Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1206-1222 |
| Number of pages | 17 |
| Journal | Human Brain Mapping |
| Volume | 42 |
| Issue number | 4 |
| DOIs | |
| State | Published - Mar 2021 |
Funding
The authors thank to Dr Paulo Oliveira, for the clinical support and supervision of patients enrolled in the study and all the members of the Orthopedic Department of Centro Hospitalar Universitário de São João, Porto, for their support with data collection. The authors thank to Unilabs Boavista and Dr Nuno Martins for all the support in imaging data acquisition. The authors are thankful to all Apkarian lab and Galhardo lab members that contributed to this study with their time and resources. J. B. was funded through CCDRN (Norte‐08‐5369‐FSE‐000026), OARSI Collaborative Scholarship 2018, and Luso‐American Development Foundation R&D@PhD scholarship grant. Neuroimage data acquisition was provided by Unilabs Boavista and the Grünenthal Young Pain Researcher 2017 Grant. CCDRN, Grant/Award Number: Norte‐08‐5369‐FSE‐000026; Unilabs Boavista and the Grünenthal Young Pain Researcher 2017 Grant; Luso‐American Development Foundation R&D@PhD Scholarship Grant; OARSI Collaborative Scholarship 2018 Funding information The authors thank to Dr Paulo Oliveira, for the clinical support and supervision of patients enrolled in the study and all the members of the Orthopedic Department of Centro Hospitalar Universit?rio de S?o Jo?o, Porto, for their support with data collection. The authors thank to Unilabs Boavista and Dr Nuno Martins for all the support in imaging data acquisition. The authors are thankful to all Apkarian lab and Galhardo lab members that contributed to this study with their time and resources. J. B. was funded through CCDRN (Norte-08-5369-FSE-000026), OARSI Collaborative Scholarship 2018, and Luso-American Development Foundation R&D@PhD scholarship grant. Neuroimage data acquisition was provided by Unilabs Boavista and the Gr?nenthal Young Pain Researcher 2017 Grant.
Keywords
- brain networks
- brain topology
- chronic pain
- graph properties
- osteoarthritis
ASJC Scopus subject areas
- Anatomy
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Neurology
- Clinical Neurology