Unraveling the dynamics of seizure-like activity in neuronal networks using machine learning

Seizures affect millions worldwide, significantly impacting quality of life and increasing the risk of premature death. Although seizure-like activity in neuronal networks is commonly characterized by synchronized firing, the mechanisms underlying network reorganization and dynamics remain incompletely understood. A better understanding of how network firing changes during seizure-like activity can improve diagnosis and treatment approaches for seizures. Here, we combine an in vitro model of primary cortical networks cultured on microelectrode arrays with a multilayered machine learning (ML) pipeline to investigate bicuculline-induced seizure-like activity. Our multistep analysis approach, which consists of using a long short-term memory (LSTM) autoencoder for dimensionality reduction, followed by uniform manifold approximation and projection (UMAP) and hierarchical clustering, revealed the emergence of distinct neuronal subpopulations with characteristic activity profiles after seizure-like activity induction, even within globally synchronized network activity states. Furthermore, deep Granger causality, an advanced analysis technique for identifying predictive relationships in data, applied to our nonlinear time series, revealed disproportionate neuronal responses to seizure-like activity-driven network firing changes. We also trained ML classifiers to distinguish native and seizure-like activity states with high accuracy using different firing features. Spike rate was the most significant feature for achieving high classification accuracy. Ultimately, these findings demonstrate the power of our analytical framework for characterizing seizure-like activity. Our low-cost, two-dimensional model of seizure-on-a-chip, combined with novel performance metrics, could serve as a valuable tool for screening potential new antiepileptic drugs and for gaining a deeper understanding of how seizure-like activity alters the functional organization of neuronal networks.<b>NEW & NOTEWORTHY</b> This study introduces a novel approach for analyzing seizure-like activity in neuronal networks using a multilayered machine learning pipeline. We show that even during drug-induced network disinhibition leading to global network synchronization, neurons organize into distinct, spatially distributed subnetworks with unique firing profiles. Our innovative seizure-on-a-chip model enables the comprehensive investigation of network reorganization and dynamics during seizure-like activity, which could potentially be useful for understanding pathophysiology, developing diagnostic tools, and screening new antiepileptic drugs.