The computational and neural substrates of individual differences in impulsivity under loss framework

• Published in: Human brain mapping Vol. 45; no. 11; pp. e26808 - n/a
• Main Authors: Jiang, Keying, Zhao, Guang, Feng, Qian, Guan, Shunping, Im, Hohjin, Zhang, Bin, Wang, Pinchun, Jia, Xuji, Zhu, Haidong, Zhu, Ye, Wang, He, Wang, Qiang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2024
• Subjects: Adult; Bayes Theorem; Bayesian analysis; Brain - diagnostic imaging; Brain - physiology; Brain Mapping; Brain research; Cognition & reasoning; Computational neuroscience; Cortex (parietal); Decision making; Delay Discounting - physiology; Diffusion models; Diffusion rate; Drift; Drift rate; Female; Humans; Hypotheses; Impulsive behavior; Impulsive Behavior - physiology; Impulsivity; individual variability; Individuality; intertemporal choice; inter‐subject representational similarity analysis; Investigations; loss framework; Magnetic Resonance Imaging; Male; Medical imaging; Mental disorders; Neuroimaging; Personality; Personality traits; Prefrontal cortex; Substantia grisea; Substrates; Young Adult; intertemporal choice; individual variability; loss framework; inter‐subject representational similarity analysis
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26808

Numerous neuroimaging studies have identified significant individual variability in intertemporal choice, often attributed to three neural mechanisms: (1) increased reward circuit activity, (2) decreased cognitive control, and (3) prospection ability. These mechanisms that explain impulsivity, however, have been primarily studied in the gain domain. This study extends this investigation to the loss domain. We employed a hierarchical Bayesian drift‐diffusion model (DDM) and the inter‐subject representational similarity approach (IS‐RSA) to investigate the potential computational neural substrates underlying impulsivity in loss domain across two experiments (n = 155). These experiments utilized a revised intertemporal task that independently manipulated the amounts of immediate and delayed‐loss options. Behavioral results demonstrated positive correlations between the drift rate, measured by the DDM, and the impulsivity index K in Exp. 1 (n = 97) and were replicated in Exp. 2 (n = 58). Imaging analyses further revealed that the drift rate significantly mediated the relations between brain properties (e.g., prefrontal cortex activations and gray matter volume in the orbitofrontal cortex and precuneus) and K in Exp. 1. IS‐RSA analyses indicated that variability in the drift rate also mediated the associations between inter‐subject variations in activation patterns and individual differences in K. These findings suggest that individuals with similar impulsivity levels are likely to exhibit similar value processing patterns, providing a potential explanation for individual differences in impulsivity within a loss framework. The current study employed a hierarchical Bayesian drift‐diffusion model and inter‐subject representational similarity approaches across two experiments to investigate the neural substrates underlying intertemporal choices under loss framework. The findings underscore the multifaceted nature of impulsivity, highlighting the interplay between computational and neural factors.