Epskamp2022 - New trends in network modeling of psychopathology
- Type:#article
- Date read: 2022-09-20
- Subject: (in brackets, can also bracket keywords in text)
- Bibtex: @epskamp2022
- Bibliography: Epskamp, S., & Isvoranu, A.-M. (2022). New trends in network modeling of psychopathology. World Psychiatry, 21(3), 463–464. https://doi.org/10.1002/wps.21017
Example citation
Recent developments include methods for aggregating results in meta-analyses, confirmatory network analysis and longitudinal network analysis [@epskamp2022].
Key takeaways
- (Reminder): It’s not just symptoms, but also external factors. They are both viewed as interacting components in a system.
- Network psychometrics as a sub-field of network approaches in psychiatry
- Promising developments
- Meta-analysis: meta-analytic Gaussian network aggregation (MAGNA).
- Exploratory -> Confirmatory network analysis. (There’s a new R-package for this too)
- Longitudinal network analysis (what I want to do with the IAPT data)
- Formal mathematical modeling. Instead of bottom-up approaches, formulating theoretical models based on prior knowledge.
- Very strong last paragraph, cited below.
Embracing the complexity of mental health. In attempting to obtain identifiable multivariate models that are estimable from practically obtainable datasets, network psychometrics may make concessions that make the models used deviate from the complex systems thinking that inspired network theory. For example, psychometric network models estimated form cross-sectional data mostly include only pairwise interactions, do not feature phase transitions between multiple stable states, and include only linear effects, while complex systems thinking often involves the presence of feedback loops, phase transitions between multiple stable states, interactions at different time-scales, and non-linear effects. It may be that the understanding of mental health requires all these concepts and more, being an interplay not only of symptoms but also of numerous other factors, ranging from biological to sociological factors, and from fast effects that span seconds to slow effects that span a lifetime. The development of methods that capture and explain this complexity may be one of the great challenges that mental health research is to face in the coming decades.