Simultaneous EEG-fMRI Recordings Research Articles

Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration.

Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean ) and variance dFC (dFCvar ). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean ) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar ).

Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: A 7T study

Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Simultaneous EEG-fMRI was performed at 7T on healthy participants at rest or under visual stimulation, with concurrent recordings of breathing and cardiac activity. PA variability was found to contribute to EEG variance with more than 500 μV2 at 7T, which extrapolates to 92 μV2 at 3T. Clustering analyses revealed that PA variability not only is linked to variations in head position/orientation, as previously hypothesized, but also, and more importantly, to the respiratory cycle and to heart rate fluctuations. The latter mechanisms are associated to short-timescale variability (even across consecutive heartbeats), and their importance varied across EEG channels. In light of this PA variability, three PA correction techniques were compared: average artifact subtraction (AAS), optimal basis sets (OBS), and an approach based on K-means clustering. All methods allowed the recovery of visual evoked potentials from the EEG data; nonetheless, OBS and K-means tended to outperform AAS, likely due to the inability of the latter in modeling short-timescale variability. Altogether, these results offer novel insights into the dynamics and underlying mechanisms of the pulse artifact, with important consequences for its correction, relevant to most EEG-fMRI applications.

Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation.

Slow-wave sleep is an optimal opportunity for memory consolidation: when encoding occurs in the presence of a sensory cue, delivery of that cue during sleep enhances retrieval of associated memories. Recent studies suggest that cues might promote consolidation by inducing neural reinstatement of cue-associated content during sleep, but direct evidence for such mechanisms is scant, and the relevant brain areas supporting these processes are poorly understood. Here, we address these gaps by combining a novel olfactory cueing paradigm with an object-location memory task and simultaneous EEG-fMRI recording in human subjects. Using pattern analysis of fMRI ensemble activity, we find that presentation of odor cues during sleep promotes reactivation of category-level information in ventromedial prefrontal cortex that significantly correlates with post-sleep memory performance. In identifying the potential mechanisms by which odor cues selectively modulate memory in the sleeping brain, these findings bring unique insights into elucidating how and what we remember.

Conductive gel bridge sensor for motion tracking in simultaneous EEG-fMRI recordings

EEG-fMRI allows the localization of the hemodynamic correlates of neural activity and has been shown to be useful as a diagnostic tool in pre-surgical evaluation of refractory epilepsy. However, EEG recordings may be highly contaminated by artifacts induced by movements inside the magnetic field thus rendering the scan difficult for interpretation. Existing methods for motion correction require additional equipment or hardware modification. We introduce a simple method for motion artifact detection, the conductive gel bridge sensor (CGBS), easily applicable using the standard setup. We report examples of CGBS use in two patients with epilepsy and demonstrate the method’s ability to successfully differentiate between epochs of brain activity and those of movement.

Abnormal dynamics of functional connectivity density in children with benign epilepsy with centrotemporal spikes.

Converging evidence has shown the link between benign epilepsy with centrotemporal spikes (BECTS) and abnormal functional connectivity among distant brain regions. However, prior research in BECTS has not examined the dynamic changes in functional connectivity as networks form. We combined functional connectivity density (FCD) mapping and sliding windows correlation analyses, to fully capture the functional dynamics in patients with respect to the presence of interictal epileptic discharges (IEDs). Resting-state fMRI was performed in 43 BECTS patients and 28 healthy controls (HC). Patients were further classified into two subgroups, namely, IED (n = 20) and non-IED (n = 23) depending on the simultaneous EEG-fMRI recordings. The global dynamic FCD (dFCD) was measured using sliding window correlation. Then we quantified dFCD variability using their standard deviation. Compared with HC, patients with and without IEDs both showed invariable dFCD (decreased) among the orbital fontal cortex, anterior cingulate cortex and striatum, as well as variable dFCD (increased) in the posterior default mode network (P < 0.05, AlphaSim corrected). Correlation analysis indicated that the variable dFCD in precuneus was related to seizure onset age (P < 0.05, uncorrected). BECTS with IEDs showed variable dFCD in regions related to the typical seizure semiology. The abnormal patterns of fluctuating FCD in BECTS suggest that both active and chronic epileptic state may contribute to altered dynamics of functional connectivity associated with cognitive disturbances and developmental alterations. These findings highlight the importance of considering fluctuating dynamic neural communication among brain systems to deepen our understanding of epilepsy diseases.

