Autoregressive Modeling of the Eeg in Systemic Kainic Acid-Induced Epileptogenesis

Abstract

Background activity as well as three kinds of bilateral epileptiform discharges, recorded from the cerebral cortex and hippocampus of freely behaving rats treated with intravenous kainic acid (KA), were analysed by the directed transfer function (DTF) method within multivariate autoregressive modeling of the EEG. This method reveals statistical influence (flow of activity) between brain regions at different frequencies. There was no significant influence between rhythms in different brain regions in the background EEG. Early after KA administration, low frequency rhythms (< 10Hz) in the frontal cortex began to lead slow rhythms in other areas and high frequency rhythms (20-60 Hz), possibly gamma oscillations, intensified in the hippocampus. In spike-wave discharges, frontal cortex led both low and high frequency rhythms. Initially during generalised non-convulsive discharges, slow rhythms originated from frontal cortex and high frequency rhythms from hippocampus while later, slow rhythms as well, often arose from hippocampus. During the convulsive discharge, the flow of activity of dominant slow rhythms repeatedly changed between hippocampus and neocortex, with more frequent dominance of the hippocampus, while hippocampus continued to lead high frequency rhythms. We conclude that KA-induced epileptiform discharges are cortical and hippocampal events, specifically that the frontal cortex is early to express low frequency rhythms and the hippocampus, high frequency rhythms. More generally, the findings suggest that epileptiform discharges result from interacting rhythms of different frequencies that arise from different structures, and that gamma oscillations possibly contribute to widespread synchronisation during some forms of epileptogenesis.

Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals

“…It has been used to localize the epileptogenic focus during stationary seizure epochs (Franaszczuk et al, 1994;Franaszczuk and Bergey, 1998;Wilke et al, 2009) and to investigate kainic acid-induced epileptogenesis in rats (Medvedev and Willoughby, 1999). The DTF has been applied to various biomedical signals: fMRI time-series (Deshpande et al, 2009), scalp EEG for sleep studies (Kaminski et al, Jan. 1995;Kaminski et al, 1997;De Gennaro et al, 2004;De Gennaro et al, 2005;Salih et al, 2009) and Evoked Related Potentials Liang et al, 2000;Mima et al, 2001;Ginter et al, 2001;Babiloni et al, 2005).…”

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

“…This concept refers to the pattern of causal interactions between the elements of a network (Friston, 1994; Sporns, 2007). Effective connectivity has been investigated using multivariate measures related to Granger causality (Baccala and Sameshima, 2001; Kaminski and Blinowska, 1991; Sameshima and Baccala, 1999) to study the sources of seizure onset, as well as the neural circuitry of epileptogenic brain tissue (Ding et al, 2007; Franaszczuk and Bergey, 1998; Franaszczuk et al, 1994; Ge et al, 2007; Korzeniewska et al, 2012b; Medvedev and Willoughby, 1999; Takahashi et al, 2007; Wilke et al, 2008; Wilke et al, 2010; Wilke et al, 2009b). …”

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

“…Autoregressive (AR) models have been successfully applied to the analysis of EEG signals including simulation (Charbonnier et al, 1987;Kaipio and Karjalainen, 1997a), spectral estimation (Madhavan et al, 1991;Medvedev and Willoughby, 1999;Guller et al, 2001;Moller et al, 2001;Subasi, 2007), classification (Wada et al 1996;Subasi et al, 2005), and synchronization (Franaszczuk and Bergey, 1999). A common routine for dealing with non-stationary EEG signals using timeinvariant AR models is to partition a long time-course of data into several segments and then apply an AR modelling approach to each of these segmentations that can be treated as stationary processes (Praetorius et al, 1977;Michael and Houchin, 1979;Barlow, 1985;Amir and Gath, 1989).…”