No abstract

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.

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.

Matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) at atmospheric pressure (AP) is, with a few notable exceptions, overshadowed by its vacuum based forms and AP transmission mode (TM) MALDI-MS lacks the uptake its potential benefits might suggest. The reasons for this are not fully understood and it is clear further development is required to realise the flexibility and power of this ionisation method and geometry. Here we report the build of a new AP-TM-MALDI-MSI ion source with plasma ionisation enhancement. This novel ion source is used to analyse a selection of increasingly complex systems from molecular standards to murine brain tissue sections. Significant enhancement of detected ion intensity is observed in both positive and negative ion mode in all systems, with up to 2000 fold increases observed for a range of tissue endogenous species. The substantial improvements conferred by the plasma enhancement are then employed to demonstrate the acquisition of proof of concept tissue images, with high quality spectra obtained down to 10 × 10 µm pixel size.

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 ) 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 (vh ) as an indicator of connectivity dynamics. We observe significant positive IMCC for h and vh , for multiple bands in the NIZ only, with the strongest effect in the lower icEEG frequencies. Similarly, intra-modal h and vh 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.

Although it has been consistently found that local blood-oxygen-level-dependent (BOLD) changes are better modelled by a combination of the power of multiple EEG frequency bands rather than by the power of a unique band alone, the local electro-haemodynamic coupling function is not yet fully characterised. Electrophysiological studies have revealed that the strength of the coupling between the phase of low- and the amplitude of high- frequency EEG activities (phase–amplitude coupling - PAC) has an important role in brain function in general, and in preparation and execution of movement in particular. Using electrocorticographic (ECoG) and functional magnetic resonance imaging (fMRI) data recorded simultaneously in humans performing a finger-tapping task, we investigated the single-trial relationship between the amplitude of the BOLD signal and the strength of PAC and the power of α, β, and γ bands, at a local level. In line with previous studies, we found a positive correlation for the γ band, and negative correlations for the PACβγ strength, and the α and β bands. More importantly, we found that the PACβγ strength explained variance of the amplitude of the BOLD signal that was not explained by a combination of the α, β, and γ band powers. Our main finding sheds further light on the distinct nature of PAC as a functionally relevant mechanism and suggests that the sensitivity of EEG-informed fMRI studies may increase by including the PAC strength in the BOLD signal model, in addition to the power of the low- and high- frequency EEG bands.

HighlightsWe 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.

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.