Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood

The nucleus accumbens-prefrontal connectivity as a predictor of chronic low back pain

ABSTRACT

The nucleus accumbens (NAc) and its prefrontal connections are implicated in the aetiology of chronic low back pain (CLBP). Animal and human studies suggest that the NAc and its connections play a critical role in the transition from acute to CLBP. However, whole-brain connectivity in individuals with longstanding CLBP has not been systematically investigated. Using a functional connectomics approach, we examined whether the 2 NAc subregions-shell and core-exhibit different whole-brain connectivity between CLBP patients and healthy controls (HCs; total N = 197). The identified connections were correlated with CLBP intensity (multiple comparisons corrected), and their reproducibility was validated in 2 independent cohorts. These clinically relevant and reproducible connections were further leveraged to classify CLBP using machine learning. Compared with HC (n = 41), individuals with CLBP (n = 39) exhibited hyperconnectivity between the NAc shell and core and the prefrontal cortex (PFC). Although several NAc-PFC connections were linked to higher CLBP intensity, only the connections between the left NAc shell and core to the right dorsolateral PFC were reproduced in validation cohorts (total CLBP n = 53; HC n = 64). Nucleus accumbens-right dorsolateral PFC connections achieved 84% classification accuracy using logistic regression. The machine learning analyses demonstrate how knowledge-based feature selection can reliably detect CLBP. Overall, we report that NAc-PFC connectivity consistently distinguishes people with CLBP from HC and suggest an abnormal interaction between the NAc and brain regions involved in motivation, decision-making, and pain regulation.

Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks

Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.