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There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals.We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. ‘root mean squared EEG frequency’). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

For the first time in research in humans, we used simultaneous icEEG-fMRI to examine the link between connectivity in haemodynamic signals during the resting-state (rs) and connectivity derived from electrophysiological activity in terms of the inter-modal connectivity correlation (IMCC). We quantified IMCC in nine patients with drug-resistant epilepsy (i) within brain networks in 'healthy' non-involved cortical zones (NIZ) and (ii) within brain networks involved in generating seizures and interictal spikes (IZ1) or solely spikes (IZ2). Functional connectivity (h 2 ) estimates for 10 min of resting-state data were obtained between each pair of electrodes within each clinical zone for both icEEG and fMRI. A sliding window approach allowed us to quantify the variability over time of h 2 (vh 2) as an indicator of connectivity dynamics. We observe significant positive IMCC for h 2 and vh 2, for multiple bands in the NIZ only, with the strongest effect in the lower icEEG frequencies. Similarly, intra-modal h 2 and vh 2 were found to be differently modified as a function of different epileptic processes: compared to NIZ, [Formula: see text] was higher in IZ1, but lower in IZ2, while [Formula: see text] showed the inverse pattern. This corroborates previous observations of inter-modal connectivity discrepancies in pathological cortices, while providing the first direct invasive and simultaneous comparison in humans. We also studied time-resolved FC variability multimodally for the first time, finding that IZ1 shows both elevated internal [Formula: see text] and less rich dynamical variability, suggesting that its chronic role in epileptogenesis may be linked to greater homogeneity in self-sustaining pathological oscillatory states.

Highlights • We created a validation method for the evaluation of automated classification of interictal spikes. • We used a modified version of Wave_clus (WC) to automatically classify the data of 5 patients. • WC classification was similar to EEG reviewers providing an unbiased evaluation of the clinical data., Objective To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Method Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers’ spike identification and individual spike class labels visually and quantitatively. Results The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. Conclusions WC performance is indistinguishable to that of EEG reviewers’ suggesting it could be a valid clinical tool for the assessment of IEDs. Significance WC can be used to provide quantitative analysis of epileptic spikes.

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., Highlights ► Seizure EEG–fMRI data of a hypothalamic hamartoma epileptic patient was analysed. ► Two competing hypotheses for seizure propagation pathways were tested. ► DCM was used to estimate all plausible connectivity models of seizure propagation. ► Bayesian model selection yielded a model consistent with previous evidence. ► This study provides the first application of DCM to EEG–fMRI data of human seizures.

The EEG acquired simultaneously with fMRI is distorted by a number of artefacts related to the presence of strong magnetic fields, which must be reduced in order to allow for a useful interpretation and quantification of the EEG data. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. However, a number of artefacts related to the MR-environment can be found to distort the EEG data acquired even without ongoing fMRI acquisition. In this paper, we investigate the most prominent of those artefacts, caused by the Helium cooling pump, and propose a method for its reduction and respective validation in data collected from epilepsy patients. Since the Helium cooling pump artefact was found to be repetitive, an average template subtraction method was developed for its reduction with appropriate adjustments for minimizing the degradation of the physiological part of the signal. The new methodology was validated in a group of 15 EEG–fMRI datasets collected from six consecutive epilepsy patients, where it successfully reduced the amplitude of the artefact spectral peaks by 95 ± 2 % while the background spectral amplitude within those peaks was reduced by only −5 ± 4 %. Although the Helium cooling pump should ideally be switched off during simultaneous EEG–fMRI acquisitions, we have shown here that in cases where this is not possible the associated artefact can be effectively reduced in post processing.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations.

A study of the epileptic seizure dynamics using simultaneous recording of electroencephalography correlated functional magnetic resonance imaging (EEG-fMRI) data from 5 focal epilepsy patients undergoing pre-surgical evaluation was realised with the aim of identifying the focus and propagation network seizure-involved. A method based on the General Linear Model (GLM) at different neurophysiology regressor lags (LasgM), a connectivity model-based method, Dynamic Causal Modelling (DCM), and a data-driven method, Granger Causality (GC) were investigated. DCM analysis provided meaningful and significant results when a sufficient number of seizure events was recorded, but suffered from the generally poor data signal-to-noise ratio (SNR). The concordance between the LagsM results and the clinical expectation suggests that LagsM can be useful as a complementary approach. Intending to establish the validity of a GC approach for the problem addressed, a simulation study was performed and the results showed that it is not appropriated.