ArticleQuantitative imaging in medicine and surgery2025
Cortical functional and structural alterations in rhegmatogenous retinal detachment: a multimodal analysis using surface-based independent component analysis, morphometry, and machine learning.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Although previous research has identified functional abnormalities in visual pathways and gray matter areas linked to visual processing in patients with rhegmatogenous retinal detachment (RRD), the precise nature and extent of cortical functional and structural disruptions in these individuals remain insufficiently explored. This study aimed to characterize cortical changes in RRD and investigate their utility as neuroimaging markers. Methods: This study used surface-based independent component analysis (ICA) and morphological measurement techniques to investigate cortical functional and structural changes in RRD patients. Subsequently, it applied support vector machine (SVM) and SHapley Additive exPlanations (SHAP) methods to classify RRD patients and healthy controls (HCs), identifying key brain regions that contribute most to the prediction model. Results: In terms of cortical function, the RRD patients showed significant increases in functional connectivity (FC) in the visual, cognitive, auditory, and motor networks (voxel P<0.001, cluster P<0.05), as well as a significant enhancement in functional network connectivity (FNC) between the visual network (VN) and sensorimotor network (SMN) (P<0.01) compared to the HCs. The RRD patients exhibited significant cortical thinning in the left hemisphere lateral occipital cortex (lh_lateraloccipital), right hemisphere pericalcarine cortex (rh_pericalcarine), and right hemisphere lateral orbitofrontal cortex (rh_lateralorbitofrontal) regions (voxel P<0.01, cluster P<0.05) in the cortical structure. Notably, the FC value in the VN1 region showed the best classification performance in both the SVM and SHAP analyses. Conclusions: This study found pronounced functional and structural abnormalities in the cortex of RRD patients, potentially linked to cognitive and visual dysfunction. Notably, FC alterations in the VN1 region may act as a robust neuroimaging indicator capable of distinguishing individuals with RRD from HCs. Our results offer novel insights into the underlying neuropathological processes of RRD, and underscore the value of early neuroimaging markers for timely diagnosis and clinical intervention.
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