Gu, Xiaosi

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6343

Xiaosi Gu is an Associate Professor and head of the Computational Psychiatry Unit at the Center for BrainHealth. Her research has been focused mainly on computational psychiatry, or using quantitative methods to characterize mental disorders. She wants to more efficiently target different parts of the brain and different neurotransmitter systems. Currently her research interests are addiction and depression.

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    A Multilevel Computational Characterization of Endophenotypes in Addiction
    (Society for Neuroscience) Fiore, Vincenzo G.; Ognibene, D.; Adinoff, B.; Gu, Xiaosi; Fiore, Vincenzo G.; Gu, Xiaosi
    Addiction is characterized by a profound intersubject (phenotypic) variability in the expression of addictive symptomatology and propensity to relapse following treatment. However, laboratory investigations have primarily focused on common neural substrates in addiction and have not yet been able to identify mechanisms that can account for the multifaceted phenotypic behaviors reported in the literature. To fill this knowledge gap theoretically, 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 (RL). 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. Furthermore, endophenotypes with opposite asymmetrical biases among cortico-striatal circuits expressed similar addiction behaviors, but responded differently to simulated treatments, suggesting personalized treatment development could rely on endophenotypic rather than phenotypic differentiations. We propose our simulated results, confirmed across neural and algorithmic levels of analysis, inform on a fundamental and, to date, neglected quantitative method to characterize clinical heterogeneity in addiction. © 2018 Fiore et al.
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    Incubation of Craving: A Bayesian Account
    (Nature Publishing Group) Gu, Xiaosi; 0000-0002-9373-987X (Gu, X); Gu, Xiaosi
    No abstract available.

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