A novel neural computational model of generalized periodic discharges in acute hepatic encephalopathy

• Published in: Journal of computational neuroscience 2019-12, Vol.47 (2-3), p.109-124
• Main Authors: Song, Jiang-Ling, Paixao, Luis, Li, Qiang, Li, Si-Hui, Zhang, Rui, Westover, M. Brandon
• Format: Article
• Language: English
• Subjects: Arousal; Biomedical and Life Sciences; Biomedicine; Brain; Brain - physiopathology; Computational neuroscience; Computer Simulation; Delirium; Design optimization; Design parameters; Discharge; EEG; Electroencephalography; Epilepsy; Excitability; Firing pattern; Hepatic encephalopathy; Hepatic Encephalopathy - physiopathology; Human Genetics; Humans; Liver diseases; Mental disorders; Models, Neurological; Neurology; Neurosciences; Periodic variations; Periodicity; Synaptic transmission; Theory of Computation
• ISSN: 0929-5313; 1573-6873; 1573-6873
• DOI: 10.1007/s10827-019-00727-3

A cute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired attention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain activity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic discharges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE, how these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds a computational model of AHE (AHE-CM), based on three modifications of the well-studied Liley model which emulate mechanisms believed central to brain dysfunction in AHE: increased neuronal excitability, impaired synaptic transmission, and enhanced postsynaptic inhibition. To relate our AHE-CM to clinical EEG data from patients with AHE, we design a model parameter optimization method based on particle filtering (PF-POM). Based on results from 7 AHE patients, we find that the proposed AHE-CM not only performs well in reproducing important aspects of the EEG, namely the periodicity of triphasic waves (TPWs), but is also helpful in suggesting mechanisms underlying variation in EEG patterns seen in AHE. In particular, our model helps explain what conditions lead to increased frequency of TPWs. In this way, our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium by relating microscopic mechanisms to EEG patterns.