AMYOTROPHIC LATERAL SCLEROSIS ALTERS CORTICAL NETWORK DEVELOPMENT THROUGH A TRANSITION FROM HYPEREXCITABLE STATES TO PROGRESSIVE NETWORK DECLINE

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder marked by degeneration of motor neurons in the spinal cord, brainstem, and cortex, resulting in weakness, muscle atrophy, and spasticity. The precise mechanisms underlying disease onset and progression remain unclear, highlighting the need for predictive, multiscale models capable of integrating complex network-level dynamics. Computational approaches combined with in vitro systems offer a powerful framework to dissect cellular and network dysfunctions in ALS. We reconstructed the developmental trajectory of cortical networks derived from SOD1G93A mouse embryos using an integrated, multimodal strategy. Experimental data, including imaging and electrophysiological measurements, were coupled with computational modeling and machine learning to probe network-level alterations and identify predictive signatures of dysfunction. ALS-derived networks exhibited an early phase marked by increased excitability that progressively evolved into a fragmented and inefficient network organization with impaired functional connectivity. Disrupted astrocytic support at early stages compromised synchronization, while synaptic investigations uncovered a marked excitatory-inhibitory imbalance, including inefficient inhibitory compensation and GABA/glutamate co-transmission. In silico simulations identified defective intrinsic adaptation as a key driver of network hyperexcitability, and machine learning classifiers reliably captured electrophysiological patterns, predicting early network dysfunction. Overall, these findings characterize ALS as a neurodevelopmental disorder of cortical network homeostasis, driven by the convergence of synaptic dysregulation, glial deficits, and impaired intrinsic adaptation. By combining experimental electrophysiology with computational modeling, this study establishes a mechanistic link between early network instability and subsequent disease progression and highlights network-level electrophysiological signatures as promising biomarkers for early diagnosis and therapeutic screening.