Computational endophenotypes in addiction (Fiore et al 2018)

"... here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. ..."

Region(s) or Organism(s): Striatum; Basal ganglia

Transmitters: Dopamine

Model Concept(s): Addiction; Learning; Reinforcement Learning

Simulation Environment: MATLAB

Implementer(s): Fiore, Vincenzo G. [vincenzo.g.fiore at]; Ognibene, Dimitri


Fiore VG, Ognibene D, Adinoff B, Gu X. (2018). A Multilevel Computational Characterization of Endophenotypes in Addiction eNeuro.

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