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"Tapert, Susan F."

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Englisch

Ergebnisse 5 Einträge

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Gowin, J. L., Ball, T. M., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2017). Corrigendum to “Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse” [Drug and Alcohol Dependence 152 (2015) 93-101]. Drug and Alcohol Dependence, 175, 255. https://doi.org/10.1016/j.drugalcdep.2017.03.003
Gowin, J. L., May, A. C., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2017). Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 2(1), 94–103. https://doi.org/10.1016/j.bpsc.2016.02.002

BACKGROUND: Cocaine use disorders (CUDs) have been associated with increased risk-taking behavior. Neuroimaging studies have suggested that altered activity in reward and decision-making circuitry may underlie cocaine user's heightened risk-taking. It remains unclear if this behavior is driven by greater reward salience, lack of appreciation of danger, or another deficit in risk-related processing. METHODS: Twenty-nine CUD participants and forty healthy comparison participants completed the Risky Gains Task during a functional magnetic resonance imaging scan. During the Risky Gains Task, participants choose between a safe option for a small, guaranteed monetary reward and risky options with larger rewards but also the chance to lose money. Frequency of risky choice overall and following a win versus a loss were compared. Neural activity during the decision and outcome phase were examined using linear mixed effects models. RESULTS: Although the groups did not differ in overall risk-taking frequency, the CUD group chose a risky option more often following a loss. Neuroimaging analyses revealed that the comparison group showed increasing activity in the bilateral ventral striatum as they chose higher-value, risky options, but the CUD group failed to show this increase. During the outcome phase, the CUD group showed a greater decrease in bilateral striatal activity relative to the comparison group when losing the large amount, and this response was correlated with risk-taking frequency after a loss. CONCLUSIONS: The brains of CUD individuals are hypersensitive to losses, leading to increased risk-taking behaviors, and this may help explain why these individuals take drugs despite aversive outcomes.
Gowin, J. L., Ball, T. M., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2015). Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse. Drug and Alcohol Dependence, 152, 93–101. https://doi.org/10.1016/j.drugalcdep.2015.04.018

BACKGROUND: Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. METHODS: 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. RESULTS: 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. CONCLUSIONS: These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders.
Stewart, J. L., Connolly, C. G., May, A. C., Tapert, S. F., Wittmann, M., & Paulus, M. P. (2014). Cocaine dependent individuals with attenuated striatal activation during reinforcement learning are more susceptible to relapse. Psychiatry Research, 223(2), 129–139. https://doi.org/10.1016/j.pscychresns.2014.04.014

Cocaine-dependent individuals show altered brain activation during decision making. It is unclear, however, whether these activation differences are related to relapse vulnerability. This study tested the hypothesis that brain-activation patterns during reinforcement learning are linked to relapse 1 year later in individuals entering treatment for cocaine dependence. Subjects performed a Paper-Scissors-Rock task during functional magnetic resonance imaging (fMRI). A year later, we examined whether subjects had remained abstinent (n=15) or relapsed (n=15). Although the groups did not differ on demographic characteristics, behavioral performance, or lifetime substance use, abstinent patients reported greater motivation to win than relapsed patients. The fMRI results indicated that compared with abstinent individuals, relapsed users exhibited lower activation in (1) bilateral inferior frontal gyrus and striatum during decision making more generally; and (2) bilateral middle frontal gyrus and anterior insula during reward contingency learning in particular. Moreover, whereas abstinent patients exhibited greater left middle frontal and striatal activation to wins than losses, relapsed users did not demonstrate modulation in these regions as a function of outcome valence. Thus, individuals at high risk for relapse relative to those who are able to abstain allocate fewer neural resources to action-outcome contingency formation and decision making, as well as having less motivation to win on a laboratory-based task.
Gowin, J. L., Harlé, K. M., Stewart, J. L., Wittmann, M., Tapert, S. F., & Paulus, M. P. (2014). Attenuated insular processing during risk predicts relapse in early abstinent methamphetamine-dependent individuals. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 39(6), 1379–1387. https://doi.org/10.1038/npp.2013.333

There is some evidence that neuroimaging can be used to predict relapse among abstinent methamphetamine-dependent (MD) individuals. However, it remains unclear what cognitive and neural processes contribute to relapse. This investigation examined whether insula activation during risk-taking decisions-a process shown to be disrupted in MD-is able to predict susceptibility for relapse. Sixty-eight MD enrolled in a treatment program during early abstinence completed a risk-taking task during functional magnetic resonance imaging. Sixty-three of the sixty-eight individuals were followed up 1 year after the study. Of these, 18 MD reported relapse. The 45 abstinent MD showed patterns of insula activation during risky decisions that resembled those found in prior studies of healthy controls, consisting of lower insula activation during safe decisions paired with higher activation during risky decisions. In contrast, the 18 relapsed MD showed similar insula activation during safe and risky decisions. An increase in one standard deviation in the difference in insula activation between risky and safe choices was associated with a 0.34 odds ratio for relapse at any given time. A median split of insula activation (difference between risky and safe) showed that individuals in the bottom half were two times more likely to relapse. In addition, a model that included several other brain regions increased prediction accuracy compared with insula-based model alone. These results suggest that failure to differentially activate the insula as a function of risk is a part of an altered risk-processing network associated with an increased susceptibility to relapse.

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