Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI

Abstract

Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

Keywords: Dynamic functional connectivity; EEG-fMRI; Epilepsy; Pre-surgical planning.

Fig. 1

Pipeline of the dFC-EEG approach. The figure describes the steps to investigate the dependency of the epileptic network dynamic from the epileptic activity, expressed as the variance of the convolved IED (VarIED), by showing patient P2 (see Table 1 for IED type, number, and inter-IED period). The IED detected from the corrected EEG (A.a) were convolved with multiple HRF (A.b) and used to derive the IED-related BOLD map (A.c), from which the epileptic focus was identified. Average BOLD timecourses were extracted from the fROIs defined by a second GLM with the focus timecourse as main regressor (A.d). The fROIs-to-fROIs dFC was evaluated with a sliding-window approach (B.f), that was also applied to derive the variance of the convolved IED; i.e., the VarIED signal (B.e). The significant Spearman correlations between fROIs-to-fROIs dFC timecourses and the VarIED defined the connections included in the dynamic epileptic subnetwork (C.g).

Fig. 2

Size of the dynamic epileptic subnetwork. The bar plot in a) shows in red the percentage of connections that are part of the dynamic epileptic subnetwork, with respect to the total number of connections of the full (static) epileptic network, for each patient. In b) the total number of connections N within the epileptic network are reported for each patient. In c), the details on the acquisition parameters (see, Table 2) are shown in color-coded modality: red and orange for the MR scanner magnetic strength (3 and 1.5 Tesla, respectively); dark and light blue for the number of electrodes used for the simultaneous EEG-fMRI acquisition (64 and 256, respectively); dark, semi-dark and light green for parameters of the functional sequence used for the fMRI (combination of the repetition time (TR) expressed in seconds and the number of functional volumes). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Visualization of dynamic epileptic subnetwork. For each patient, the dynamic epileptic subnetwork is shown in the form of a brain graph in axial, coronal and sagittal view. Green spheres of equal size represent fROIs, labelled with a number indicating their statistical relevance in the epileptic network (Paragraph 2.7). The strength of significant connections is color-coded according to a global color bar scaled in the range [-1, 1]. The dynamic epileptic subnetwork is also reported in the form of lower triangular correlation matrix with equivalent color-code. The lightning bolt indicates the epileptogenic hemisphere for each patient. L : left ; R : right. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Summary of the dynamic epileptic subnetwork lateralization with respect to the focus. For each patient, the percentages of intra-hemispheric connections ipsilateral (in yellow) to the epileptic focus, contralateral (in light blue) to the epileptic focus as well as inter-hemispheric (in teal) are reported, normalized to the total number of connections of the dynamic epileptic subnetwork. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Laterality index of the connections. For each patient, the laterality index of connections, evaluated according to the definition in (Eq.1), is shown for the static epileptic network (pink) and for the dynamic epileptic subnetwork (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)