A GRAPH THEORY FRAMEWORK FOR MAPPING CORTICAL CIRCUIT ASSEMBLY AT SINGLE NEURON RESOLUTION IN DEVELOPMENT AND DISEASE

The emergence of functional neural networks in the mammalian cortex arises from the interplay between intrinsic cellular programs and extrinsic regional cues and sensory information. Brain organoids provide a platform to disentangle these factors, isolating cell-autonomous processes from external sensory cues. We developed a longitudinal framework integrating high-density multi-electrode array electrophysiology with graph-theoretical analysis. These measures include modularity and small-world properties to quantify how organoid-derived neural network topology and dynamics evolve over time at single-neuron resolution from local connections to the emergence of long-range connections. To probe the impact of inhibitory neuron enrichment on these dynamics, we applied this framework to mouse dorsalized (DF) and ventralized forebrain (VF) organoids, with VF organoids particularly enriched for parvalbumin interneurons.. We profiled over 30,000 putative neurons across mouse DF and VF organoids during 30 days of development. Our analyses revealed that regional cellular composition alone drives distinct topological signatures during maturation, reflected by higher modularity indices and clustering coefficients. These properties indicate that inhibitory neuron enriched networks tend to be more modular, with locally segregated dynamics that favor decorrelation between modules, whereas predominantly excitatory networks exhibit greater global synchronization. Finally, we demonstrated the translational utility of this framework by modeling SHANK3 loss-of-function in human cortical organoids. Our analysis reveals how disrupted synaptic scaffolding shifts the developmental trajectory of functional connectivity, providing a quantitative bridge between molecular deficits and circuit-level dysfunction in Autism Spectrum Disorder (ASD).