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Computational endophenotypes in addiction (Fiore et al 2018)
Vincenzo G Fiore
FioreEtAl2018 [125502]
"... 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. ..."
  • Fiore VG, Ognibene D, Adinoff B, Gu X (2018) Show Other
  • Fiore, Vincenzo G. [vincenzo.g.fiore at gmail.com] Show Other
  • Ognibene, Dimitri Show Other
vincenzo.g.fiore@gmail.com
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Revisions: 9
Last Time: 7/10/2018 2:21:53 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin