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<p>Primary angle-closure glaucoma (PACG), an irreversible blinding disease characterized by retinal ganglion cell damage and optic nerve atrophy, exerts significant effects on brain functional networks. Using resting-state functional magnetic resonance imaging (rs-fMRI) data from 34 PACG patients and 34 matched healthy controls (HCs), we extracted four types of connectivity features—voxel-wise static functional connectivity (FC), dynamic functional connectivity (dFC), effective connectivity (EC), and dynamic effective connectivity (dEC)—via the AAL90 (Automated Anatomical Labeling 90) atlas following preprocessing. Elastic net feature selection was applied independently to each connectivity type to retain the top 10% most discriminative features. We evaluated the classification performance of ten machine learning models using individual feature types as well as their combined features, with the FC-based logistic regression (LR) model achieving optimal diagnostic efficacy (accuracy = 0.92, AUC = 0.96). SHapley Additive exPlanations (SHAP) of the model identified 20 critical connections, revealing abnormal patterns at both the region of interest (ROI)-level and network-level within brain networks such as the visual network (VSN), dorsal attention network (DAN), and sensorimotor network (SMN). Statistical group comparisons validated reduced connectivity (e.g., VSN-SMN, VSN-DAN) and enhanced DAN-thalamus connectivity in patients, while voxel-wise analyses of key regions confirmed diminished connectivity to visual areas. The results provide insights into how machine learning can be effectively employed to detect PACG-specific brain network disruptions and highlight potential neuroimaging biomarkers.</p>

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Machine learning with multitype functional connectivity uncovers whole-brain network disruption in primary angle-closure glaucoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Guangxiang Chen,
  • Dekai Hu,
  • Xin Huang,
  • Zhijiang Wan

摘要

Primary angle-closure glaucoma (PACG), an irreversible blinding disease characterized by retinal ganglion cell damage and optic nerve atrophy, exerts significant effects on brain functional networks. Using resting-state functional magnetic resonance imaging (rs-fMRI) data from 34 PACG patients and 34 matched healthy controls (HCs), we extracted four types of connectivity features—voxel-wise static functional connectivity (FC), dynamic functional connectivity (dFC), effective connectivity (EC), and dynamic effective connectivity (dEC)—via the AAL90 (Automated Anatomical Labeling 90) atlas following preprocessing. Elastic net feature selection was applied independently to each connectivity type to retain the top 10% most discriminative features. We evaluated the classification performance of ten machine learning models using individual feature types as well as their combined features, with the FC-based logistic regression (LR) model achieving optimal diagnostic efficacy (accuracy = 0.92, AUC = 0.96). SHapley Additive exPlanations (SHAP) of the model identified 20 critical connections, revealing abnormal patterns at both the region of interest (ROI)-level and network-level within brain networks such as the visual network (VSN), dorsal attention network (DAN), and sensorimotor network (SMN). Statistical group comparisons validated reduced connectivity (e.g., VSN-SMN, VSN-DAN) and enhanced DAN-thalamus connectivity in patients, while voxel-wise analyses of key regions confirmed diminished connectivity to visual areas. The results provide insights into how machine learning can be effectively employed to detect PACG-specific brain network disruptions and highlight potential neuroimaging biomarkers.