S176. A Mean field model to study the triphasic waves in acute hepatic encephalopathy. (May 2018)

• Record Type: Journal Article
• Authors: Song, Jiangling; Sun, Haoqi; Jing, Jin; Zhang, Rui; Cash, S. Sydney; Westover, M. Brandon
• Is Part Of: Clinical neurophysiology. Volume 129(2018)Supplement 1
• Page Start: e207
• Page End: e208
• Publication Date: 2018-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.536
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Introduction: Triphasic waves (TWs) are a distinctive electroencephalogram (EEG) pattern consisting of consisting 'triphasic' epileptiform discharges repeating at relatively regular intervals, most commonly in patients with acute, severe toxic/metabolic encephalopathy (TME). TWs share some similarities with EEG patterns seen in non-convulsive status epilepticus (NCSE), and show similar responses to benzodiazepine administration. Nevertheless the mechanisms underlying TWs are poorly understood, and their relationship to NCSE debated. Here we explore the conditions in a biologically plausible computational model of the EEG that lead to patterns resembling TWs, and attempt to relate these to mechanisms operative in one type of TME: acute hepatic encephalopathy (AHE). Methods: Our work builds on an existing neural mean field model (NMFM) developed for how periodic discharges arise in severe cerebral anoxia (an adaptation of the bursting Liley model), through three types of processes: (a) metabolic failure leading to impaired synaptic transmission; and (b) increased neuronal excitability. In addition, the model accounts for effects of (c) GABA-ergic modulation by anesthetics. We identify pathophysiologic mechanisms involved in AHE which can lead to each of these effects, and relate these mechanisms to changes in the parameters of the NMFM. In this way our model relates "microscopic" (biochemical) mechanisms to "macroscopic" observations (EEG patterns). Then, we relate the simulated EEG patterns to clinical EEG data of patients with AHE through by constructing a comparison function, on the basis of different feature extraction methods, similarity measurements and optimization algorithms. Results: The model is able to reproduce key qualitative features of EEG data from patients with AHE, including the approximate shape, irregular and slowed background, and semi-periodic pattern of TWs. In addition, we find that the dynamic evolution of EEG activity during AHE can be characterized through the change of four key parameters of proposed NMFM ((recovery time constants), (potentiation factor of EPSP), (decay rate of IPSP)), which reflect gradual changes in underlying physiological mechanisms. Conclusion: Known alterations in cerebral physiology in acute hepatic encephalopathy are relatable to parameters of a biologically plausible mean-field model of the EEG. Our model represents a starting point for exploring the dynamical mechanisms underlying the EEG of severe encephalopathy.