Fear Extinction Recall Modulates Human Frontomedial Theta and Amygdala Activity.

Human functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) studies, as well as animal studies, indicate that the amygdala and frontomedial brain regions are critically involved in conditioned fear and that frontomedial oscillations in the theta range (4-8 Hz) may support communication between these brain regions. However, few studies have used a multimodal approach to probe interactions among these key regions in humans. Here, our goal was to bridge the gap between prior human fMRI, EEG, and animal findings. Using simultaneous EEG-fMRI recordings 24 h after fear conditioning and extinction, conditioned stimuli presented (CS+E, CS-E) and not presented during extinction (CS+N, CS-N) were compared to identify effects specific to extinction versus fear recall. Differential (CS+ vs. CS-) electrodermal, frontomedial theta (EEG) and amygdala responses (fMRI) were reduced for extinguished versus nonextinguished stimuli. Importantly, effects on theta power covaried with effects on amygdala activation. Fear and extinction recall as indicated by theta explained 60% of the variance for the analogous effect in the right amygdala. Our findings show for the first time the interplay of amygdala and frontomedial theta activity during fear and extinction recall in humans and provide insight into neural circuits consistently linked with top-down amygdala modulation in rodents.

Multi layer spectral decomposition technique for ERD estimation in EEG μ rhythms: An EEG–fMRI study

Abstract In this paper, power suppression detection in mu band known as event-related desynchronization during a simultaneous EEG–fMRI recording is addressed. Simultaneous EEG-fMRI experiments provide great opportunities to recognize relation between the active areas in the brain, discovered by fMRI, and EEG events. However, decoding EEG is challenging as a result of artifacts which MRI scanner applies on EEG. Ballistocardiogram (BCG) is one of the main destructive artifacts which deteriorates EEG rhythms specially in mu band. The proposed method, is a supervised time-frequency decomposition algorithm which estimates underlying rhythms of EEG signals recorded in MRI scanner. In the proposed method, a multi layer decomposition approach using three criteria is developed to extract brain oscillations. This goal is achieved by eliminating the artifact related components at each decomposition layer as a result of removing useless extracted components in each layer. Performance of the proposed method is evaluated using synthetic and real EEG. The achieved results from synthetic data confirm the ability of the proposed method to extract mu rhythms when different levels of noise exist. In addition, proposed algorithm is applied on a set of real EEG acquired in a simultaneous EEG–fMRI recording. Estimated ERD confirms the superiority of the developed algorithm in terms of estimating changes of mu rhythms. Moreover, an EEG–fMRI integration is performed to explore the correlation between blood oxygenation level dependent (BOLD) in fMRI and ERD of mu rhythm in EEG.

P 24 Electrophysiological connectivity during oscillatory cycles of alpha activity and the BOLD-signal correlate in major depression

Introduction Major depressive disorder (MDD) belongs to the leading causes of disability worldwide. From an electrophysiological point of view, EEG-alpha activity and alpha phase synchronization seem to be possible biomarkers with diagnostic and prognostic value. In parallel, functional magnetic resonance imaging (fMRI) studies suggest altered blood oxygen-level dependent (BOLD) signals in e.g. frontal and prefrontal brain areas ( Lemogne et al., 2012 ). However, the association between electrophysiological alterations and differences of fMRI activity in MDD remains vague. The goal of this study was to analyze the linkage between physiologically synchronized neuronal activity and hemodynamic changes as assessed by simultaneous EEG-fMRI recordings and its alterations in MDD. Method Therefore, simultaneous EEG-fMRI 12 min resting state measurements of 22 unmedicated MDD-patients and 15 healthy controls were conducted. EEG phase synchronization as markers for cortical connectivity between two scalp regions were calculated at the electrode level. Time series of the phase synchronization were averaged according to the repetition time (2400 ms) and used as regressors for the BOLD signal analysis. Results While interhemispheric activity e.g. between electrodes F3 and F4 showed increased BOLD signals in associated areas at both sides in healthy subjects, intrahemispheric connectivity, e.g. between C3 and O1 was associated with heterogeneous patterns with mainly frontal increased and occipitally decreased BOLD signals. Concerning the EEG phase synchronization associated BOLD signals, a significant difference between patients suffering from MDD and healthy subjects was found within the subgenual gyrus for a variety of intra- and interhemispheric EEG-connections. In particular, in patients, the subgenual gyrus seems to be more involved in the regulation of EEG-alpha synchronization in comparison to healthy controls. Conclusion EEG-alpha phase synchronization is associated with marked BOLD signal changes. The regulation of EEG-alpha phase synchronization seems to be altered in MDD with the subgenual gyrus as a key hub.

