Browsing by Author "Wang, John M."

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    Associability-modulated loss learning is increased in posttraumatic stress disorder
    Brown, Vanessa M.; Zhu, Lusha; Wang, John M.; Frueh, B. Christopher; Casas, Brooks; Chiu, Pearl H. (eLife Sciences, 2018-01-09)
    Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat- deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention- based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets.
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    In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use
    Wang, John M.; Zhu, Lusha; Brown, Vanessa M.; De La Garza, Richard II; Newton, Thomas F.; Casas, Brooks; Chiu, Pearl H. (Elsevier, 2018-08-03)
    Background: In substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state–dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility. Methods: In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects’ learning effects were assessed with computational model–derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α−), neural prediction error signals (δ+, δ−), cocaine use, and desire to use were assessed. Results: During cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine. Conclusions: Neurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction. © 2018
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    Male Carriers of the FMR1 Premutation Show Altered Hippocampal-Prefrontal Function During Memory Encoding
    Wang, John M.; Koldewyn, Kami; Hashimoto, Ryuichiro; Schneider, Andrea; Le, Lien; Tassone, Flora; Cheung, Katherine; Hagerman, Paul J.; Hess, Davidl; Rivera, Susan M. (Frontiers Media S.A., 2012-10-30)
    Previous functional MRI (fMRI) studies have shown that fragile X mental retardation 1 (FMR1) premutation allele carriers (FXPCs) exhibit decreased hippocampal activation during a recall task and lower inferior frontal activation during a working memory task compared to matched controls. The molecular characteristics of FXPCs includes 55 to 200 CGG trinucleoutide expansions, increased FMR1 mRNA levels, and decreased FMRP levels especially at higher repeat sizes. In the current study, we utilized MRI to examine differences in hippocampal volume and function during an encoding task in young male FXPCs. While no decreases in either hippocampal volume or hippocampal activity were observed during the encoding task in FXPCs, FMRP level (measured in blood) correlated with decreases in parahippocampal activation. In addition, activity in the right dorsolateral prefrontal cortex during correctly encoded trials correlated negatively with mRNA levels. These results, as well as the established biological effects associated with elevated mRNA levels and decreased FMRP levels on dendritic maturation and axonal growth, prompted us to explore functional connectivity between the hippocampus, prefrontal cortex, and parahippocampal gyrus using a psychophysiological interaction analysis. In FXPCs, the right hippocampus evinced significantly lower connectivity with right ventrolateral prefrontal cortex (VLPFC) and right parahippocampal gyrus. Furthermore, the weaker connectivity between the right hippocampus and VLPFC was associated with reduced FMRP in the FXPC group. These results suggest that while FXPCs show relatively typical brain response during encoding, faulty connectivity between frontal and hippocampal regions may have subsequent effects on recall and working memory.
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    Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy
    Brown, Vanessa M.; Zhu, Lusha; Solway, Alec; Wang, John M.; McCurry, Katherine L.; Casas, Brooks; Chiu, Pearl H. (American Medical Association, 2021-07-28)
    IMPORTANCE Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets. OBJECTIVE To determine associations among computational model–derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. DESIGN, SETTING, AND PARTICIPANTS In this mixed cross-sectional–cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021. MAIN OUTCOMES AND MEASURES Computational model–based analyses of participants’ choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT). RESULTS Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model–based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = −0.14; 95%credible interval [CrI], −0.12 to −0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t₉₇ = −2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = −0.11; 95% CrI, −0.20 to −0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = −0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77). CONCLUSIONS AND RELEVANCE In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.
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    Trajectories of Risk Learning and Real-World Risky Behaviors During Adolescence
    Wang, John M. (Virginia Tech, 2020-08-31)
    Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves (Casey, Jones, and Hare, 2008), often choosing among options about which they have little knowledge and experience. This process of individuation and independence is reflected as real-world risk taking behaviors (Silveri et al., 2004), including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death (Casey et al., 2008). The extent to which adolescents continue to display increased behaviors with negative consequences during this period of life depends critically on their ability to explore and learn potential consequences of actions within novel environments. This learning is not limited to the value of the outcome associated with making choices, but extends to the levels of risk taken in making those choices. While the existing adolescence literature has focused on neural substrates of risk preferences, how adolescents behaviorally and neurally learn about risks remain unknown. Success or failure to learn the potential variability of these consequences, or the risks involved, in ambiguous decisions is hypothesized to be a crucial process to allow the individuals to make decisions based on their risk preferences. The alternative in which adolescents fail to learn about the risks involved in their decisions leaves the adolescent in a state of continued exploration of the ambiguity, reflected as continued risk-taking behavior. This dissertation comprises 2 papers. The first paper is a perspective paper outlining a paradigm that risk taking behavior observed during adolescents may be a product of each adolescent's abilities to learn about risk. The second paper builds on the hypothesis of the perspective paper by first examining neural correlates of risk learning and quantifying individual risk learning abilities and then examining longitudinal risk learning developmental trajectories in relation to real-world risk-trajectories in adolescent individuals.