Anhedonia is apparent in different mental disorders and is suggested to be related to dysfunctions in the reward system and/or affect regulation. It may hence be a common underlying feature associated with symptom severity of mental disorders.

We constructed a cross-sectional graphical Least Absolute Shrinkage and Selection Operator (LASSO) network and a relative importance network to estimate the relationships between anhedonia severity and the severity of symptom clusters of major depressive disorder (MDD), anxiety sensitivity (AS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) in a sample of Dutch adult psychiatric patients (N = 557).

Both these networks revealed anhedonia severity and depression symptom severity as central to the network. Results suggest that anhedonia severity may be predictive of the severity of symptom clusters of MDD, AS, ADHD, and ASD. MDD symptom severity may be predictive of AS and ADHD symptom severity.

The results suggest that anhedonia may serve as a common underlying transdiagnostic psychopathology feature, predictive of the severity of symptom clusters of depression, AS, ADHD, and ASD. Thus, anhedonia may be associated with the high comorbidity between these symptom clusters and disorders. If our results will be replicated in future studies, it is recommended for clinicians to be more vigilant about screening for anhedonia and/or depression severity in individuals diagnosed with an anxiety disorder, ADHD and/or ASD.

The ICD-11 includes a new disorder, complex post-traumatic stress disorder (CPTSD). A network approach to CPTSD will enable investigation of the structure of the disorder at the symptom level, which may inform the development of treatments that target specific symptoms to accelerate clinical outcomes.

We aimed to test whether similar networks of ICD-11 CPTSD replicate across culturally different samples and to investigate possible differences, using a network analysis.

We investigated the network models of four nationally representative, community-based cross-sectional samples drawn from Germany, Israel, the UK, and the USA (total N = 6417). CPTSD symptoms were assessed with the International Trauma Questionnaire in all samples. Only those participants who reported significant functional impairment by CPTSD symptoms were included (N = 1591 included in analysis; mean age 43.55 years, s.d. 15.10, range 14–99, 67.7% women). Regularised partial correlation networks were estimated for each sample and the resulting networks were compared.

Despite differences in traumatic experiences, symptom severity and symptom profiles, the networks were very similar across the four countries. The symptoms within dimensions were strongly associated with each other in all networks, except for the two symptom indicators assessing aspects of affective dysregulation. The most central symptoms were ‘feelings of worthlessness’ and ‘exaggerated startle response’.

The structure of CPTSD symptoms appears very similar across countries. Addressing symptoms with the strongest associations in the network, such as negative self-worth and startle reactivity, will likely result in rapid treatment response.

A.M. and M.C. were members of the World Health Organization (WHO) ICD-11 Working Group on the Classification of Disorders Specifically Associated with Stress, reporting to the WHO International Advisory Group for the Revision of ICD-10 Mental and Behavioural Disorders. The views expressed in this article are those of the authors and do not represent the official policies or positions of the International Advisory Group or the WHO.

It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques.

A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states (‘cheerful’, ‘content’, ‘down’, ‘insecure’ and ‘suspicious’) were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters.

Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics.

Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.