A computational lens on menopause-associated psychosis

Area of Science:

• Neuroscience
• Computational Psychiatry
• Endocrinology

Background:

• Psychotic episodes cause significant distress and functional impairment.
• Women experience a second risk period for new-onset psychosis during menopause transition.
• Estrogen decline during menopause is hypothesized to remove a protective factor against psychosis.

Purpose of the Study:

• To explore the neural mechanisms linking estrogen decline to psychosis symptoms.
• To propose how estrogen influences latent states in information processing related to psychosis.
• To contextualize estrogen's effects on neural pathways implicated in psychosis.

Main Methods:

• Utilizing computational psychiatry tools to link symptom presentation with algorithmic and biological correlates.
• Modeling changes in information processing (latent states) to understand symptom formation.
• Reviewing and contextualizing the observed effects of estrogen decline on neural pathways.

Main Results:

• Estrogen decline may alter neural pathways involved in psychosis.
• Computational models suggest estrogen influences latent states related to cognitive and psychotic symptoms.
• This framework provides a systematic approach to understanding psychosis pathogenesis.

Conclusions:

• Computational frameworks can systematically identify patient-specific pathways in menopause-associated psychosis (MAP).
• This approach refines models of psychosis pathogenesis across different age groups.
• Understanding estrogen's role via computational psychiatry offers insights into psychosis mechanisms.