The Large-Scale Brain Network Model of Sleep Deprivation on Risky Decision Making

Chen Xing; Guo Bowen; Yan Kaikai; Mao Tianxin; Rao Hengyi

doi:10.16719/j.cnki.1671-6981.20260108

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Journal of Psychological Science ›› 2026, Vol. 49 ›› Issue (1) : 68-81. DOI: 10.16719/j.cnki.1671-6981.20260108

General Psychology，Experimental Psychology & Ergonomics

The Large-Scale Brain Network Model of Sleep Deprivation on Risky Decision Making

Chen Xing, Guo Bowen, Yan Kaikai, Mao Tianxin, Rao Hengyi

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Abstract

Sleep is a fundamental physiological phenomenon that is essential for physical health, cognitive ability and emotional regulation. However, with technological advances and the accelerated pace of life, sleep deprivation has become increasingly prevalent, significantly impairing the cognitive and emotional functioning of individuals. Risky decision making, as a type of uncertain decision making, refers to the process by which people weigh options that have multiple outcomes and the probability of each outcome occurring is known. People make risky decisions all the time in their daily lives and at work. Most studies have confirmed that sleep deprivation significantly affects an individual's risky decision making preferences.
The neural processes by which sleep deprivation affects risky decision-making involve three main large-scale brain networks: the central executive network, the reward network, and the salience network. Specifically, when individuals experience total sleep deprivation, the activation level of the central executive network is significantly reduced, i.e., the dorsolateral prefrontal activation level decreases and the individual's inhibitory control is severely impaired. The activation levels of the orbitofrontal cortex and the ventral medial prefrontal within the reward network decreased, but the activation level of the striatum was enhanced, and the brain regions interacted with each other to greatly weaken the individual's ability to resist immediate rewards and avoid impulsive behaviors. At the same time, decreased activation levels in the amygdala and the anterior insula within the salience network, but enhanced activation levels in the anterior cingulate cortex, lead individuals to make more irrational decisions. The same three large-scale brain networks are included when individuals with partial sleep deprivation make risky decisions. The difference is that partial sleep deprivation only significantly decreases activation levels in the dorsolateral prefrontal and enhances activation levels in the anterior insula. However, partial sleep deprivation reduces the functional connectivity of the dorsolateral prefrontal and striatum, the anterior insula, and the orbitofrontal cortex and amygdala, resulting in the inability of individuals to effectively inhibit high-risk behaviors and reduce decision-making performance.
Previous studies have mostly focused on the effects of different levels of sleep deprivation on the level of activation in specific brain regions and single brain networks, but ignored the overall role of large-scale brain networks. It has been found that the brain integrates and processes information in the form of brain networks, and multiple brain networks work together to ultimately change an individual's behavioral performance. Complete sleep deprivation affects an individual's risky decision-making performance by directly altering the activation levels of the central executive, reward, and salience networks. When an individual receives a reward or suffers a loss, the activation of the reward network or salience network is further enhanced, which in turn affects the central executive network and ultimately alters the individual's subsequent risky decision-making performance. The feedback mechanism for risky decision-making in partial sleep deprivation is impaired, making it difficult to effectively regulate individual decision-making behavior. As in the case of total sleep deprivation, the results of risky decision making in individuals with partial sleep deprivation were fed back to the reward and salience networks, which influenced the individual's future decision making.
Future research is suggested to further explore the following issues. Considering the development prospects of machine learning and deep learning technologies, future research should use these technologies to computationally model the rich brain network data to further deepen the understanding of brain function and structure. In addition, the dynamic effects of different degrees of sleep deprivation on risky decision making are further refined by carefully dividing sleep deprivation time. At the same time, the generalizability of the effects of sleep deprivation on decision making is explored.

Key words

total sleep deprivation / partial sleep deprivation / risk decision-making / large-scale brain network model

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Chen Xing, Guo Bowen, Yan Kaikai, Mao Tianxin, Rao Hengyi. The Large-Scale Brain Network Model of Sleep Deprivation on Risky Decision Making[J]. Journal of Psychological Science. 2026, 49(1): 68-81 https://doi.org/10.16719/j.cnki.1671-6981.20260108

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