BackgroundDepression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across... Show moreBackgroundDepression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across episodes, some evidence indicates that suicidal ideation may be an exception. However, these findings warrant replication, especially over longer periods and across multiple episodes.AimsTo assess the relative stability of suicidal ideation in comparison with other non-core depressive symptoms across episodes.MethodWe examined 490 individuals with current major depressive disorder (MDD) at baseline and at least one subsequent episode during 9-year follow-up within the Netherlands Study of Depression and Anxiety (NESDA). The Inventory of Depressive Symptomatology (IDS) was used to assess DSM-5 non-core MDD symptoms (fatigue, appetite/weight change, sleep disturbance, psychomotor disturbance, concentration difficulties, worthlessness/guilt, suicidal ideation) at baseline and 2-, 4-, 6- and 9-year follow-up. We examined consistency in symptom presentation (i.e. whether the symptom met the diagnostic threshold, based on a binary categorisation of the IDS) using kappa (κ) and percentage agreement, and stability in symptom severity using Spearman correlation, based on the continuous IDS scores.ResultsOut of all non-core depressive symptoms, insomnia appeared the most stable across episodes (r = 0.55–0.69, κ = 0.31–0.47) and weight decrease the least stable (r = 0.03–0.33, κ = 0.06–0.19). For suicidal ideation, correlations across episodes ranged from r = 0.36 to r = 0.55 and consistency ranged from κ = 0.28 to κ = 0.49.ConclusionsSuicidal ideation is moderately stable in recurrent depression over 9 years. Contrary to prior reports, however, it does not exhibit substantially more stability than most other non-core symptoms of depression. Show less
Despite decades of clinical, sociopolitical, and research efforts, progress in understanding and treating mental health problems remains disappointing. I discuss two barriers that have contributed... Show moreDespite decades of clinical, sociopolitical, and research efforts, progress in understanding and treating mental health problems remains disappointing. I discuss two barriers that have contributed to a problematic oversimplification of mental illness. The first is diagnostic literalism, mistaking mental health problems (complex within-person processes) for the diagnoses by which they are classified (clinically useful idealizations to facilitate treatment selection and prognosis). The second is reductionism, the isolated study of individual elements of mental disorders. I propose conceptualizing people's mental health states as outcomes emerging from complex systems of biological, psychological, and social elements and show that this systems perspective explains many robust phenomena, including variability within diagnoses, comorbidity among diagnoses, and transdiagnostic risk factors. It helps us understand diagnoses and reductionism as useful epistemological tools for describing the world, rather than ontological convictions about how the world is. It provides new lenses through which to study mental illness (e.g., attractor states, phase transitions), and new levers to treat them (e.g., early warning signals, novel treatment targets). Embracing the complexity of mental health problems requires opening our ivory towers to theories and methods from other fields with rich traditions, including network and systems sciences. Show less
Ron, J. de; Robinaugh, D.J.; Fried, E.I.; Pedrelli, P.; Jain, F.A.; Mischoulon, D.; Epskamp, S. 2022
Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact... Show moreNetwork psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error. Show less
Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis... Show morePsychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process. Show less
Objective There is a great variety of measurement instruments to assess similar constructs in clinical research and practice. This complicates the interpretation of test results and hampers the... Show moreObjective There is a great variety of measurement instruments to assess similar constructs in clinical research and practice. This complicates the interpretation of test results and hampers the implementation of measurement-based care. Method For reporting and discussing test results with patients, we suggest converting test results into universally applicable common metrics. Two well-established metrics are reviewed: T scores and percentile ranks. Their calculation is explained, their merits and drawbacks are discussed, and recommendations for the most convenient reference group are provided. Results We propose to express test results as T scores with the general population as reference group. To elucidate test results to patients, T scores may be supplemented with percentile ranks, based on data from a clinical sample. The practical benefits are demonstrated using the published data of four frequently used instruments for measuring depression: the CES-D, PHQ-9, BDI-II and the PROMIS depression measure. Discussion Recent initiatives have proposed to mandate a limited set of outcome measures to harmonize clinical measurement. However, the selected instruments are not without flaws and, potentially, this directive may hamper future instrument development. We recommend using common metrics as an alternative approach to harmonize test results in clinical practice, as this will facilitate the integration of measures in day-to-day practice. Show less
Roefs, A.; Fried, E.I.; Kindt, M.; Martijn, C.; Elzinga, B.M.; Evers, A.W.M.; ... ; Jansen, A. 2022
The core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating... Show moreThe core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating mental disorders, and adopts the network approach to psychopathology as its foundation of research. Its core assumption is that dynamically interacting symptoms constitute the disorder. Our goal is to further develop the network approach by studying (1) dynamic networks of symptoms and other variables (i.e., elements) in a large number of individuals with a wide range of mental disorders from a transdiagnostic perspective (network-based diagnosis; mapping), including both Ecological Momentary Assessment (EMA) and digital phenotyping, (2) the transdiagnostic mechanisms reflecting potential causal relations among elements of the networks by performing experimental (pre-)clinical studies (zooming), and (3) the effectiveness of personalised network-informed interventions (tar-geting). Challenges to overcome in this research program are discussed, which relate to data collection (e.g., selection of EMA variables) and data analyses (e.g., power considerations), the development and application of network-informed diagnoses and network-informed interventions (e.g., what characteristic(s) of the network to target in interventions), and the implementation in clinical practice (e.g., train therapists in the use of networks in therapy). Show less
