Computational phenotypes underlying effort-based decision-making and negative symptoms in a transdiagnostic severe...

Area of Science:

• Neuroscience
• Psychiatry
• Computational Psychiatry

Background:

• Impairments in effort-based decision-making (EBDM) are linked to negative symptoms across various psychiatric diagnoses.
• The concept of equifinality suggests different disorders may lead to similar outcomes (negative symptoms) through distinct EBDM mechanisms, but this remains unconfirmed.

Purpose of the Study:

• To investigate whether different psychiatric disorders utilize distinct computational processes in EBDM.
• To determine if a shared EBDM mechanism underlies negative symptoms across the severe mental illness spectrum, irrespective of diagnosis.

Main Methods:

• Utilized computational modeling to analyze EBDM processes in a large sample (n=920) including schizophrenia, bipolar disorder, depressive disorder, clinical high-risk for psychosis, and healthy controls.
• Assessed EBDM using the Effort Expenditure for Rewards Task, alongside negative symptom and neuropsychological assessments.
• Modeled EBDM behavior to differentiate between reliance on subjective value (reward magnitude/probability) versus bias (failure to use reward information).

Main Results:

• Computational models revealed significant differences in EBDM processes across diagnostic groups.
• Schizophrenia and first-episode psychosis groups were best fit by a bias model, indicating reduced use of reward magnitude and probability.
• Across all groups, a bias model fit was associated with more severe negative symptoms and cognitive impairments, independent of diagnosis.

Conclusions:

• Psychosis and mood spectrum disorders exhibit distinct EBDM approaches.
• Equifinality in the development of negative symptoms was not supported; impaired utilization of reward and probability information is a transdiagnostic EBDM mechanism linked to negative symptoms.