CRCNS: Neural Populations, High Frequency Oscillations and EEG seizures

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Abstract Epilepsy is a neurological disease affecting 65 million people worldwide. Patients with medically intractable seizures and with a clearly localized focus can often be successfully treated by surgical resection of the epileptic tissue. However, localization accuracy is limited, especially if overt structural abnormalities are absent. High frequency oscillations have been proposed as a key localizing biomarker. Unfortunately, progress in understanding the dynamics of seizure electrophysiology has been impeded by lack of measurement techniques for detailed monitoring of large networks both the temporal and spatial domains. We propose that multiscale network modeling incorporating known pathological mechanisms can provide useful insights into the circumstances under which high frequency oscillations are generated. We propose an interdisciplinary approach coupling modeling with an existing dataset of unique multiscale recordings, ranging from single-cell to large networks of millions of cells, during seizure activity in human cortical networks. Our proposal is focused around several important clinical questions. First, we seek to define precisely how high frequency spectral components in broadband clinical recordings reflect the pathological activity specific to seizing brain areas. Second, we will identify the neuronal mechanisms permitting localized pathological activity to propagate, and how this propagation affects the properties· of broadband clinical recordings. In contrast to the prior literature in this area, our approach explicitly incorporates the network effects of cellular dynamics known to occur during experimental seizures, I.e. paroxysmal depolarization and depolarization block of specific cell populations. In Aim 1 we use scalable and detailed modeling of individual network nodes to relate single-cell activity to the mesoscopic network scale. The focus of this aim is to determine how network interactions generate high frequency oscillations. Independently, in Aim 2 we test the hypothesis that these small, mesoscopic networks are responsible, via propagation of the high frequency components, for the high-gamma oscillation in macroelectrode signals. We accomplish this by generating the macroelectrode signal with a simple linear model that employs single-cell and small network signals as its input. In Aim 3 we relate the observed seizure propagation, due to failure of the inhibitory veto, to network dynamics associated with the paroxysmal depolarization block. For all aims we will validate simulated results with data recorded during experimental seizures in slices of human cortex (single-cell and local network activity), and in vivo microelectrode recordings during human seizures (multi-unit as well as local network activities).