Epileptic Discharge Related Functional Connectivity Within and Between Networks in Benign Epilepsy with Centrotemporal Spikes.

Benign epilepsy with centrotemporal spikes (BECTS) is a common childhood epilepsy syndrome associated with abnormalities in neurocognitive domains, particularly during interictal epileptiform discharges (IEDs). Here, we investigated the effects of IEDs on brain's intrinsic connectivity networks in 43 BECTS patients and 28 matched healthy controls (HCs). Patients were further divided into IED and non-IED subgroups based on simultaneous EEG-fMRI recordings. Functional connectivity within and between five networks, corresponding to seizure origination and cognitive processes, were analyzed to measure IED effects. We found that patients exhibited increased connectivity within the auditory network (AN) and the somato-motor network (SMN), and decreased connectivity within the basal ganglia network and the dorsal attention network, suggesting that both transient and chronic seizure activity may disturb normal network organization. The IED group showed decreased functional connectivity within the default mode network (DMN) compared with the non-IED group and HCs, implying that the DMN was selectively impaired during epileptiform discharges associated with altered self-referential cognitive functions. Moreover, the IED group exhibited increased positive correlations between the AN and the SMN, which suggests a possible excessive influence of centrotemporal spiking on information processing in the auditory system. The association between epileptic activity and network dysfunctions highlights their importance in investigating the pathological mechanism underlying BECTS.

Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy: A wavelet analysis approach.

Simultaneous scalp EEG-fMRI recording is a noninvasive neuroimaging technique for combining electrophysiological and hemodynamic aspects of brain function. Despite the time-varying nature of both measurements, their relationship is usually considered as time-invariant. The aim of this study was to detect direct associations between scalp-recorded EEG and regional changes of hemodynamic brain connectivity in focal epilepsy through a time-frequency paradigm. To do so, we developed a voxel-wise framework that analyses wavelet coherence between dynamic regional phase synchrony (DRePS, calculated from fMRI) and band amplitude fluctuation (BAF) of a target EEG electrode with dominant interictal epileptiform discharges (IEDs). As a proof of concept, we applied this framework to seven patients with focal epilepsy. The analysis produced patient-specific spatial maps of DRePS-BAF coupling, which highlight regions with a strong link between EEG power and local fMRI connectivity. Although we observed DRePS-BAF coupling proximate to the suspected seizure onset zone in some patients, our results suggest that DRePS-BAF is more likely to identify wider 'epileptic networks'. We also compared DRePS-BAF with standard EEG-fMRI analysis based on general linear modelling (GLM). There was, in general, little overlap between the DRePS-BAF maps and GLM maps. However, in some subjects the spatial clusters revealed by these two analyses appeared to be adjacent, particularly in medial posterior cortices. Our findings suggest that (1) there is a strong time-varying relationship between local fMRI connectivity and interictal EEG power in focal epilepsy, and (2) that DRePS-BAF reflect different aspects of epileptic network activity than standard EEG-fMRI analysis. These two techniques, therefore, appear to be complementary. Hum Brain Mapp 38:5356-5374, 2017. © 2017 Wiley Periodicals, Inc.

Cortical and Subcortical Coordination of Visual Spatial Attention Revealed by Simultaneous EEG-fMRI Recording.

