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. 2025 Aug;10(8):856-864.
doi: 10.1016/j.bpsc.2024.12.010. Epub 2024 Dec 26.

Deconstructing Delay Discounting in Human Cocaine Addiction Using Computational Modeling and Neuroimaging

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Free article

Deconstructing Delay Discounting in Human Cocaine Addiction Using Computational Modeling and Neuroimaging

Michal M Graczyk et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Aug.
Free article

Abstract

Background: A preference for sooner-smaller over later-larger rewards, known as delay discounting, is a candidate transdiagnostic marker of waiting impulsivity and a research domain criterion. While abnormal discounting rates have been associated with many psychiatric diagnoses and abnormal brain structure, the underlying neuropsychological processes remain largely unknown. Here, we deconstruct delay discounting into choice and rate processes by testing different computational models and investigate their associations with white matter tracts.

Methods: Patients with cocaine use disorder (CUD) (n = 107) and healthy participants (n = 81) completed the Monetary Choice Questionnaire. We computed their discounting rate using the well-known Kirby method, as well as logistic regression, single-subject Bayesian, and full hierarchical Bayesian models. In Bayesian models, we also included a choice sharpness parameter. Seventy patients with CUD and 69 healthy participants also underwent diffusion tensor imaging tractography to quantify streamlines that connect the executive control and valuation brain networks.

Results: Patients with CUD showed significantly higher discounting rates and lower choice sharpness, suggesting greater indifference in their choices. Importantly, the full Bayesian model had the greatest reliability for parameter recovery when compared to the Kirby and logistic regression methods. Using Bayesian estimates, we found that white matter streamlines that connect the executive control network with the nucleus accumbens predicted the discounting rate in healthy participants but not in patients with CUD.

Conclusions: We demonstrated that measuring delay discounting and choice sharpness directly with a novel computational model explained impulsive discounting choices in patients with CUD better than standard hyperbolic discounting. Our findings highlight a distinct neuropsychological phenotype of impulsive discounting, which may be generalizable to other patient groups.

Keywords: Bayesian model; Brain networks; Decision making; Impulsivity; Intertemporal choice; Research domain criteria.

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