Altered white matter integrity and structural network topology in rhegmatogenous retinal detachment: A diffusion tensor imaging study.

Background: Rhegmatogenous retinal detachment (RRD) has been associated with gray matter alterations, but its effects on white matter microstructure and brain network organization remain largely unexplored.

Methods: This study included 40 RRD patients and 36 healthy controls (HCs), who underwent diffusion tensor imaging (DTI). Tract-Based Spatial Statistics (TBSS) was used to assess white matter microstructure, and graph theory was applied to quantify structural network topology. In addition, a support vector machine (SVM) classifier was trained to evaluate the discriminative potential of imaging-derived features.

Results: Compared to HCs, RRD patients exhibited disrupted white matter network topology, characterized by reduced small-world properties and increased global efficiency. Regionally, widespread alterations in nodal centrality and efficiency were observed, primarily in the frontal, temporal, and occipital lobes. Structural connectivity analysis revealed enhanced integration between attention-related networks and diminished within-network coherence in the default mode and dorsal attention systems. TBSS further identified microstructural abnormalities in the corpus callosum and corona radiata. Notably, degree centrality (DC) achieved the highest classification accuracy in SVM, with an area under the curve (AUC) of 0.9125.

Conclusion: RRD patients exhibit widespread alterations in white matter microstructure and structural network topology, indicating central nervous system involvement following acute peripheral visual loss. Among network metrics, DC showed the highest discriminative power. These findings offer preliminary insights into the neural mechanisms of RRD and may inform future studies on disease stratification or prognosis.