Osteoarthritis (OA) manifests with chronic pain, motor impairment, and proprioceptive changes. However, the role of the brain in the disease is largely unknown. Here, we studied brain networks using the mathematical properties of graphs in a large sample of knee and hip OA (KOA, n = 91; HOA, n = 23) patients. We used a robust validation strategy by subdividing the KOA data into discovery and testing groups and tested the generalizability of our findings in HOA. Despite brain global topological properties being conserved in OA, we show there is a network wide pattern of reorganization that can be captured at the subject‐level by a single measure, the hub disruption index. We localized reorganization patterns and uncovered a shift in the hierarchy of network hubs in OA: primary sensory and motor regions and parahippocampal gyrus behave as hubs and insular cortex loses its central placement. At an intermediate level of network structure, frontoparietal and cingulo‐opercular modules showed preferential reorganization. We examined the association between network properties and clinical correlates: global disruption indices and isolated degree properties did not reflect clinical parameters; however, by modeling whole brain nodal degree properties, we identified a distributed set of regions that reliably predicted pain intensity in KOA and generalized to hip OA. Together, our findings reveal that while conserving global topological properties, brain network architecture reorganizes in OA, at both global and local scale. Network connectivity related to OA pain intensity is dissociated from the major hub disruptions, challenging the extent of dependence of OA pain on nociceptive signaling.

The interaction between osteoarthritis (OA) pain and brain properties remains minimally understood, although anatomical and functional neuroimaging studies suggest that OA, similar to other chronic pain conditions, may impact as well as partly be determined by brain properties. Here, we studied brain gray matter (GM) properties in OA patients scheduled to undergo total joint replacement surgery. We tested the hypothesis that brain regional GM volume is distinct between hip OA (HOA) and knee OA (KOA) patients, relative to healthy controls and moreover, that these properties are related to OA pain. Voxel-based morphometry group contrasts showed lower anterior cingulate GM volume only in HOA. When we reoriented the brains (flipped) to examine the hemisphere contralateral to OA pain, precentral GM volume was lower in KOA and HOA, and 5 additional brain regions showed distortions between groups. These GM changes, however, did not reflect clinical parameters. Next, we subdivided the brain into larger regions, approximating Brodmann areas, and performed univariable and machine learning-based multivariable contrasts. The univariable analyses approximated voxel-based morphometry results. Our multivariable model distinguished between KOA and controls, was validated in a KOA hold-out sample, and generalized to HOA. The multivariable model in KOA, but not HOA, was related to neuropathic OA pain. These results were mapped into term space (using Neurosynth), providing a meta-analytic summary of brain anatomical distortions in OA. Our results indicate more subtle cortical anatomical differences in OA than previously reported and also emphasize the interaction between OA pain, namely its neuropathic component, and OA brain anatomy.

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A significant proportion of osteoarthritis (OA) patients continue to experience moderate to severe pain after total joint replacement (TJR). Preoperative factors related to pain persistence are mainly studied using individual predictor variables and distinct pain outcomes, thus leading to a lack of consensus regarding the influence of preoperative parameters on post-TJR pain. In this prospective observational study, we evaluated knee and hip OA patients before, 3 and 6 months post-TJR searching for clinical predictors of pain persistence. We assessed multiple measures of quality, mood, affect, health and quality of life, together with radiographic evaluation and performance-based tasks, modeling four distinct pain outcomes. Multivariate regression models and network analysis were applied to pain related biopsychosocial measures and their changes with surgery. A total of 106 patients completed the study. Pre-surgical pain levels were not related to post-surgical residual pain. Although distinct pain scales were associated with different aspects of post-surgical pain, multi-factorial models did not reliably predict post-surgical pain in knee OA (across four distinct pain scales) and did not generalize to hip OA. However, network analysis showed significant changes in biopsychosocial-defined OA personality post-surgery, in both groups. Our results show that although tested clinical and biopsychosocial variables reorganize after TJR in OA, their presurgical values are not predictive of post-surgery pain. Derivation of prognostic markers for pain persistence after TJR will require more comprehensive understanding of underlying mechanisms.