The neural bases of event monitoring across domains: a simultaneous ERP-fMRI study.

The ability to check and evaluate the environment over time with the aim to detect the occurrence of target stimuli is supported by sustained/tonic as well as transient/phasic control processes, which overall might be referred to as event monitoring. The neural underpinning of sustained control processes involves a fronto-parietal network. However, it has not been well-defined yet whether this cortical circuit acts irrespective of the specific material to be monitored and whether this mediates sustained as well as transient monitoring processes. In the current study, the functional activity of brain during an event monitoring task was investigated and compared between two cognitive domains, whose processing is mediated by differently lateralized areas. Namely, participants were asked to monitor sequences of either faces (supported by right-hemisphere regions) or tools (left-hemisphere). In order to disentangle sustained from transient components of monitoring, a simultaneous EEG-fMRI technique was adopted within a block design. When contrasting monitoring versus control blocks, the conventional fMRI analysis revealed the sustained involvement of bilateral fronto-parietal regions, in both task domains. Event-related potentials (ERPs) showed a more positive amplitude over frontal sites in monitoring compared to control blocks, providing evidence of a transient monitoring component. The joint ERP-fMRI analysis showed that, in the case of face monitoring, these transient processes rely on right-lateralized areas, including the inferior parietal lobule and the middle frontal gyrus. In the case of tools, no fronto-parietal areas correlated with the transient ERP activity, suggesting that in this domain phasic monitoring processes were masked by tonic ones. Overall, the present findings highlight the role of bilateral fronto-parietal regions in sustained monitoring, independently of the specific task requirements, and suggest that right-lateralized areas subtend transient monitoring processes, at least in some task contexts

Getting the best outcomes from epilepsy surgery.

Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG–fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG–fMRI data in which abnormalities are not apparent on scalp EEG

Data-driven analysis of simultaneous EEG/fMRI using an ICA approach

Due to its millisecond-scale temporal resolution, EEG allows to assess neural correlates with precisely defined temporal relationship relative to a given event. This knowledge is generally lacking in data from functional magnetic resonance imaging (fMRI) which has a temporal resolution on the scale of seconds so that possibilities to combine the two modalities are sought. Previous applications combining event-related potentials (ERPs) with simultaneous fMRI BOLD generally aimed at measuring known ERP components in single trials and correlate the resulting time series with the fMRI BOLD signal. While it is a valuable first step, this procedure cannot guarantee that variability of the chosen ERP component is specific for the targeted neurophysiological process on the group and single subject level. Here we introduce a newly developed data-driven analysis procedure that automatically selects task-specific electrophysiological independent components (ICs). We used single-trial simultaneous EEG/fMRI analysis of a visual Go/Nogo task to assess inhibition-related EEG components, their trial-to-trial amplitude variability, and the relationship between this variability and the fMRI. Single-trial EEG/fMRI analysis within a subgroup of 22 participants revealed positive correlations of fMRI BOLD signal with EEG-derived regressors in fronto-striatal regions which were more pronounced in an early compared to a late phase of task execution. In sum, selecting Nogo-related ICs in an automated, single subject procedure reveals fMRI-BOLD responses correlated to different phases of task execution. Furthermore, to illustrate utility and generalizability of the method beyond detecting the presence or absence of reliable inhibitory components in the EEG, we show that the IC selection can be extended to other events in the same dataset, e.g., the visual responses

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Electrocorticographic Activation Patterns of Electroencephalographic Microstates.

Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates

Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and Independent Component Analysis

Copyright © 2011 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Clinical Neurophysiology . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Clinical Neurophysiology, 2011 Vol. 122 Issue 2, pp. 267-77 DOI: http://dx.doi.org/10.1016/j.clinph.2010.06.033Objective The present study examined the benefit of rapid alternation of EEG and fMRI (a common strategy for avoiding artifact caused by rapid switching of MRI gradients) for detecting experimental modulations of ERPs in combined EEG–fMRI. The study also assessed the advantages of aiding the extraction of specific ERP components by means of signal decomposition using Independent Component Analysis (ICA). Methods ‘Go–nogo’ task stimuli were presented either during fMRI scanning or in the gaps between fMRI scans, resulting in ‘gradient’ and ‘no-gradient’ ERPs. ‘Go–nogo’ differences in the N2 and P3 components were subjected to conventional ERP analysis, as well as single-trial and reliability analyses. Results Comparable N2 and P3 enhancement on ‘nogo’ trials was found in the ‘gradient’ and ‘no-gradient’ ERPs. ICA-based signal decomposition resulted in better validity (as indicated by topography), greater stability and lower measurement error of the predicted ERP effects. Conclusions While there was little or no benefit of acquiring ERPs in the gaps between fMRI scans, ICA decomposition did improve the detection of experimental ERP modulations. Significance Simultaneous and continuous EEG–fMRI acquisition is preferable to interleaved protocols. ICA-based decomposition is useful not only for artifact cancellation, but also for the extraction of specific ERP components

Bayesian multi-modal model comparison: a case study on the generators of the spike and the wave in generalized spike–wave complexes

We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike–wave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporal–parietal regions and the most likely model associated with the wave component to comprise the same temporal–parietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges