Reorganization of functional brain network architecture in chronic osteoarthritis pain

FIGURE 1. Methodological overview of the computation pipeline for global and nodal graph properties, hub disruption indices and modular reorganization analysis. (a) For each subject included in the study, brain was parcellated in 256 regions of interest (ROIs) from a 264 parcellation Scheme (eight ROIs corresponding to the cerebellum were excluded). For each ROI, blood oxygenation level‐dependent signal (BOLD) was extracted as an average over voxels within 10‐mm diameter spheres with center at defined peak coordinate. Next, a 256 × 256 Pearson's full correlation matrix was computed between all pairs of ROIs time‐series; nine adjacency matrices were then calculated at different link densities (2–10%). (b) Graph properties (degree, clustering coefficient, efficiency, and betweenness centrality) were estimated using the Brain Connectivity Toolbox: First, we calculated nodal (local level) properties; latter, by averaging each property across the 256 ROIs we computed the corresponding global measurement. (c) Hub disruption indices were calculated for each subject as the gradient of a straight line fitted to a scatterplot of the nodal property of interest, for example, degree, minus the same…

FIGURE 2. Disruption of functional network hub organization in osteoarthritis (OA) patients. (a) Group hub disruption index calculation for degree (K D) at 5% link density: for each node (256 regions), the mean degree in the control group (x‐axis) is plotted against the mean nodal difference between groups (KOA—control) (y‐axis). Red dots represent nodes that are non‐hubs in controls but show an abnormal increase in degree in KOA patients: precentral and postcentral gyrus; filled blue dots represent nodes that are typically hubs in healthy controls and show a reduction in degree for KOA: insula; paracingulate gyrus; opercular cortex. The hub disruption index corresponds to the slope of the line fitted to the data (red line), K D = −0.18, p < .001. Insert shows individual K D values. On the right, brain graphical representation of the difference in mean degree between KOA and controls, top 10% most different regions of interest (ROIs) are depicted, red denotes abnormally increased degree and blue abnormally decreased degree in KOA compared to healthy controls. (c) Boxplots of the subject‐wise estimated hub disruption indices for the control group (blue) and KOA (red) at 5% link density.…

FIGURE 3. Hub topology is altered in knee OA (KOA): differences in hub status between osteoarthritis (OA) patients and healthy controls. (a) Mean nodal difference between groups (KOA—control), organized by score and thresholded at ±2 SD (gray lines) from the mean difference (red dots) and graphic representation of selected nodes. (b) Validation of nodal disruption in KOA hold out sample: 6 out of 11 regions were validated: sensory‐motor regions and parahippocampal gyrus present a significant degree gain and insula/operculum, normal hub nodes, show an abnormal reduction of degree in KOA patients. (c) Hip OA group, validates uniquely the increase in degree for S1 and parahippocampal gyrus. FP, frontoparietal cortex; DMN, default mode network; ROI, region of interest; S1, primary somatosensory cortex; SM, sensory‐motor cortex. *p < .05, permutational test

FIGURE 4. Multinodal distributed degree properties predict pain intensity numeric pain rating (NRS) in osteoarthritis (OA) patients. (a) Graphical representation of the linear regression model using Elastic net regularization and variable selection with penalty weight (α) of .5 and regularization parameter (λ) choice via a 10‐fold cross validation. Brain nodes depicted correspond to regions predicting pain intensity; node size reflects the weight (B‐coefficients) in the regression model. This is also illustrated in (b) the majority of nodes has regression coefficients set to zero, indicating that the corresponding variables are not contributing to the model. Nonzero regression coefficients identify the predictive features and indicate the weight and direction of degree change in relation to the response variable: that is, higher levels of pain (NRS) relate with lower degree in parahipoccampal gyrus, putamen, and superior temporal gyrus and higher degree in paracingulate cortex and inferior temporal gyrus. (c) Features selected in the elastic net regression predicted the magnitude of response and (d) validate in the hold out knee OA (KOA) and (e) hip OA samples: high correlation value…

FIGURE 5. Modular reorganization in osteoarthritis (OA) involves predominantly nodes assigned the frontoparietal and cingulo‐opercular task control networks. (a) Agreement difference matrix obtained from the difference between the modular agreement matrix from OA and control groups. Positive (red) values reflect pairs of nodes that are estimated to appear more commonly in the same module in OA patients, and negative (blue) values represent pairs of nodes that are estimated to appear less commonly in the same module in OA. White value represents regions of interest (ROIs) that has the same behavior in both groups. (b) Blue line shows overall modular reorganization for each node as the sum of both positive and negative values for each node (sum of absolute value per row in (a)), meaning the largest value, the greater reorganization. To statistically evaluate these values, we performed a permutation test of sum reorganization estimation (null model, gray color), yielding one‐side p‐values across all ROIs. (c) Representation of statistically significant nodes showing modular reorganization at a threshold of p < .01; size reflects magnitude of the absolute agreement difference