Reduced Neural Recruitment for Bayesian Adjustment of Inhibitory Control in Methamphetamine Dependence

Abstract

Delineating the processes that contribute to the progression and maintenance of substance dependence is critical to understanding and preventing addiction. Several previous studies have shown inhibitory control deficits in individuals with stimulant use disorder. We used a Bayesian computational approach to examine potential neural deficiencies in the dynamic predictive processing underlying inhibitory function among recently abstinent methamphetamine-dependent individuals (MDIs), a population at high risk of relapse. Sixty-two MDIs were recruited from a 28-day inpatient treatment program at the San Diego Veterans Affairs Medical Center and compared with 34 healthy control subjects. They completed a stop-signal task during functional magnetic resonance imaging. A Bayesian ideal observer model was used to predict individuals’ trial-to-trial probabilistic expectations of inhibitory response, P(stop), to identify group differences specific to Bayesian expectation and prediction error computation. Relative to control subjects, MDIs were more likely to make stop errors on difficult trials and had attenuated slowing following stop errors. MDIs further exhibited reduced sensitivity as measured by the neural tracking of a Bayesian measure of surprise (unsigned prediction error), which was evident across all trials in the left posterior caudate and orbitofrontal cortex (Brodmann area 11), and selectively on stop error trials in the right thalamus and inferior parietal lobule. MDIs are less sensitive to surprising task events, both across trials and upon making commission errors, which may help explain why these individuals may not engage in switching strategy when the environment changes, leading to adverse consequences.

Amphetamine disrupts haemodynamic correlates of prediction errors in nucleus accumbens and orbitofrontal cortex

“…These outcome-driven signals did however correlate with an unsigned prediction error: how surprising or salient any outcome is based on current expectations. There are an increasing number of studies implicating OFC in modulating salience for the purposes of learning [71][72][73][74]. However, given the precise pattern of OFC signals observed in the current study, the OFC T O2 responses might reflect the acquired salience of an outcome, e.g., refs.…”

Gender-related differences in cue-elicited cravings in Internet gaming disorder: The effects of deprivation

“…Multiple studies and theoretical models have suggested that reward systems contribute to IGD (Brand et al., 2016; Dong, Hu, & Lin, 2013; Dong et al., 2011; Dong & Potenza, 2014). Higher activation of the caudate may relate to increased striatal dopamine synthesis (Harle, Zhang, Ma, Yu, & Paulus, 2016; Perry et al., 2015; van Holst et al., 2017). The positive correlation between caudate activation and self-reported craving suggests that the higher the caudate activates when experiencing gaming cues, the greater the motivation to game.…”

Bayesian computational markers of relapse in methamphetamine dependence

“…Recently, we showed that both healthy individuals (Ide et al, 2013) and stimulant users (Harlé et al, 2014; Harlé et al, 2016) continuously alter their response strategy in a standard inhibitory paradigm (stop-signal task, SST), such that dynamic fluctuations in their reaction time and performance are consistent with a Bayesian sequential adjustment of their beliefs (Yu and Cohen, 2009) and decision strategy (Shenoy and Yu, 2011). Whereas standard behavioral measures to assess performance in the SST, such as mean stop-signal reaction time (SSRT) or stop signal asynchrony dependent error rate, are relatively easy to obtain, Bayes-optimal model parameters have the advantage that they provide quantitative explanatory measures of an underlying putative cognitive process.…”