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Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly

GSE312396 Mus musculus Expression profiling by high throughput sequencing 7 samples Submitted 2025/12/08 Platform GPL34328
Summary
The mouse cortex is a canonical model for studying how functional neural networks emerge, yet it remains unclear which topological features arise from intrinsic cellular organization versus sensory input. Mouse forebrain organoids provide a powerful system to investigate these intrinsic mechanisms. We generated dorsal (DF) and ventral (VF) forebrain organoids from mouse pluripotent stem cells and tracked their development using longitudinal electrophysiology. DF organoids showed progressively stronger network-wide correlations, while VF organoids developed more refined activity patterns with enhanced small-world topology and increased modular organization. Both organoid types form small-world networks, but their topological organization differs. These differences emerged without extrinsic inputs and correlate with Pvalb+ interneuron enrichment in VF organoids. Our findings demonstrate how cellular composition influences neural circuit self-organization, establishing mouse forebrain organoids as a tractable platform to study cortical network architecture.
Published in
Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly
Hernandez S, Schweiger HE, Cline I et al. · bioRxiv : the preprint server for biology 2025 · PMID 40654898 · doi:10.1101/2025.05.01.651773
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Also filed as BioProject PRJNA1373237 and SRA study SRP651086. Searching any of these in the dataset finder brings you back here.

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