Modeling of the Neurovascular Coupling in Epileptic Discharges

Abstract

Despite the interest in simultaneous EEG-fMRI studies of epileptic spikes, the link between epileptic discharges and their corresponding hemodynamic responses is poorly understood. In this context, biophysical models are promising tools for investigating the mechanisms underlying observed signals. Here, we apply a metabolic-hemodynamic model to simulated epileptic discharges, in part generated by a neural mass model. We analyze the effect of features specific to epileptic neuronal activity on the blood oxygen level dependent (BOLD) response, focusing on the issues of linearity in neurovascular coupling and on the origin of negative BOLD signals. We found both sub- and supra-linearity in simulated BOLD signals, depending on whether one observes the early or the late part of the BOLD response. The size of these non-linear effects is determined by the spike frequency, as well as by the amplitude of the excitatory activity. Our results additionally indicate a minor deviation from linearity at the neuronal level. According to a phase space analysis, the possibility to obtain a negative BOLD response to an epileptic spike depends on the existence of a long and strong excitatory undershoot. Moreover, we strongly suggest that a combined EEG-fMRI modeling approach should include spatial assumptions. The present study is a step towards an increased understanding of the link between epileptic spikes and their BOLD responses, aiming to improve the interpretation of simultaneous EEG-fMRI recordings in epilepsy.

Computational models of epileptiform activity

“…Although they are not strictly-speaking focused on epileptiform activity, some recently-proposed models include a vascular and/or a glial compartment in addition to the neuronal compartment. These so-called neuro-glio-vascular models were proposed at various levels of description, typically cellular (Aubert and Costalat, 2002), neural mass (Sotero and Trujillo-Barreto, 2008;Blanchard et al, 2011;Voges et al, 2012) and neural field (Bojak et al, 2011). Their main advantages include the possibility to assess metabolic variables (O 2 , glucose) or astrocytic functions (glutamate uptake) or to help the interpretation of multimodal EEG-fMRI data.…”

A study of the electro-haemodynamic coupling using simultaneously acquired intracranial EEG and fMRI data in humans

“…A metabolic-hemodynamic model proposed by Sotero and Trujillo-Barreto, 2007, Sotero and Trujillo-Barreto, 2008, foresees amplitude, duration, and area under the excitatory curve of the sharp wave as good predictors of the amplitude of the BOLD signal (Voges et al, 2012). In the standard model of the BOLD effect, an increase in neuronal activity induces an increase in CBF, which provides more oxygen and glucose to the tissues; if the increase in CBF exceeds the simultaneous increase in oxygen consumption, the local concentration of deoxyhaemoglobin decreases and the intensity of the BOLD effect increases (Buxton, 2012).…”

Optical imaging of acute epileptic networks in mice

“…11 Separate studies of epileptic networks documented a decrease of functional connectivity in the self-referential, somatosensory, visual, and auditory networks, 22 as well as in the default mode network 20,23,24 and in the dorsal attention network. 25 The distributed nature of activation during epileptic seizures, the heterogeneity within the patient population and nonlinear coupling 26,27 between vascular components during large neuronal discharges support investigating these phenomena in controlled animal models to better characterize network changes with epileptic events.…”