Abnormal functional lateralization and cooperation in primary angle-closure glaucoma correlate with cell type-specific transcriptional signatures.
Abstract
Background
Primary angle-closure glaucoma (PACG) damages retinal ganglion cells (RGCs) and is associated with neurodegeneration. This study used resting-state functional magnetic resonance imaging (fMRI) to analyze hemispheric lateralization and cooperative functional alterations in PACG. Machine learning assessed the classification efficacy of neuroimaging indicators, while integrated transcriptomics described spatial relationships between neuroimaging changes and genes, neurotransmitters, and cell types.
Methods
Resting-state fMRI data were collected from 101 subjects (44 patients with PACG and 57 controls). Whole-brain maps of the Autonomy Index and connectivity of functional homotopic voxels (CFH) were constructed. Their classification efficacy was assessed using five machine learning classifiers. Partial Least Squares (PLS) spatial correlation analysis examined relationships between neuroimaging maps and gene-transcript profiles, cell type density, and neurotransmitter-receptor distribution maps.
Results
PACG showed increased Autonomy Index in cerebellar Crus II and the right paracentral lobule, with widespread reductions in interhemispheric cooperation. Five machine learning models yielded classification results. Image-transcriptome analysis showed that positive Autonomy Index and CFH-positive gene features were enriched in synaptic signaling and neurodevelopment, whereas negative Autonomy Index and CFH-negative gene profiles correlated with neurodegeneration, metabolic dysfunction, and vascular-immune responses. Oligodendrocytes and endothelial cells showed spatial associations with the Autonomy Index and CFH. Brain lateralization and interhemispheric cooperation showed spatial correlations with VAChT topology and several neurotransmitter receptor/transporter maps.
Conclusion
PACG is characterized by enhanced cerebral lateralization and diminished interhemispheric cooperation. Explainable machine learning evaluated the classification efficacy of these imaging features. These findings provide a multimodal spatial-association framework relating PACG-related imaging alterations to normative molecular and cellular brain maps.