Network properties and regional brain morphology of the insular cortex correlate with individual pain thresholds

Abstract

Pain thresholds vary considerably across individuals and are influenced by a number of behavioral, genetic and neurobiological factors. However, the neurobiological underpinnings that account for individual differences remain to be fully elucidated. In this study, we used voxel-based morphometry (VBM) and graph theory, specifically the local clustering coefficient (CC) based on resting-state connectivity, to identify brain regions, where regional gray matter volume and network properties predicted individual pain thresholds. As a main finding, we identified a cluster in the left posterior insular cortex (IC) reaching into the left parietal operculum, including the secondary somatosensory cortex, where both regional gray matter volume and the local CC correlated with individual pain thresholds. We also performed a resting-state functional connectivity analysis using the left posterior IC as seed region, demonstrating that connectivity to the pre-as well as postcentral gyrus bilaterally; that is, to the motor and primary sensory cortices were correlated with individual pain thresholds.To our knowledge, this is the first study that applied VBM in combination with voxelbased graph theory in the context of pain thresholds. The co-location of the VBM and the local CC cluster provide first evidence that both structure and function map to the same brain region while being correlated with the same behavioral measure; that is, pain thresholds. The study highlights the importance of the posterior IC, not only for pain perception in general, but also for the determination of individual pain thresholds.

Gray matter cortical thickness predicts individual pain sensitivity: a multi-center machine learning approach

“…Moreover, in their aforementioned study, Grant et al reported a negative association between cortical thickness and pain sensitivity in long-term mediation practitioners not only in the rACC, but interestingly, also in the PHG (Grant et al, 2010). Neumann et al recently also reported increased grey matter volume in PHG with lower pain sensitivity (Neumann et al, 2021) in center 1 of the present multi-center analysis.…”

“…Although we still lack a rigorously validated structure-based predictive model for pain sensitivity, several studies have reported structural brain correlates of the sensitivity to pain on the group level. Voxel-based morphometry (VBM) studies have reported that pain sensitivity is significantly correlated to structures including the cingulate cortex (CC), precuneus, primary somatosensory cortex (S1), putamen, insula, parahippocampal gyrus (PHG), among others (Emerson et al, 2014; Neumann et al, 2021; Niddam et al, 2021; Ruscheweyh et al, 2018; Zhang et al, 2020). Surface based morphometry techniques (Fischl, 2012) can provide additional insights and, in contrast to VBM, a better account for gyrification via measures such as parcellated brain cortical thicknesses and volumes (Fischl et al, 2001).…”

Resting-state functional heterogeneity of the right insula contributes to pain sensitivity

“…The anterior insula connects to prefrontal and anterior cingulate cortices, which are implicated in cognitive pain modulation 15 , 23 , and these connections were shown to be important features in predicting individual pain sensitivity 17 . Additionally, in our recent study we found that morphological and functional network properties of the posterior insula are also associated with pain sensitivity 24 . Based on this, the anterior-dominant connectopy might represent a more effective connectivity layout in terms of top-down pain modulation.…”

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

“…Studies have reported the differences in nodal degree and PC of functional brain networks between patients with CBP and the HCs (72,73). The CC of the insular cortex was found to be correlated with individual pain thresholds (74). Therefore, the combination of the TGI of these three network properties can comprehensively depict the alternation of brain networks of patients with CBP, which undoubtedly performed better than the TGI of a single network property in predicting the pain intensity.…”