Visual spatial attention has been studied in humans with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) individually. However, due to the intrinsic limitations of each of these methods used alone, our understanding of the systems-level mechanisms underlying attentional control remains limited. Here, we examined trial-to-trial covariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in humans, which allowed delineation of both the generators and modulators of the cue-triggered event-related oscillatory brain activity underlying attentional control function. The fMRI activity in visual cortical regions contralateral to the cued direction of attention covaried positively with occipital gamma-band EEG, consistent with activation of cortical regions representing attended locations in space. In contrast, fMRI activity in ipsilateral visual cortical regions covaried inversely with occipital alpha-band oscillations, consistent with attention-related suppression of the irrelevant hemispace. Moreover, the pulvinar nucleus of the thalamus covaried with both of these spatially specific, attention-related, oscillatory EEG modulations. Because the pulvinar's neuroanatomical geometry makes it unlikely to be a direct generator of the scalp-recorded EEG, these covariational patterns appear to reflect the pulvinar's role as a regulatory control structure, sending spatially specific signals to modulate visual cortex excitability proactively. Together, these combined EEG/fMRI results illuminate the dynamically interacting cortical and subcortical processes underlying spatial attention, providing important insight not realizable using either method alone.SIGNIFICANCE STATEMENT Noninvasive recordings of changes in the brain's blood flow using functional magnetic resonance imaging and electrical activity using electroencephalography in humans have individually shown that shifting attention to a location in space produces spatially specific changes in visual cortex activity in anticipation of a stimulus. The mechanisms controlling these attention-related modulations of sensory cortex, however, are poorly understood. Here, we recorded these two complementary measures of brain activity simultaneously and examined their trial-to-trial covariations to gain insight into these attentional control mechanisms. This multi-methodological approach revealed the attention-related coordination of visual cortex modulation by the subcortical pulvinar nucleus of the thalamus while also disentangling the mechanisms underlying the attentional enhancement of relevant stimulus input and those underlying the concurrent suppression of irrelevant input.

Neural Activity Elicited by a Cognitive Task can be Detected in Single-Trials with Simultaneous Intracerebral EEG-fMRI Recordings

Recent studies have shown that it is feasible to record simultaneously intracerebral EEG (icEEG) and functional magnetic resonance imaging (fMRI) in patients with epilepsy. While it has mainly been used to explore the hemodynamic changes associated with epileptic spikes, this approach could also provide new insight into human cognition. However, the first step is to ensure that cognitive EEG components, that have lower amplitudes than epileptic spikes, can be appropriately detected under fMRI. We compared the high frequency activities (HFA, 50-150[Formula: see text]Hz) elicited by a reading task in icEEG-only and subsequent icEEG-fMRI in the same patients ([Formula: see text]), implanted with depth electrodes. Comparable responses were obtained, with 71% of the recording sites that responded during the icEEG-only session also responding during the icEEG-fMRI session. For all the remaining sites, nearby clusters (distant of 7[Formula: see text]mm or less) also demonstrated significant HFA increase during the icEEG-fMRI session. Significant HFA increases were also observable at the single-trial level in icEEG-fMRI recordings. Our results show that low-amplitude icEEG signal components such as cognitive-induced HFAs can be reliably recorded with simultaneous fMRI. This paves the way for the use of icEEG-fMRI to address various fundamental and clinical issues, notably the identification of the neural correlates of the BOLD signal.

Physiological noise correction using ECG-derived respiratory signals for enhanced mapping of spontaneous neuronal activity with simultaneous EEG-fMRI

The study of spontaneous brain activity based on BOLD-fMRI may be seriously compromised by the presence of signal fluctuations of non-neuronal origin, most prominently due to cardiac and respiratory mechanisms. Methods used for modeling and correction of the so-called physiological noise usually rely on the concurrent measurement of cardiac and respiratory signals. In simultaneous EEG-fMRI recordings, which are primarily aimed at the study of spontaneous brain activity, the electrocardiogram (ECG) is typically measured as part of the EEG setup but respiratory data are not generally available. Here, we propose to use the ECG-derived respiratory (EDR) signal estimated by Empirical Mode Decomposition (EMD) as a surrogate of the respiratory signal, for retrospective physiological noise correction of typical simultaneous EEG-fMRI data. A physiological noise model based on these physiological signals (P-PNM) complemented with fMRI-derived noise regressors was generated, and evaluated, for 17 simultaneous EEG-fMRI datasets acquired from a group of seven epilepsy patients imaged at 3T. The respiratory components of P-PNM were found to explain BOLD variance significantly in addition to the cardiac components, suggesting that the EDR signal was successfully extracted from the ECG, and P-PNM outperformed an image-based model (I-PNM) in terms of total BOLD variance explained. Further, the impact of the correction using P-PNM on fMRI mapping of patient-specific epileptic networks and the resting-state default mode network (DMN) was assessed in terms of sensitivity and specificity and, when compared with an ICA-based procedure and a standard pre-processing pipeline, P-PNM achieved the best performance. Overall, our results support the feasibility and utility of extracting physiological noise models of the BOLD signal resorting to ECG data exclusively, with substantial impact on the simultaneous EEG-fMRI mapping of resting-state networks, and, most importantly, epileptic networks where sensitivity and specificity are still limited.

