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Data
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Computational endophenotypes in addiction (Fiore et al 2018)
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Vincenzo G Fiore
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"... 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.
..."
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Fiore VG, Ognibene D, Adinoff B, Gu X (2018) Show
Other
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Fiore, Vincenzo G. [vincenzo.g.fiore at gmail.com] Show
Other
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Ognibene, Dimitri Show
Other
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vincenzo.g.fiore@gmail.com
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