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

Network structure of social withdrawal symptoms in Asian psychiatric patients at high risk of hikikomori: Findings from the REAP-AD3

BACKGROUND

Hikikomori is a severe pathological form of social withdrawal that first emerged in Japan in the late 20th century and has since become a global phenomenon. This was recently added to the cultural concept of distress in the DSM-5-TR.

OBJECTIVE

This study aimed to examine the precise network structure of social withdrawal symptoms in Asian psychiatric patients at high risk of hikikomori using data from Phase 3 of the Research on Asian Psychotropic Prescription Patterns for Antidepressants.

METHODS

High risk of hikikomori was defined as a score ≥ 42 on the 1-month version of the 25-item Hikikomori Questionnaire (HQ-25M), a scale that measures social withdrawal symptoms. The HQ-25M network structures were estimated separately for patients at high and low risks of hikikomori. The differences in network structure invariance and global strength invariance between the two networks were evaluated. Data from 2993 participants were assessed, including 1939 and 1054 patients at high and low risk of hikikomori, respectively.

RESULTS

Network analysis revealed that enjoyment of social activities was the most central symptom among patients at high risk of hikikomori, whereas trust issues were the most central among those at low risk of hikikomori. In addition, although no significant differences were identified in the overall network structures, the global strength invariance differed significantly between networks.

CONCLUSION

While the study has several limitations, the findings may point to potential differences in how social withdrawal symptoms are structured between individuals with high versus low risk of hikikomori, particularly with regard to the overall connectivity among symptoms. A notable finding is that low enjoyment of social interactions may be a main area for early intervention. However, given that the participants were all psychiatric patients receiving antidepressant medication and able to attend in-person evaluations, the applicability of these results to non-clinical groups or individuals with more severe social withdrawal may be restricted.

Symptom networks of common mental disorders in public versus private healthcare settings in India

We present a series of network analyses aiming to uncover the symptom constellations of depression, anxiety and somatization among 2,796 adult primary health care attendees in Goa, India, a low- and middle-income country (LMIC). Depression and anxiety are the leading neuropsychiatric causes of disability. Yet, the diagnostic boundaries and the characteristics of their dynamically intertwined symptom constellations remain obscure, particularly in non-Western settings. Regularized partial correlation networks were estimated and the diagnostic boundaries were explored using community detection analysis. The global and local connectivity of network structures of public versus private healthcare settings and treatment responders versus nonresponders were compared with a permutation test. Overall, depressed mood, panic, fatigue, concentration problems and somatic symptoms were the most central. Leveraging the longitudinal nature of the data, our analyses revealed baseline networks did not differ across treatment responders and nonresponders. The results did not support distinct illness subclusters of the CMDs. For public healthcare settings, panic was the most central symptom, whereas in private, fatigue was the most central. Findings highlight varying mechanism of illness development across socioeconomic backgrounds, with potential implications for case identification and treatment. This is the first study directly comparing the symptom constellations of two socioeconomically different groups in an LMIC.

An exploratory graphical analysis of the Montgomery-Åsberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data

BACKGROUND

The 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups.

AIMS

To assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention).

METHOD

Individual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models.

RESULTS

Empirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models.

LIMITATIONS

Metric invariance could not be explored due to research environment limitations.

CONCLUSIONS

An 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.

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

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.