Moving From Static to Dynamic Models of the Onset of Mental Disorder: A Review

摘要: Importance In recent years, there has been increased focus on subthreshold stages of mental disorders, with attempts to model and predict which individuals will progress full-threshold disorder. Given this research attention the clinical significance issue, article analyzes assumptions theoretical models in field. Observations Psychiatric into predicting onset disorder shown an overreliance one-off sampling cross-sectional data (ie, a snapshot state other risk markers) may benefit from taking dynamic changes account predictive modeling. Cross-disciplinary approaches complex system structures changes, such as dynamical systems theory, network instability mechanisms, chaos catastrophe offer potent that can be applied emergence (or decline) psychopathology, including psychosis prediction, well transdiagnostic symptoms. Conclusions Relevance approaching psychopathology rather than category, identifying dynamics change (eg, abrupt vs gradual onset), determining factors these are most sensitive interpersonal neurochemical change) individual variability architecture change. These goals advanced by testing hypotheses emerge cross-disciplinary systems. Future studies require repeated longitudinal assessment relevant variables through either combination of) micro-level (momentary day-to-day) macro-level (month year) assessments. Ecological momentary is collection technique appropriate for assessment. Relevant statistical joint modeling time series analysis, metric-based model-based methods draw mathematical principles This next generation prediction more accurately nature have treatment implications, introducing means critical periods deterioration.