Quantitative interictal networks to localize seizure generators

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PROJECT SUMMARY Surgical outcomes for patients with intractable epilepsy are modest, in large part due to mis-localization of seizure generators. Currently, epileptologists localize seizure generators by manually identifying the seizure onset zone on intracranial EEG. There is also abundant interictal EEG data (between seizures) that is largely ignored because, until now, clinicians lacked rigorous approaches to interpret this data. Recent work by Dr. Conrad, her co-authors, and others, validated two quantitative methods to analyze interictal EEG: interictal epileptiform discharges (IEDs) and functional connectivity (FC). This work demonstrates that quantitative analysis of IEDs and FC reveals regions — referred to here as hubs — that help identify seizure generators (Conrad et al., Brain, 2020; Conrad et al., Network Neuroscience, 2020). This proposed project will evaluate IED and FC hubs in order to elucidate their mechanisms and their potential utility in surgical planning. The central hypothesis of this proposal is that epilepsy measurably alters the interictal network, allowing us to probe seizure generators with interictal data. The objectives of the proposal are: to determine the temporal stability and spatial correspondence between IED and FC hubs (Aim 1), to determine if these hubs localize seizure generators (Aim 2), and to actively probe the interictal network with cortical stimulation to verify hypothesized FC hubs (Aim 3). These studies will expand our understanding of epileptic networks. If successful, quantitative analysis of interictal EEG will enhance localization of seizure generators and improve surgical outcomes. This proposed project will also provide critical career development training to Dr. Conrad, an Epilepsy Instructor at University of Pennsylvania. The proposal builds on Dr. Conrad's background in quantitative EEG analysis and clinical epilepsy. Dr. Conrad will be mentored by Brian Litt, a world-renowned expert in computational epilepsy; Danielle Bassett, a leading expert in network theory; and Eric Marsh, a leading expert in quantitative IED analysis. The training plan in this proposal includes mentored completion of the proposed research, as well as a rigorous program of didactics, workshops, lab meetings, and directly relevant clinical work. Together, these experiences will provide essential training in time-series statistics, machine learning, subject enrollment, cortical stimulation, and EEG data acquisition. This proposed project and training plan will launch Dr. Conrad on an independent research career focused on using a multimodal approach to evaluating interictal data in order to improve our understanding and treatment of epilepsy.