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