ArticlebioRxiv : the preprint server for biology2026
Impact of Aii Amacrine Cell Rewiring in a Pathoconnectome-Based Computational Model of Early Retinal Degeneration.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Retinitis pigmentosa (RP), a retinal degenerative disease, is characterized by progressive photoreceptor loss and ongoing remodeling and rewiring of the inner retina. This study investigates rod network rewiring through pathoconnectomic evaluation and its impacts on signaling patterns. The glycinergic Aii amacrine cell (Aii) plays a central role in the healthy retina bridging rod and cone pathways, enabling an increased dynamic range of vision. Pathoconnectomics reveals altered connectivity in both the excitatory drive and gap junctional coupling of Aiis in retinal degeneration. A computational model of the rewired network was developed to assess the functional consequences of these structural changes by simulating light-evoked responses and changes in excitatory postsynaptic potentials (EPSPs). The model predicts significant changes in bipolar and Aii EPSPs between active and baseline conditions, driven by newly formed gap junctions in the degenerate retina. Notably, the aberrant circuitry induces rhythmic firing of up to 10 Hz in retinal ganglion cells, consistent with network depolarization relative to the healthy baseline state. These findings align with patch-clamp observations in rd1 and rd10 mouse models of RP, suggesting that Aii-mediated network alterations may underlie early clinical symptoms, including impaired adaptation between photopic and scotopic vision. More broadly, this work demonstrates that integrating computational modeling with pathoconnectomics enables predictive analysis of signaling in early-stage retinal degeneration and may help identify windows for therapeutic intervention. Such models could be further extended with multi-scale bioelectromagnetic simulations to optimize neurostimulation strategies aimed at slowing disease progression.
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