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Addressing Altered Anticipation as a Transdiagnostic Target Through Computational Psychiatry
Pradyumna Sepúlveda1, Ines Aitsahalia2, Krishan Kumar2
1Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York; Italian Academy for Advanced Studies, Columbia University, New York, New York.
Summary
Altered anticipation affects many psychiatric conditions. Computational psychiatry offers new ways to understand these changes and develop targeted treatments for better mental health outcomes.
Area of Science:
- Cognitive Neuroscience
- Computational Psychiatry
- Psychiatric Disorders
Background:
- Anticipation of future experiences is vital but poorly understood in psychiatric conditions.
- Current research has limitations in explaining altered anticipation mechanisms and treatments.
Purpose of the Study:
- To propose an integrated computational psychiatry approach for understanding altered anticipation.
- To explore how computational modeling can offer deeper insights into psychiatric conditions.
Main Methods:
- Reviewing altered anticipation across schizophrenia, depression, anxiety, substance use, and eating disorders.
- Summarizing insights from self-report scales and neuroimaging.
- Exploring computational modeling (reinforcement learning, anticipatory utility theory).
Main Results:
- Altered anticipation is a transdiagnostic feature in various psychiatric disorders.
- Computational models offer potential to overcome limitations of traditional methods.
- These approaches can reveal underlying mechanisms and individual variations.
Conclusions:
- An integrated computational psychiatry approach can provide comprehensive transdiagnostic insights.
- This approach aids in discovering novel therapeutic targets.
- It advances the field of precision psychiatry for improved patient care.
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