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

Abstract

Acute 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.

Keywords: Acute hepatic encephalopathy (AHE); Electroencephalogram (EEG); Generalized periodic discharges; Liley model; Neural computational model; Particle filtering.

Fig. 1

Sketch of Liley model.

Fig. 2

The detailed transformation of electrical activities in Liley model.

Fig. 3

An AHE-EEG segment and extracted features.

Fig. 4

The procedure of particle-filter-based parameter estimation method.

Fig. 5

(a) A contrast between 7 types of model outputs and their labels; (b) the distribution of model output labels under 9 different cases where (F_am, γ_i) ϵ (3, 1.5, 0.8) × (0.065, 0.032, 0.014) (here, ${\tau }_e^{rec}(500:100:20000)$, ${\tau }_i^{rec}(250:50:2500)$; (c) color bar of 7 types of model outputs.

Fig. 6

The effects of adding three extra mechanisms added to the baseline Liley model.

Fig. 7

The feature F₁ of two AHE-EEG signals (S_fast and S_slow).

Fig. 8

The optimized values of ${\tau }_e^{rec}$ and ${\tau }_i^{rec}$ in 50 trails.

Fig. 9

Comparison between the original simulated EEG signal (labeled as “ground truth”) and the model output with the optimal values determined by PF-POM (labeled as “simulated”)

Fig. 10

An AHE-EEG segment (20s) from “patient 1” and the simulated EEG segment by the proposed approach “AHE-NCM + PF-POM”

Fig. 11

a) The distribution of mean of ITIs in a parameter space ${\tau }_e^{rec}\times {\tau }_i^{rec}$ ; b) color bar of 7 types of model outputs.