Clinical ophthalmology (Auckland, N.Z.) · 2026
Mapping Multiscale Brain Changes in Primary Angle-Closure Glaucoma Using Regional Radiomics Similarity Networks.
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Hu Die, Hu Rui-Yang, Zhang Yan-Yan, Cheng Yu-Yue, Yu Fang-Liang, Huang Xin
Аннотация
PURPOSE: To investigate large-scale structural brain network reorganization in primary angle-closure glaucoma (PACG) using regional radiomics similarity networks (R2SNs) and to characterize their associated molecular and neurobiological substrates.
DESIGN: Case-control study.
METHODS: Structural magnetic resonance imaging data were acquired from 44 patients with PACG and 44 age- and sex-matched healthy controls. Individualized R2SNs were constructed to identify PACG-related structural network alterations. Partial least squares regression was used to link R2SN alterations with brain-wide transcriptomic profiles, followed by enrichment, cell-type, neurochemical, and epicenter analyses. Supervised machine learning was used to evaluate the discriminative value of R2SN-derived features.
RESULTS: Compared with controls, patients with PACG demonstrated widespread R2SN alterations extending beyond the visual pathway, involving frontal, temporal, limbic, and subcortical regions. Network-level analyses revealed disrupted structural covariance across intrinsic functional systems, particularly within limbic, default mode, attentional, and frontoparietal control networks. Imaging-transcriptomic analyses showed spatial coupling between PACG-related network alterations and gene expression profiles enriched in synaptic, cytoskeletal, and immune-related processes. Epicenter analysis identified highly connected hub regions within default mode and attentional systems. Machine learning classifiers based on R2SN features achieved robust discrimination between groups.
CONCLUSION: PACG is associated with widespread structural brain network reorganization beyond the visual system.R2SN-derived network features may provide sensitive imaging markers of central structural reorganization and offer a multiscale framework for understanding the neural mechanisms of PACG.
DESIGN: Case-control study.
METHODS: Structural magnetic resonance imaging data were acquired from 44 patients with PACG and 44 age- and sex-matched healthy controls. Individualized R2SNs were constructed to identify PACG-related structural network alterations. Partial least squares regression was used to link R2SN alterations with brain-wide transcriptomic profiles, followed by enrichment, cell-type, neurochemical, and epicenter analyses. Supervised machine learning was used to evaluate the discriminative value of R2SN-derived features.
RESULTS: Compared with controls, patients with PACG demonstrated widespread R2SN alterations extending beyond the visual pathway, involving frontal, temporal, limbic, and subcortical regions. Network-level analyses revealed disrupted structural covariance across intrinsic functional systems, particularly within limbic, default mode, attentional, and frontoparietal control networks. Imaging-transcriptomic analyses showed spatial coupling between PACG-related network alterations and gene expression profiles enriched in synaptic, cytoskeletal, and immune-related processes. Epicenter analysis identified highly connected hub regions within default mode and attentional systems. Machine learning classifiers based on R2SN features achieved robust discrimination between groups.
CONCLUSION: PACG is associated with widespread structural brain network reorganization beyond the visual system.R2SN-derived network features may provide sensitive imaging markers of central structural reorganization and offer a multiscale framework for understanding the neural mechanisms of PACG.