The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal (the so-called EEG-informed fMRI integration strategy), the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Simultaneous electroencephalogram (EEG)-functional Magnetic Resonance Imaging (fMRI) recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a hemodynamic response function to model the associated Blood Oxygen Level Dependent (BOLD) changes. Although more flexible approaches may allow a higher degree of complexity for the hemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent, and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity, and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.

Simultaneous EEG-fMRI offers the possibility of non-invasively studying the spatiotemporal dynamics of epileptic activity propagation from the focus towards an extended brain network, through the identification of the haemodynamic correlates of ictal electrical discharges. In epilepsy associated with hypothalamic hamartomas (HH), seizures are known to originate in the HH but different propagation pathways have been proposed. Here, Dynamic Causal Modelling (DCM) was employed to estimate the seizure propagation pathway from fMRI data recorded in a HH patient, by testing a set of clinically plausible network connectivity models of discharge propagation. The model consistent with early propagation from the HH to the temporal-occipital lobe followed by the frontal lobe was selected as the most likely model to explain the data. Our results demonstrate the applicability of DCM to investigate patient-specific effective connectivity in epileptic networks identified with EEG-fMRI. In this way, it is possible to study the propagation pathway of seizure activity, which has potentially great impact in the decision of the surgical approach for epilepsy treatment.

Summary:  Purpose: The epilepsy associated with hypothalamic hamartomas (HHs) has typical clinical, electrophysiologic, and behavioral manifestations refractory to drug therapy and with unfavorable evolution. It is well known that only sessile lesions produce epilepsy, but no correlation has been established between the different types of sessile hamartomas and the diverse manifestations of the epilepsy. We correlate anatomic details of the hamartoma and the clinical and neurophysiologic manifestations of the associated epilepsy. Methods: HHs of seven patients with epilepsy (ages 2– 25 years) were classified as to lateralization and connection to the anteroposterior axis of the hypothalamus by using high‐resolution brain magnetic resonance imaging. We correlated the anatomic classification with the clinical and neurophysiologic manifestations of the epilepsy as evaluated in long‐term (24 h) video‐EEG recordings. Results: HHs ranged in size from 0.4 to 2.6 cc, with complete lateralization in six of seven patients. Ictal manifestations showed good correlation with the lobar involvement of ictal/interictal EEGs. These manifestations suggest the existence of two types of cortical involvement, one associated with the temporal lobe, produced by hamartomas connected to the posterior hypothalamus (mamillary bodies), and the other associated with the frontal lobe, seen in lesions connecting to the middle hypothalamus. Conclusions: A consistent clinical and neurophysiologic pattern of either temporal or frontal lobe cortical secondary involvement was found in the patients of our series. It depends on whether the hamartoma connects to the mamillary bodies (temporal lobe cases) or whether it connects to the medial hypothalamus (frontal lobe cases).