Concurrent EEG-fMRI Research Articles

Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data

We consider the problem of removing gradient artifact from electroencephalogram (EEG) signal, recorded concurrently with functional magnetic resonance imaging (fMRI) acquisition. We estimate the artifact by exploiting its quasi-periodicity over the epochs and its similarity over the different channels by using independent vector analysis, a recent extension of independent component analysis for multiple datasets. The method fully makes use of the spatio-temporal information by using spatial dependences across channels to estimate the artifact for a particular channel. Thus, it provides robustness with respect to uncontrollable changes such as head movement and fluctuations in the B0 field during the acquisition. Results using both simulated data with gradient artifact and EEG data collected concurrently with fMRI show the desirable performance of the new method.

The superior temporal sulcus and the N170 during face processing: Single trial analysis of concurrent EEG–fMRI

Face-selective neural signals have been reliably identified using both EEG and fMRI studies. These consist of the N170 component, a neural response peaking approximately 170ms after a face is presented, and face-selective activations in the fusiform face area (FFA), the occipital face area (OFA), and the superior temporal sulcus (STS). As most neuroimaging studies examine these face-selective processes separately, the relationship between the N170 neural response and activation in the fusiform gyrus is still debated. In this study, we concurrently measured EEG and fMRI responses to upright faces, inverted faces, and objects to examine this association. We introduce a method for single-trial estimation of N170 amplitudes and correlation of the trial-by-trial variation in N170 neural responses with fMRI BOLD responses. For upright faces, BOLD responses in the right STS were negatively correlated with N170 amplitudes, showing greater activation on trials in which N170 amplitudes were larger (more negative). For inverted faces, a medial region of the fusiform gyrus (mFG) was positively correlated with N170 amplitudes, showing greater activation on trials in which N170 amplitudes were smaller (less negative). This result points to the STS as a crucial region for generating the N170 associated with face perception, and suggests that the mFG is additionally recruited for processing inverted faces, particularly on trials in which N170 is small. Despite the different time resolution of fMRI and EEG signals, our single-trial estimation and EEG–fMRI correlation method can reveal associations between activation in face-selective brain regions and neural processes at 170ms associated with face perception.

On the feasibility of concurrent human TMS-EEG-fMRI measurements

Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware.

The spatiospectral characterization of brain networks: Fusing concurrent EEG spectra and fMRI maps

Different imaging modalities capture different aspects of brain activity. Functional magnetic resonance imaging (fMRI) reveals intrinsic networks whose BOLD signals have periods from 100 s (0.01 Hz) to about 10s (0.1 Hz). Electroencephalographic (EEG) recordings, in contrast, commonly reflect cortical electrical fluctuations with periods up to 20 ms (50 Hz) or above. We examined the correspondence between intrinsic fMRI and EEG network activity at rest in order to characterize brain networks both spatially (with fMRI) and spectrally (with EEG). Brain networks were separately identified within the concurrently recorded fMRI and EEG at the aggregate group level with group independent component analysis and the association between spatial fMRI and frequency by spatial EEG sources was examined by deconvolving their component time courses. The two modalities are considered linked if the estimated impulse response function (IRF) is significantly non-zero at biologically plausible delays. We found that negative associations were primarily present within two of five alpha components, which highlights the importance of considering multiple alpha sources in EEG-fMRI. Positive associations were primarily present within the lower (e.g. delta and theta) and higher (e.g. upper beta and lower gamma) spectral regions, sometimes within the same fMRI components. Collectively, the results demonstrate a promising approach to characterize brain networks spatially and spectrally, and reveal that positive and negative associations appear within partially distinct regions of the EEG spectrum.

Concurrent EEG-fMRI assessments: Exploiting the best of two worlds or basic and clinical neuroscience

Concurrent EEG-fMRI assessments: Exploiting the best of two worlds or basic and clinical neuroscience

Fluctuating concepts of childhood absence epilepsy

Childhood absence epilepsy (CAE) appears not to be benign. Generations of pediatric neurologists have considered this form of childhood epilepsy to be less severe, since intellectual function is usually not severely affected. Over the past year, however, this common childhood epilepsy has received increasing attention, with results suggesting a more complex picture. The complicated relationship of seizures and attentional impairment in CAE is just one example of the broader behavioral, cognitive, and linguistic difficulties in children with CAE. In this issue of Neurology ®, Bai et al.1 investigate functional network activity as a surrogate for executive functions. They demonstrate interictal abnormalities in resting state blood oxygenation level–dependent (BOLD) functional connectivity (fc-MRI) datasets from children with CAE, compared to typically developing children. Strikingly, the difference is one of increased connectivity between homotopic areas of lateral prefrontal cortex. They show, with concurrent EEG-fMRI in the most rigorous manner possible, that the often frequent absence seizures themselves did not overtly influence the finding of increased connectivity. A finding of increased resting state functional connectivity is relatively rare in the rapidly expanding literature describing resting state fc-MRI in neurologic disease. Most studies across neurologic disease describe decreased functional connectivity, which is …

