Neural Codes Characterizing Epileptic Brain States
Keywords: Epilepsy
High Frequency Oscillation (HFO)
Intracranial Electroencephalogram (iEEG)
Low Frequency Oscillation (LFO)
Modulation Index (MI)
Region of Interest (ROI)

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. For patients with medically intractable epilepsy, clinicians identify seizure onset zones (SOZs) for resection in an attempt to localize the epileptogenic zone (EZ), which is the cortical tissue that is indispensible for seizure generation. The overarching aim of this thesis was to investigate the interactions between different neural rhythms and to determine how these interactions can be used as potential biomarkers of epileptogenicity and epileptogenic tissue, which were ultimately used to develop an automated system for objectively localizing this EZ by identifying regions of interest (ROIs). Complex wavelet coefficient-based features of various neural rhythms (1 - 200 Hz) were used to investigate the temporal state classification of seizure-like events (SLEs) from three different rodent models of epileptogenicity. An unsupervised learning paradigm - namely, the hidden Markov model (HMM) algorithm - was applied. The presence of these neural rhythms was not sufficient for temporal state classification in human intracranial electroencephalogram (iEEG) recordings. The increased complexity of a human neuronal network required higher order features for state classification - namely, features of cross-frequency coupling. Modulation of the amplitude of high frequency oscillations (HFOs) by the phase of low frequency oscillations (LFOs) was measured by computing the modulation index (MI). Delta- (0.5 - 4 Hz) and theta- (4 - 8 Hz) modulated HFOs (30 - 450 Hz) were examined across the channels of a 64-electrode subdural grid. HMM training was performed on mean MI values and channels consistently capturing two and/or three states were identified. The 2-state HMMs were able to distinguish between seizure and non-seizure states in six of seven patients while the 3-state HMMs were able to additionally distinguish between seizure transition states in four of seven patients. Moreover, the HMM-identified channels were found to overlap with the resected tissue in patients experiencing an improved post-surgical outcome while there was no overlap observed when post-surgical improvement was not achieved. Mean MI values were also subjected to eigenvalue decomposition (EVD) and channels defining the ROIs were selected based on the components of the eigenvector corresponding to the largest eigenvalue. ROIs were compared to the SOZs identified by two independent neurologists as well as the region of resected tissue. MI was found to capture the seizure in time for six of seven patients and identified ROIs in all seven. The number of EVD-selected channels that were not identified by either neurologist was found to correlate with the post-surgical outcome when resection was performed. Specifically, patients had a poorer post-surgical outcome when the number of unidentified EVD-selected channels increased. Moreover, in patients who experienced a seizure-free outcome (i.e., Engel Class I) all EVD-selected channels were found to be within the resected tissue or immediately adjacent to it. These findings suggest that LFO-modulated HFOs can be used to identify ROIs in extratemporal lobe patients. These ROIs may result in better surgical outcomes when used to compliment the SOZs identified by clinicians for resection.