Using a temporal decomposition technique to investigate dynamic changes in subnetworks derived from resting state fMRI during generalized spike-and-wave discharges: an EEG-fMRI pilot study

Event Abstract Back to Event Using a temporal decomposition technique to investigate dynamic changes in subnetworks derived from resting state fMRI during generalized spike-and-wave discharges: an EEG-fMRI pilot study Chayanin Tangwiriyasakul1*, Suejen Perani1, Siti N. Yaakub1 and Mark P. Richardson1 1 King's College London, Basic and Clinical Neuroscience, United Kingdom Introduction: Many recent studies suggest that generalized seizures arise from abnormality in whole brain networks (Richardson et al. 2012). Most evidence shows abnormalities in static networks (e.g. reduced default mode network connectivity). In this study, we are interested to investigate dynamic changes in the level of synchrony during generalized spike and wave discharge (GSW) period using simultaneous EEG-fMRI recordings. Methods: Three patients with newly diagnosed epilepsy underwent four resting state scans (each scan lasts ~10 minutes). Pre-processed fMRI data were band-pass filtered between 0.04-0.07 Hz (Glerean et al. 2011) and a Hilbert transform was applied. The AAL atlas was used to divide the brain into 90 cortical regions, with each region represented by the first principal component of the instantaneous phase of all voxels in the region. We estimated a binary synchronization matrix at each time-point with regions considered to be synchronized if the phase difference was less than π/6, giving 296 binary synchronization matrices for each subject (Ponce-Alvarez et al. 2014). Tensor decomposition technique was then used to extract sub-networks, resulting in 9 major components (90x90 matrices). GSW periods were marked by an experienced neurologist. Results: A total of 32 GSW occurrences were found in this study. Figure 1 shows an example of temporal evolution of nine subnetworks in patient-1. From visual inspection (see Figure 1B/1C), 20 out of 32 SWD occurrences (63%) were found during a minimum of network synchrony (or turning point, labelled as T in Fig 1B/C). One the other hand, 9 events (27%) were found while the net contribution from all subnetworks was in a maximum stable-and-synchronous state (or plateau state, labelled as P in Fig 1B/C). The remaining 3 GSW occurrences (10%) were unclassified. Figure 1: Subnetwork structure of spatiotemporal synchronization patterns in patient-1. A) The 9 detected subnetworks. B) Temporal activation strength of each subnetwork. C) Total activation strength of all nine subnetworks (which represents a level of total network synchrony). D) Temporal evolution of phase difference throughout the ~10 minute scan. E) Band-pass filtered fMRI raw signals in 25 brain regions. The red stripes indicate the period during GSW. Note that T = turning and P = Plateau. Conclusion: Up to now, the mechanism underlying seizure generation is not yet clear. The results from this study may suggest two possible causes of SWD generations: (1) transition between two states, which may cause instability in the brain networks or (2) over synchronization of the brain networks. In the future, a larger study population is needed to address these two hypotheses. Figure 1

Joint source separation of simultaneous EEG-fMRI recording in two experimental conditions using common spatial patterns.

Simultaneous collection of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has become increasingly popular in neuroscientific studies, because it can provide neural information with both high spatial and temporal resolution. In order to maximally utilize the information contained in simultaneous EEG-fMRI recording, many sophisticated multimodal data-mining methods, such as joint ICA, have been developed. However, these methods normally deal with data recorded in one experimental condition, and they cannot effectively extract information on activities that are distinct in two conditions. In this paper, a new data decomposition method called joint common spatial pattern (jCSP) is proposed. Compared with previous methods, the jCSP method exploits inter-conditional difference in the strength of brain source activities to achieve source separation, and is able to uncover the source activities with the strongest discriminative power. A group analysis based on clustering is further proposed to reveal distinctive jCSP patterns at group level. We applied joint CSP to a simultaneous EEG-fMRI dataset collected from 21 subjects under two different resting-state conditions (eyes-closed and eyes-open). Results show a distinct dynamic pattern shared by EEG alpha power and fMRI signal during eyes-open resting-state.

