CNS neuroscience & therapeutics · 2026
Multiscale Cortical Remodeling Following Abrupt Visual Deafferentation in Rhegmatogenous Retinal Detachment: Imaging Transcriptomics and Neurotransmitter Mapping.
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Ji Yu, Huang Xin, Wang Yuan-Yuan, Zhou Lin, Dong Zhuo-Er, Wu Xiao-Rong
Аннотация
BACKGROUND: Neuroimaging evidence indicates brain alterations in rhegmatogenous retinal detachment (RRD), but the molecular and neurochemical correlates of these macroscale patterns remain unclear.
METHODS: We examined gray matter volume (GMV), intrinsic neural timescale (INT) and structural covariance network (SCN) gradients in 51 patients with RRD and 45 healthy controls (HCs). Two-sample Mendelian randomization (MR) evaluated whether RRD-related genetic liability was consistent with a putative causal effect on GMV. SCN-gradient alterations were further evaluated using exploratory spatial association analyses based on the Allen Human Brain Atlas (AHBA) and complementary neurotransmitter maps. SHAP-explainable machine-learning classification models compared the discriminative utility of structural versus functional features.
RESULTS: Patients with RRD showed reduced GMV in the visual network (VN) and shortened INT in the default mode network (DMN). MR results were consistent with a putative causal effect of RRD genetic liability on VN atrophy. SCN gradients revealed a hierarchical "visual-downward and limbic-upward" shift. Exploratory imaging-transcriptomic spatial association analyses suggested that gradient alterations were spatially aligned with gene expression patterns enriched for neurodevelopmental and synaptic pathways, including excitatory/inhibitory neuronal and microglial signatures; complementary neurotransmitter mapping analyses further suggested spatial correspondence with normative monoaminergic/cholinergic and μ-opioid maps, together with an inverse spatial association with GABAa receptor density maps. INT-based ML models outperformed GMV-based models (best AUC = 0.753), with SHAP identifying INT as the predominant contributor.
CONCLUSION: RRD is associated with coordinated structural and functional alterations across cortical hierarchies. Exploratory transcriptomic and neurotransmitter spatial association patterns may provide biological context for these imaging abnormalities and inform future studies of prognosis and underlying mechanisms.
METHODS: We examined gray matter volume (GMV), intrinsic neural timescale (INT) and structural covariance network (SCN) gradients in 51 patients with RRD and 45 healthy controls (HCs). Two-sample Mendelian randomization (MR) evaluated whether RRD-related genetic liability was consistent with a putative causal effect on GMV. SCN-gradient alterations were further evaluated using exploratory spatial association analyses based on the Allen Human Brain Atlas (AHBA) and complementary neurotransmitter maps. SHAP-explainable machine-learning classification models compared the discriminative utility of structural versus functional features.
RESULTS: Patients with RRD showed reduced GMV in the visual network (VN) and shortened INT in the default mode network (DMN). MR results were consistent with a putative causal effect of RRD genetic liability on VN atrophy. SCN gradients revealed a hierarchical "visual-downward and limbic-upward" shift. Exploratory imaging-transcriptomic spatial association analyses suggested that gradient alterations were spatially aligned with gene expression patterns enriched for neurodevelopmental and synaptic pathways, including excitatory/inhibitory neuronal and microglial signatures; complementary neurotransmitter mapping analyses further suggested spatial correspondence with normative monoaminergic/cholinergic and μ-opioid maps, together with an inverse spatial association with GABAa receptor density maps. INT-based ML models outperformed GMV-based models (best AUC = 0.753), with SHAP identifying INT as the predominant contributor.
CONCLUSION: RRD is associated with coordinated structural and functional alterations across cortical hierarchies. Exploratory transcriptomic and neurotransmitter spatial association patterns may provide biological context for these imaging abnormalities and inform future studies of prognosis and underlying mechanisms.