Characterizing Neuroplasticity in SYNGAP1 Mutants through Integrated Computational and Experimental Approaches

Mutations in SYNGAP1 are strongly associated with neurodevelopmental disorders such as autism, intellectual disability, and epilepsy, which are linked to disruptions in synaptic plasticity and neuronal network function (Satterstrom et al., 2020). To elucidate the neuroplasticity deficits in SYNGAP1 -mutant neurons, we propose a three-pronged approach combining computational modeling, high-resolution extracellular electrophysiology, and intracellular validation to fully characterize network and cellular dynamics in these mutants. Development of Network Simulations. We are constructing computational network models using the NetPyNE framework (Dura-Bernal et al., 2019) that integrate features derived from experimental data collected using high-density microelectrode arrays (HD-MEAs) equipped with 26,400 platinum electrodes (Ballini et al., 2014). These simulations incorporate key network-level features such as connectivity, spatiotemporal dynamics, and morphology, derived using advanced spike sorting, footprinting, and axon reconstruction techniques. Machine learning and information-theoretic algorithms refine parameters to ensure high fidelity in replicating observed SYNGAP1 -mutant network behaviors. Age-Dependent Modeling. We aim to create a series of developmental models to capture the evolving properties of SYNGAP1 -mutant neuronal networks across different stages of maturation. This longitudinal modeling approach will identify critical windows and shifts in network properties, allowing comparisons to normative developmental trajectories (Satterstrom et al., 2020). Integrated Experimental Validation. Electrophysiological data collected using HD-MEA platforms (Obien et al., 2015) will inform model development, while patch-clamp recordings will provide intracellular perspectives for direct comparison. This dual approach ensures that extracellular and intracellular dynamics are coherently represented, enabling a comprehensive assessment of SYNGAP1 -mutant network function. Aims. 1. Aim 1: To define the developmental trajectories of SYNGAP1 -mutant neuronal networks by integrating extracellular and intracellular electrophysiological data and computational models, identifying critical deviations from normative developmental patterns. 2. Aim 2: To use computational modeling to uncover mechanistic underpinnings of SYNGAP1 -associated behavioral abnormalities and infer otherwise inaccessible processes that contribute to the observed network and cellular deficits. Preliminary Findings and Implications. Early results suggest that SYNGAP1 -mutant neuronal networks exhibit aberrant connectivity patterns and altered plasticity mechanisms, with distinct developmental signatures. Our computational framework, guided by high-resolution experimental data, provides a powerful tool for linking these network-level differences to observed behavioral deficits. By integrating intracellular and extracellular data, we aim to uncover otherwise inaccessible mechanisms, advancing our understanding of SYNGAP1 -related neurodevelopmental disorders. Adam Weiner, the principal investigator for this study, is partially supported by a stipend provided by the American Physiological Society (APS). This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.