ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2016
Reduced Neural Recruitment for Bayesian Adjustment of Inhibitory Control in Methamphetamine Dependence.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- Abnormal DLPFC activation in severe methamphetamine use disorder during abstinence: fNIRS evidence from Stroop task.Frontiers in psychiatry · 2026Article
- Interoceptive Processing in Substance Use Disorders (SUDs): From the Neuroanatomy to Insights from Computational Models and Predictive Coding Frameworks.Current topics in behavioral neurosciences · 2025Review
- Social context and drug cues modulate inhibitory control in cocaine addiction: involvement of the STN evidenced through functional MRI.Molecular psychiatry · 2024Article
- Assessing social anhedonia in a transdiagnostic sample: Insights from a computational psychiatry lens.Journal of mood and anxiety disorders · 2024Article
- Motivational context and neurocomputation of stop expectation moderate early attention responses supporting proactive inhibitory control.Frontiers in human neuroscience · 2024Article
- Impulsivity in amphetamine use disorder: Examination of sex differences.Addiction (Abingdon, England) · 2023Article
- State-specific alterations in the neural computations underlying inhibitory control in women remitted from bulimia nervosa.Molecular psychiatry · 2023Article
- Altered prefrontal signaling during inhibitory control in a salient drug context in cocaine use disorder.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- The ABCD stop signal data: Response to Bissett et al.Developmental cognitive neuroscience · 2022Article
- Temporally dynamic neural correlates of drug cue reactivity, response inhibition, and methamphetamine-related response inhibition in people with methamphetamine use disorder.Scientific reports · 2022Article
- An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users.The European journal of neuroscience · 2020Article
- Proactive engagement of cognitive control modulates implicit approach-avoidance bias.Cognitive, affective & behavioral neuroscience · 2020Article
- Neurobiology, Clinical Presentation, and Treatment of Methamphetamine Use Disorder: A Review.JAMA psychiatry · 2020Review
- Neurocomputational Changes in Inhibitory Control Associated With Prolonged Exposure Therapy.Journal of traumatic stress · 2020Article
- Driven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry.Biological psychiatry · 2020Review
- Bayesian computational markers of relapse in methamphetamine dependence.NeuroImage. Clinical · 2019Article
- Gender-related differences in cue-elicited cravings in Internet gaming disorder: The effects of deprivation.Journal of behavioral addictions · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.