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Gim, S.

Publications and source records attributed to Gim, S..

2 recordsLinked to original sources

Temporal Dynamics of Brain Mediation in Predictive Cue-induced Pain Modulation

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of prior predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. Here, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from transmodal to unimodal brain systems. Our findings advance the understanding of how the brain integrates prior and sensory information into pain experience over time.

neuroscience↗

Interindividual differences in pain can be explained by fMRI, sociodemographic, and psychological factors

In a recent article, Hoeppli et al. (2022) reported that sociodemographic and psychological factors were not associated with interindividual differences in reported pain intensity. In addition, the interindividual differences in pain could not be detected by thermal pain-evoked brain activities measured by functional Magnetic Resonance Imaging (fMRI). Their comprehensive analyses provided convincing evidence for these null findings, but here we provide another look at their conclusions by analyzing their behavioral data and a large-scale fMRI dataset involving thermal pain (N = 124). Our main findings are as follows: First, a multiple regression model incorporating all available sociodemographic and psychological measures could significantly predict the interindividual differences in reported pain intensity. The key to achieving a significant prediction was including multiple individual difference measures in a single model. Second, with fMRI data from a relatively homogeneous group of 124 participants, we could identify brain regions and a multivariate pattern-based predictive model significantly correlated with the interindividual differences in reported pain intensity. Our results, along with the findings of Hoeppli et al., highlight the challenge of predicting interindividual differences in pain, but also suggest that it is not an impossible task.

neuroscience↗