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A Multilevel Computational Characterization of Endophenotypes in Addiction.
Vincenzo G Fiore1, Dimitri Ognibene2,3, Bryon Adinoff4,5
1School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75080.
Eneuro
|August 4, 2018
Summary
Individual differences in brain circuitry explain addiction variability and treatment response. Understanding these endophenotypes can lead to personalized addiction treatments.
Area of Science:
- Neuroscience
- Computational Psychiatry
- Behavioral Economics
Background:
- Addiction exhibits significant inter-individual variability in symptoms and relapse rates.
- Current research often overlooks mechanisms underlying this phenotypic diversity.
- Focus on common neural substrates limits understanding of multifaceted addictive behaviors.
Purpose of the Study:
- To theoretically model phenotypic variations in addiction.
- To investigate the role of cortico-striatal circuit dynamics and reinforcement learning (RL) in addiction.
- To explore how individual differences (endophenotypes) influence addiction and treatment response.
Main Methods:
- Simulated addiction symptomology and treatment responses using a neural model of cortico-striatal circuits.
- Employed an algorithmic reinforcement learning (RL) model.
- Assessed the impact of dopamine reinforcement on ventral (goal-directed) and dorsal (habitual) systems.
Main Results:
- Endophenotypic differences in system balance predicted an inverted-U shape in optimal choice behavior.
- Greater imbalance between systems correlated with higher addiction likelihood and severity.
- Opposing endophenotypic biases showed similar behaviors but differential treatment responses.
Conclusions:
- Individual differences in brain circuit balance (endophenotypes) are crucial for understanding addiction heterogeneity.
- Personalized addiction treatments may benefit from targeting endophenotypic profiles over phenotypic ones.
- A quantitative approach to endophenotypes offers a novel method for characterizing clinical diversity in addiction.
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