Previous research has shown that, in both laboratory and real-world contexts, punishment sensitivity is associated with lower risk-taking propensity. The neural underpinnings of the association between punishment sensitivity and risk-taking, however, remain largely unknown. To address this issue, we implemented resting-state functional connectivity (RSFC) and voxel-based morphometry (VBM) methodologies to investigate the neural basis of their relationship in the current study (N=594). The behavioral results confirmed a negative association between punishment sensitivity and risk-taking propensity, which supports the hypothesis. The VBM results demonstrated a positive correlation between punishment sensitivity and gray matter volume in the right orbitofrontal cortex (ROFC). Furthermore, the results of the RSFC analysis revealed that the functional connectivity between ROFC and the right medial temporal gyrus (RMTG) was positively associated with punishment sensitivity. Notably, mediation analysis demonstrated that punishment sensitivity acted as a complete mediator in the influence of ROFC-RMTG functional connectivity on risk-taking. These findings suggest that ROFC-RMTG functional connectivity may be the neural basis underlying the effect of punishment sensitivity on risk-taking propensity, which provides a new perspective for understanding the relationship between punishment sensitivity and risk-taking propensity.
Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.
The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.
Our results showed that both structural (r values 0.263–0.375) and functional (r values 0.336–0.503) connectomes can significantly predict individuals’ cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.
The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.
In overt movement, internal models make predictions about the sensory consequences of a desired movement, generating the appropriate motor commands to achieve that movement. Using available sensory feedback, internal models are updated to allow for movement adaptation and in-turn better performance. Whether internal models are updated during motor imagery, the mental rehearsal of movement, is not well established. To investigate internal modelling during motor imagery, 66 participants were exposed to a leftwards prism shift while performing actual pointing movements (physical practice; PP), imagined pointing movements (motor imagery; MI), or no pointing movements (control). If motor imagery updates internal models, we hypothesized that aftereffects (pointing in the direction opposite the prism shift) would be observed in MI, like that of PP, and unlike that of control. After prism exposure, the magnitude of aftereffects was significant in PP (4.73° ± 1.56°), but not in MI (0.34° ± 0.96°) and control (0.34° ± 1.04°). Accordingly, PP differed significantly from MI and control. Our results show that motor imagery does not update internal models, suggesting that it is not a direct simulation of overt movement. Furthering our understanding of the mechanisms that underlie learning through motor imagery will lead to more effective applications of motor imagery.

