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Updated: May 14, 2026

In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
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Multiscale Cortical Remodeling Following Abrupt Visual Deafferentation in Rhegmatogenous Retinal Detachment: Imaging

Yu Ji1, Xin Huang2,3,4, Yuan-Yuan Wang5

  • 1Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

CNS Neuroscience & Therapeutics
|May 13, 2026
PubMed
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Rhegmatogenous retinal detachment (RRD) is linked to brain changes, including altered gray matter volume and neural timescales. These findings suggest coordinated structural and functional brain alterations in RRD patients.

Area of Science:

  • Neuroscience
  • Ophthalmology
  • Genetics

Background:

  • Neuroimaging reveals brain alterations in rhegmatogenous retinal detachment (RRD).
  • The molecular and neurochemical underpinnings of these macroscale brain changes in RRD remain unclear.
  • Understanding these correlates is crucial for elucidating RRD's impact on the brain.

Purpose of the Study:

  • To investigate gray matter volume (GMV), intrinsic neural timescale (INT), and structural covariance network (SCN) gradients in RRD patients.
  • To evaluate the potential causal effect of RRD genetic liability on brain structure using Mendelian randomization (MR).
  • To explore the relationship between SCN gradient alterations and gene expression patterns and neurotransmitter maps.

Main Methods:

  • Compared GMV, INT, and SCN gradients in 51 RRD patients and 45 healthy controls (HCs).
Keywords:
Mendelian randomizationimaging transcriptomicsintrinsic neural timescaleneurotransmitter density mapsrhegmatogenous retinal detachmentstructural covariance network gradient

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  • Utilized two-sample MR to assess RRD genetic liability's causal effect on GMV.
  • Performed exploratory spatial association analyses with the Allen Human Brain Atlas (AHBA) and neurotransmitter maps.
  • Employed SHAP-explainable machine-learning models to compare structural and functional feature discrimination.
  • Main Results:

    • RRD patients exhibited reduced GMV in the visual network (VN) and shortened INT in the default mode network (DMN).
    • MR results supported a putative causal effect of RRD genetic liability on VN atrophy.
    • SCN gradients showed a hierarchical shift, and alterations spatially correlated with neurodevelopmental/synaptic gene expression and neurotransmitter maps.
    • INT-based machine learning models outperformed GMV-based models for classification (AUC=0.753).

    Conclusions:

    • Rhegmatogenous retinal detachment is associated with coordinated structural and functional alterations across cortical hierarchies.
    • Exploratory transcriptomic and neurotransmitter analyses offer biological context for observed brain abnormalities.
    • These findings may guide future research on RRD prognosis and underlying mechanisms.