P17.19 Hand motor source extracted from concurrent EEG fMRI data by functional source separation

P17.19 Hand motor source extracted from concurrent EEG fMRI data by functional source separation

EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

P19-11 Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI

P19-11 Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI

Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: A concurrent EEG-fMRI study

Concurrent EEG-fMRI studies have provided increasing details of the dynamics of intrinsic brain activity during the resting state. Here, we investigate a prominent effect in EEG during relaxed resting, i.e. the increase of the alpha power when the eyes are closed compared to when the eyes are open. This phenomenon is related to changes in thalamo-cortical and cortico-cortical synchronization. In order to investigate possible changes to EEG-fMRI coupling and fMRI functional connectivity during the two states we adopted a data-driven approach that fuses the multimodal data on the basis of parallel ICA decompositions of the fMRI data in the spatial domain and of the EEG data in the spectral domain. The power variation of a posterior alpha component was used as a reference function to deconvolve the hemodynamic responses from occipital, frontal, temporal, and subcortical fMRI components. Additionally, we computed the functional connectivity between these components. The results showed widespread alpha hemodynamic responses and high functional connectivity during eyes-closed (EC) rest, while eyes open (EO) resting abolished many of the hemodynamic responses and markedly decreased functional connectivity. These data suggest that generation of local hemodynamic responses is highly sensitive to state changes that do not involve changes of mental effort or awareness. They also indicate the localized power differences in posterior alpha between EO and EC in resting state data are accompanied by spatially widespread amplitude changes in hemodynamic responses and inter-regional functional connectivity, i.e. low frequency hemodynamic signals display an equivalent of alpha reactivity.

Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI

EEG quality is a crucial issue when acquiring combined EEG-fMRI data, particularly when the focus is on using single trial (ST) variability to integrate the data sets. The most common method for improving EEG data quality following removal of gross MRI artefacts is independent component analysis (ICA), a completely blind source separation technique. In the current study, a different approach is proposed based on the functional source separation (FSS) algorithm. FSS is an extension of ICA that incorporates prior knowledge about the signal of interest into the data decomposition. Since in general the part of the EEG signal that will contain the most relevant information is known beforehand (i.e. evoked potential peaks, spectral bands), FSS separates the signal of interest by exploiting this prior knowledge without renouncing the advantages of using only information contained in the original signal waveforms.A reversing checkerboard stimulus was used to generate visual evoked potentials (VEPs) in healthy control subjects. Gradient and ballistocardiogram artefacts were removed with template subtraction techniques to form the raw data, which were then subjected to ICA denoising and FSS. The resulting EEG data sets were compared using several metrics derived from average and ST data and correlated with fMRI data. In all cases, ICA was an improvement on the raw data, but the most obvious improvement was provided by FSS, which consistently outperformed ICA. The results show the benefit of FSS for the recovery of good quality single trial evoked potentials during concurrent EEG-fMRI recordings.

Individual alpha-frequency correlates with amplitude of visual evoked potential and hemodynamic response

In a simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) study, the predictive value of the individual alpha-frequency at rest (IAF) for the amplitude of neuronal and vascular responses to visual stimulation was investigated. Across subjects, we find (i) an inverse relationship between IAF and the amplitude of the alpha-rhythm at rest. The IAF also predicts (ii) the amplitude of the visual evoked potential (VEP), as well as (iii) the amplitude of the alpha-rhythm during stimulation. Most importantly, (iv) IAF correlates with the oxygenation response to visual stimulation: A high IAF predicts a low alpha-amplitude at rest, a small VEP amplitude and a small oxygenation response. Conversely, a low IAF predicts high alpha-amplitude and larger electrophysiological and vascular responses to stimulation. Based on these findings, we assume that the relationship between IAF and neuronal and vascular response stems from the size of the network recruited for visual processing. The relation between IAF, alpha-amplitude, evoked potential and vascular response is discussed in the framework of a simple heuristic model. The results may partly explain the large intersubject variability observed in recently published concurrent EEG-fMRI studies.

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-by-voxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment.