New approach for identification pHFO networks to predict epileptogenesis

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PROJECT SUMMARY Epilepsy is among the most common serious neurological disorders, and about 40% of epilepsy patients do not respond to existing treatment. Clinically, the prolonged, refractory epilepsy with negative surgical outcomes is often associated with distributed epilepsy onset rather than a local epileptogenic zone. Understanding the epilepsy as a large-scale brain network abnormality enables the development of new treatment options and research directions. At present, the majority of research related to analysis of the epileptic network has been focused on the ictal period, while few have been devoted to the analysis of the earlier stages of epileptogenesis (latent period). Investigating the brain network properties of epileptogenesis is as important and can help develop antiepileptogenic interventions for epilepsy prevention and cure. Early in our experiments, we discovered pathological high-frequency oscillations (pHFOs), which are reliable biomarkers of epileptogenesis. They are generated by clusters of pathologically interconnected neurons (PIN-clusters) and reflect bursts of population spikes. Recent updates in the animal models of chronic epilepsy evidenced the spatially distributed pHFO events, which implies the development of large-scale PIN-cluster networks during epileptogenesis. It is critical to study the network topology and characteristics of PIN-cluster-formed epileptogenic networks in order to further understand the underlying mechanisms of epileptogenesis. To fulfill this gap, the present study plan is to explore pHFO-based networks using the Kainic Acid (KA)- induced status epilepticus (SE) model of epileptogenesis. We hypothesize that epileptogenesis after SE is dependent upon the formation of large-scale PIN-cluster networks that is expressed by the spatial occurrence and temporal coupling of pHFOs. Combining the biocompatible, organic–material based neural interface array (NeuroGrid) with multichannel silicon probes, we aim to identify the spatial and temporal profiles of pHFOs (Aim1). Using the advanced computational algorithms such as graph theory analysis and Shannon Entropy (SE), we propose to investigate the causal relationship and characteristics of the pHFO-based epileptogenic networks (Aim2). The outcome of this study will assess the robustness of novel network-based recording design and algorithm development. It will also determine whether the pHFO-derived network parameters are a reliable biomarker of epileptogenesis. The future plans are to translate the pHFO-network concept and computational tools into the clinical study of epilepsy. This approach may open a new direction to the prevention of epilepsy development and cure epilepsy.