EEG–fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG

In this paper, artifact removal from biomedical signals is addressed. We particularly focus on removing ballistocardiogram (BCG) artifact from EEG. BCG mainly appears in EEG signals during simultaneous EEG–fMRI recordings. Different from most existing artifact removal techniques, we propose a method based on dictionary learning framework. Due to strength of sparsifying dictionaries in applications such as image denoising, it is expected to succeed in BCG removal task as well. This is investigated in the proposed approach where a dictionary is learned from original EEG recording. The dictionary is designed to locally model BCG characteristics. After achieving the dictionary, BCG can be simply subtracted from the original signal and the clean EEG is obtained. Our experimental results on both synthetic and real data confirm the effectiveness of the proposed method. The results reveal the flexibility of learned dictionary for modeling the fluctuations in artifact, and removing it from original EEG signals.

Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI

Functional magnetic resonance imaging (fMRI), which has high spatial resolution, is increasingly used to evaluate cerebral functions in neurological and psychiatric diseases. The main limitation of fMRI is that it detects neural activity indirectly, through the associated slow hemodynamic variations. Because neurovascular coupling can be regionally altered by pathological conditions or drugs, fMRI responses may not truly reflect neural activity. Electroencephalography (EEG) recordings, which directly detect neural activity with optimal temporal resolution, can now be obtained during fMRI data acquisition. Therefore, there is a growing interest in combining the techniques to obtain simultaneous EEG-fMRI recordings. The EEG-fMRI approach has several promising clinical applications. The first is the detection of cortical areas involved in interictal and ictal epileptic activity. Second, combining evoked potentials with fMRI could be an accurate way to study eloquent cortical areas for the planning of neurosurgery or rehabilitation, circumventing the above-mentioned limitation of fMRI. Finally, the use of this approach to evaluate the functional connectivity of resting-state networks would extend the applications of EEG-fMRI to uncooperative or unconscious patients. Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI.

The cerebral cost of breathing: an FMRI case-study in congenital central hypoventilation syndrome.

Certain motor activities - like walking or breathing - present the interesting property of proceeding either automatically or under voluntary control. In the case of breathing, brainstem structures located in the medulla are in charge of the automatic mode, whereas cortico-subcortical brain networks - including various frontal lobe areas - subtend the voluntary mode. We speculated that the involvement of cortical activity during voluntary breathing could impact both on the “resting state” pattern of cortical-subcortical connectivity, and on the recruitment of executive functions mediated by the frontal lobe. In order to test this prediction we explored a patient suffering from central congenital hypoventilation syndrome (CCHS), a very rare developmental condition secondary to brainstem dysfunction. Typically, CCHS patients demonstrate efficient cortically-controlled breathing while awake, but require mechanically-assisted ventilation during sleep to overcome the inability of brainstem structures to mediate automatic breathing. We used simultaneous EEG-fMRI recordings to compare patterns of brain activity between these two types of ventilation during wakefulness. As compared with spontaneous breathing (SB), mechanical ventilation (MV) restored the default mode network (DMN) associated with self-consciousness, mind-wandering, creativity and introspection in healthy subjects. SB on the other hand resulted in a specific increase of functional connectivity between brainstem and frontal lobe. Behaviorally, the patient was more efficient in cognitive tasks requiring executive control during MV than during SB, in agreement with her subjective reports in everyday life. Taken together our results provide insight into the cognitive and neural costs of spontaneous breathing in one CCHS patient, and suggest that MV during waking periods may free up frontal lobe resources, and make them available for cognitive recruitment. More generally, this study reveals how the active maintenance of cortical control over a continuous motor activity impacts on brain functioning and cognition.

Coherent Activity in Bilateral Parieto-Occipital Cortices during P300-BCI Operation.

The visual P300 brain–computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG–fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas. Here, to capture coherent activities of the areas during P300-BCI, we collected whole-head 306-channel magnetoencephalography data. When comparing functional connectivity between the right and left parieto-occipital channels, significantly greater functional connectivity in the alpha band was observed under the GB flicker matrix condition than under the WG flicker matrix condition. Current sources were estimated with a narrow-band adaptive spatial filter, and mean imaginary coherence was computed in the alpha band. Significantly greater coherence was observed in the right posterior parietal cortex under the GB than under the WG condition. Re-analysis of previous EEG-based P300-BCI data showed significant correlations between the power of the coherence of the bilateral parieto-occipital cortices and their performance accuracy. These results suggest that coherent activity in the bilateral parieto-occipital cortices plays a significant role in effectively driving the P300-BCI.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG–fMRI recordings

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the “irritative zone” (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram–functional Magnetic Resonance Imagining (EEG–fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG–fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG–fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.