Neural network reorganization following early-life seizures: neuroprotective role of ACTH in preserving cognitive function

Abstract

Action potentials represent the fundamental mechanism by which neurons transmit and encode signals, governed by complex and interdependent neural coding schemes known as rate, temporal, and population coding. Disruptions to these schemes, particularly during early developmental periods, can lead to profound and enduring cognitive deficits. This thesis investigates how early-life neurological insults, specifically early-life seizures (ELS), disrupt neural dynamics and subsequently impair cognitive functions. We chose the medial prefrontal cortex (mPFC) as our primary region of interest due to its critical but less extensively studied role in cognitive processes compared to regions like the hippocampus. Utilizing electrophysiological, behavioral and computational approaches, including single-unit recordings, fear extinction learning, graph theoretical analysis, and Graph Attention Networks (GATs), we characterized disruptions in neuronal firing rates, neuronal spike-timing, and population connectivity in the mPFC following ELS. ☐ Our findings demonstrate that ELS significantly impairs cognitive function by inducing rigid temporal firing patterns, reducing firing rates, and disrupting population-level network dynamics essential for adaptive cognitive processing. Importantly, implementing a neuroprotective intervention using Adrenocorticotropic Hormone (ACTH), acting through melanocortin 4 receptor (MC4R) signaling pathways, effectively preserved neural coding dynamics and rescued cognitive performance post-ELS. This therapeutic approach highlights the centrality of maintaining flexible and dynamic neural network function as a critical determinant of cognitive resilience. ☐ The future direction of this dissertation extend beyond ELS, suggesting a convergent mechanistic model where disrupted neural dynamics underpin cognitive dysfunction across multiple neurological and neurodevelopmental disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Fragile X syndrome (FMR1), and traumatic brain injury (TBI). By emphasizing the preservation of neural network dynamics, this thesis advocates for a fundamental shift toward network-centric therapeutic strategies aimed at maintaining and restoring cognitive integrity across diverse neurological conditions.