From Computation to Clinic.

Area of Science:

• Computational psychiatry
• Translational neuroscience
• Cognitive and affective modeling

Background:

• Computational approaches in psychiatry show promise for elucidating disease mechanisms.
• Current utility is mainly in back-translation (clinic to computation), mapping disorders to computational processes.
• Forward translation (computation to clinic) of these models is limited, with unclear barriers and solutions.

Purpose of the Study:

• To identify challenges in preclinical and clinical translation of computational models of cognition and affect.
• To discuss practical strategies for overcoming these identified translational barriers.

Main Methods:

• Perspective review synthesizing expert opinions from basic researchers, computational scientists, and clinicians.
• Analysis of existing evidence on computational models' ability to predict treatment response in psychiatric disorders.
• Discussion of considerations for enhancing clinical relevance and stakeholder adoption.

Main Results:

• Identified specific challenges in translating computational models from preclinical systems to clinical application.
• Proposed practical approaches to overcome barriers in computational psychiatry translation.
• Highlighted evidence for computational models predicting treatment outcomes and discussed maximizing clinical utility.

Conclusions:

• Addressing translational barriers is crucial for realizing the full potential of computational psychiatry.
• Strategies such as longitudinal testing and cost-effectiveness analyses can enhance clinical relevance and adoption.
• Further research and collaboration are needed to bridge the gap between computational models and clinical practice.