms as nodes. To determine each symptom's influence, we calculated expected influence (EI) as an index of symptom centrality (i.e., “importance”). Bridge symptoms (i.e., symptoms from one syndromic cluster that have strong connections to symptoms of another syndromic cluster) were identified by computing bridge expected influence metrics. Results Fear of weight gain and dietary restraint were especially important among the ED symptoms. Interference due to obsessions was the key feature of OCD. ED and OCD clustered distinctly with few potential bridges between clusters. Discussion This study underscores the importance of cognitive symptoms for both ED and OCD although direct functional links between the two clusters are missing. Potentially, a network incorporating nodes capturing features of personality may account for diagnostic comorbidity better than specific symptoms of EDs or features of OCD do.

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“…Cognitive models of OCD posit that dysfunctional beliefs (e.g. threat estimation, control of thoughts, tolerance of uncertainty, perfectionism) lie at the heart of the onset and maintenance of OCD symptoms (Jones, Mair, Riemann, Mugno, & McNally, 2018;McNally, Mair, Mugno, & Riemann, 2017;Tolin, 47 & Maltby, 2006). However such maladaptive beliefs have been shown to poorly differentiate between those with OCD and other anxiety diagnoses (Tolin et al, 2006;Viar, Bilsky, Armstrong, & Olatunji, 2011), leading some to propose that the obsessive component of OCD is reflective of anxiety (or indeed psychopathology) more generally (Tolin et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

In search of disorders: internalizing symptom networks in a large clinical sample

McElroy

Patalay

2019

Child Psychology Psychiatry

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Background The co‐occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence. Method Symptom‐level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms. Results The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8–11, 12–14 and 15–18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age‐specific relevance of certain symptoms. Conclusions These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders.

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A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder

Cited by 151 publications

References 43 publications

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000–2011 study

Obsessive–compulsive symptoms in eating disorders: A network investigation

In search of disorders: internalizing symptom networks in a large clinical sample

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