Claudia Delli Colli

Background: Network analysis is a promising approach for elucidating the dynamics of the transition from psychopathology to well-being. Recently, symptom connectivity strength has been proposed as a measure of plasticity – the capacity to change disease severity. Yet, empirical findings remain inconsistent. We propose that this inconsistency can be resolved by recognizing that the interpretation of connectivity strength varies along the recovery process from depression, whether at baseline or during clinical change. Methods: We analyzed 2,710 depressed patients from the STAR*D dataset, grouped by the magnitude of change in depressive score. Symptom network connectivity was estimated from QIDS-C items at three time points: (i) baseline, (ii) change – defined as when clinical change in depression score occurs, (iii) post-change - corresponding to when the maximum clinical change is achieved. Results: At baseline, connectivity strength predicts the maximum clinical change, inversely correlating with its magnitude (ρ = −0.95, p = 0.001). At the change time point, connectivity strength parallels clinical change (ρ = 0.92, p = 0.002). A direct and significant association between connectivity strength and depression severity emerges only at the change (ρ = 0.98, p = 0.0003) and post-change (ρ = 0.95, p = 0.001) time points. Conclusions: The interpretation of connectivity strength for predicting depression trajectories varies by timepoint: at baseline, it measures plasticity -- the capacity for change -- whereas during clinical change, it indicates the magnitude of change in symptom severity. This framework supports the reliability of this prognostic marker for designing personalized therapeutic interventions in psychiatry.

Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients’ plasticity – that is the susceptibility to modify the mental state – identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED. Symptom severity was assessed using the Quick Inventory of Depressive Symptomatology while the context was measured at enrollment with the Quality-of-Life Enjoyment and Satisfaction Questionnaire. Patients were retrospectively grouped based on both their time to response or remission and their plasticity levels at baseline assessed through a network-based mathematical approach that operationalizes plasticity as the inverse of the symptom network connectivity strength. The results show that plasticity levels at baseline anticipate time to response and time to remission. Connectivity strength among symptoms is significantly lower – and thus plasticity higher – in patients experiencing a fast recovery. When the interplay between plasticity and context is considered, plasticity levels are predictive of disease trajectories only in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. In conclusion, the assessment of plasticity levels at baseline holds promise for predicting MDD trajectories, potentially informing the design of personalized treatments and interventions. The combination of high plasticity and the experience of a favorable context emerges as critical to achieve recovery.

Recently, network-based approaches have provided an important contribution for the understanding of mental disorders. A growing number of statistical models, developed in the context of continuous variables in high-dimensional settings, are currently being used to infer dependencies between network elements (e.g., symptoms or behavioral elements) in psychometrics. However, psychometric datasets typically correspond to low-dimensional statistical settings, namely with a low number of variables collected from a large enough sample size and the variables collected are ordinal rather than Gaussian. In this large-scale simulation study, we tested and compared the performance of 14 methodological approaches including several that, to our knowledge, have never been tested in the context of psychometrics network inference. We assessed the impact of various factors such as the sample size, the number of variables (i.e., network elements), the density of the true underlying graph and the number of ordinal levels. We conclude that the simple and classic statistical methods are undervalued in the current practice, while polychoric correlations appear to have limited additional benefits. We recommend researchers to systematically rely on more than one method in their analyses.

Citation: Vezzoni R, Colli C, Conforti C, et al. Syphilitic balanitis of Follmann: can dermoscopy be useful tool in the differential diagnosis? Dermatol Pract Concept. 2022;12(1):e2022022. DOI: https://doi.org/10.5826/dpc.1201a22 Accepted: June 2, 2021; Published: January 2022 Copyright: ©2022 Vezzoni et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License BY-NC-4.0, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original authors and source are credited. Funding: None. Competing interests: None. Authorship: All authors have contributed significantly to this publication Corresponding author: Claudio Conforti, MD, Dermatology Clinic, Maggiore Hospital of Trieste, Trieste, Italy. Email: Claudioconforti@yahoo.com Keywords : syphilitic balanitis of Follmann, dermoscopy, dermatology, urology, sexually transmitted diseases This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.