EEG Connectomics in Rotator Cuff Injury: Central Network Reorganization During Three-Plane Antagonistic Shoulder...

We characterised central nervous system reorganisation in rotator cuff injury (RCI) during three-plane antagonistic shoulder tasks using electroencephalography (EEG) connectomics, and tested whether graph metrics discriminate RCI from healthy controls (HC). We enrolled 51 RCI patients and 50 HCs performing three-plane tasks. EEG (64 channels) was preprocessed and source-localised; source-space connectivity was computed in five bands ( $\theta $ , $\alpha $ , $\beta $ , low- $\gamma $ , high- $\gamma $ ) using the weighted phase-lag index (WPLI). Whole-brain graph metrics (global efficiency (GE), characteristic path length (CPL), clustering coefficient (CC), modularity (Mod) and small-worldness (SW)) were derived, and network-based statistics (NBS) identified differential subnetworks from which mean subnetwork strength (WPLINBS) and related measures were extracted. Per action-band, single-feature linear support vector machines (SVMs) were evaluated with nested cross-validation for RCI-HC classification. Group differences were frequency-specific: $\beta $ (13-30 Hz) and low- $\gamma $ (30-50 Hz) showed RCI "over-integration/under-segregation" (higher GE and CC), whereas CPL, Mod and SW were higher in HC; $\theta $ / $\alpha $ effects were weak, whereas high- $\gamma $ effects were less robust and less consistent across shoulder tasks than the $\beta $ and low- $\gamma $ effects. NBS detected significant components only in $\beta $ /low- $\gamma $ , characterised by stronger coupling between occipital visual cortex and frontoparietal/cerebellar regions; within these components, WPLINBS, nodal strength and local efficiency were higher in RCI. CPL, GE or WPLINBS in $\beta $ /low- $\gamma $ provided robust classification (ACC $\approx 0.86$ ; AUC $\approx 0.90$ ). Overall, RCI is associated with $\beta $ /low- $\gamma $ -centred network reorganisation and enhanced occipito-parietal-frontal coupling, suggesting greater engagement of visual-related visuomotor networks during shoulder task execution. These EEG graph metrics may serve as candidate CNS biomarkers and potential targets for personalised rehabilitation.