Temporal dynamics of premenstrual dysphoric disorder revealed through predictive symptom networks

Abstract Temporal dynamics of psychiatric symptoms challenge researchers and clinicians due to their inherent unpredictability. The cyclical on–off nature of symptoms linked to physiological rhythms, such as the menstrual cycle, provides a unique window to apply temporal and dynamic modeling of psychiatric symptoms. This can be studied in premenstrual dysphoric disorder, a female hormone-triggered condition characterized by cyclical symptoms. Yet intra-individual temporal patterns and dynamics of these symptoms remain unknown. Here we show that individuals with premenstrual dysphoric disorder differ widely in symptom timing, severity, and persistence, clustering into distinct temporal symptom profiles. Dynamic modelling uncovers temporal order among core psychiatric symptoms, with anxiety emerging as a central symptom in the unfolding of affective dysregulation. Depression shows the strongest predictive associations with subsequent severity of the other core symptoms, while irritability predicts the severity of depression at the longest time-interval. These temporal symptom dynamics reveal mechanistic psychoneuroendocrine pathways and inform precision, time-sensitive interventions for the mental health of individuals with premenstrual dysphoric disorder.