Risk seeking for losses modulates the functional connectivity of the default mode and left frontoparietal networks in young males

Abstract

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.

Measuring self-regulation in everyday life: reliability and validity of smartphone-based experiments in alcohol use disorder

“…The latter further split into a risk taking for gains and mixed gambles cluster and a sampling bias cluster. The factor structure, as well as the clustering, are broadly consistent with theoretical predictions of a cognitive control dimension separate from a decision-making dimension that segregates gain and loss contexts (Deza Araujo et al, 2018).…”

“…In a recent historical review, Nigg (2017) concluded that most theories include working memory and inhibition in cognitive control (see also Botvinik & Braver, 2015), but separate them from decision-making such as risk taking. Furthermore, empirical evidence shows a distinction in neural circuits underlying risk taking to avoid losses and risk taking for gains (Deza Araujo et al, 2018). Risk taking for gains decreases with age, putatively reflecting decreases in dopamine (Rutledge et al, 2016), and risk taking for losses has been linked to circadian rhythms (Bedder et al, 2020).…”

Risk proneness modulates the impact of impulsivity on brain functional connectivity

“…Comparing risky with nonrisky adolescents, DeWitt, Aslan, and Filbey (2014) observed that the former group displayed increased connectivity between the amygdala and the right MFG, left cingulate gyrus, left precuneus, and right inferior parietal cortex and between the nucleus accumbens and the right MFG. In a similar vein, Deza Araujo et al (2018) demonstrated hyperconnectivity between the frontoparietal network and the occipital cortex and between the DMN and medial temporal and frontal regions in high risk-seeking behavior in losses (observed in people who prefer delayed potential high losses rather than immediate but sure small losses).…”

Ranking risk attitudes using an integrated AHP-TOPSIS approach

“…Other research work conducted by Araujo et al (2018) discussed the distinct connectivity patterns of large-scale brain networks that may underlie individual differences in decision-making in healthy populations. Results showed that higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the default mode network.…”