Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

The relationship between resting-state hemodynamic (fMRI) and electrophysiological (EEG) connectivity has been investigated in healthy subjects, but this relationship is unknown in patients with left and right temporal lobe epilepsies (l/rTLE). Does the magnitude of the relationship differ between healthy subjects and patients? What role does the laterality of the epileptic focus play? What are the spatial contributions to this relationship? Here we use concurrent EEG-fMRI recordings of 65 subjects from two centers (35 controls, 34 TLE patients), to assess the correlation between EEG and fMRI connectivity. For all datasets, frequency-specific changes in cross-modal correlation were seen in lTLE and rTLE. EEG and fMRI connectivities do not measure perfectly overlapping brain networks and provide distinct information on brain networks altered in TLE, highlighting the benefit of multimodal assessment to inform about normal and pathological brain function.

EEG-fMRI in awake rat and whole-brain simulations show decreased brain responsiveness to sensory stimulations during absence seizures.

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

Mesial temporal lobe spiking reveals distinct patterns of blood oxygen level-dependent functional magnetic resonance imaging activation using simultaneous intracranial electroencephalography-functional magnetic resonance imaging.

OBJECTIVE: Temporal lobe epilepsy (TLE) has a high probability of becoming drug resistant and is frequently considered for surgical intervention. However, 30% of TLE cases have nonlesional magnetic resonance imaging (MRI) scans, which is associated with worse surgical outcomes. Characterizing interactions between temporal and extratemporal structures in these patients may help understand these poor outcomes. Simultaneous intracranial electroencephalography-functional MRI (iEEG-fMRI) can measure the hemodynamic changes associated with interictal epileptiform discharges (IEDs) recorded directly from the brain. This study was designed to characterize the whole brain patterns of IED-associated fMRI activation recorded exclusively from the mesial temporal lobes of patients with nonlesional TLE. METHODS: Eighteen patients with nonlesional TLE undergoing iEEG monitoring with mesial temporal IEDs underwent simultaneous iEEG-fMRI at 3 T. IEDs were marked, and statistically significant clusters of fMRI activation were identified. The locations of IED-associated fMRI activation for each patient were determined, and patients were grouped based on the location and pattern of fMRI activation. RESULTS: Two patterns of IED-associated fMRI activation emerged: primarily localized (n = 7), where activation was primarily located within the ipsilateral temporal lobe, and primarily diffuse (n = 11), where widespread bilateral extratemporal activation was detected. The primarily diffuse group reported significantly fewer focal to bilateral tonic-clonic seizures and had better postsurgical outcomes. SIGNIFICANCE: Simultaneous iEEG-fMRI can measure the hemodynamic changes associated with focal IEDs not visible on scalp EEG, such as those arising from the mesial temporal lobe. Significant fMRI activation associated with these IEDs was observed in all patients. Two distinct patterns of IED-associated activation were seen: primarily localized to the ipsilateral temporal lobe and more widespread, bilateral activation. Patients with widespread IED associated-activation had fewer focal to bilateral tonic-clonic seizures and better postsurgical outcome, which may suggest a neuroprotective mechanism limiting the spread of ictal events.

The influence of wakefulness fluctuations on brain networks involved in centrotemporal spike occurrence.

OBJECTIVE: Drowsiness has been implicated in the modulation of centro-temporal spikes (CTS) in Self-limited epilepsy with Centro-Temporal Spikes (SeLECTS). Here, we explore this relationship and whether fluctuations in wakefulness influence the brain networks involved in CTS generation. METHODS: Functional MRI (fMRI) and electroencephalography (EEG) was simultaneously acquired in 25 SeLECTS. A multispectral EEG index quantified drowsiness ('EWI': EEG Wakefulness Index). EEG (Pearson Correlation, Cross Correlation, Trend Estimation, Granger Causality) and fMRI (PPI: psychophysiological interactions) analytic approaches were adopted to explore respectively: (a) the relationship between EWI and changes in CTS frequency and (b) the functional connectivity of the networks involved in CTS generation and wakefulness oscillations. EEG analyses were repeated on a sample of routine EEG from the same patient's cohort. RESULTS: No correlation was found between EWI fluctuations and CTS density during the EEG-fMRI recordings, while they showed an anticorrelated trend when drowsiness was followed by proper sleep in routine EEG traces. According to PPI findings, EWI fluctuations modulate the connectivity between the brain networks engaged by CTS and the left frontal operculum. CONCLUSIONS: While CTS frequency per se seems unrelated to drowsiness, wakefulness oscillations modulate the connectivity between CTS generators and key regions of the language circuitry, a cognitive function often impaired in SeLECTS. SIGNIFICANCE: This work advances our understanding of (a) interaction between CTS occurrence and vigilance fluctuations and (b) possible mechanisms responsible for language disruption in SeLECTS.

Altered dynamic functional connectivity of motor cerebellum with sensorimotor network and default mode network in juvenile myoclonic epilepsy.

Objective: To investigate whether changes occur in the dynamic functional connectivity (dFC) of motor cerebellum with cerebral cortex in juvenile myoclonic epilepsy (JME). Methods: We adopted resting-state electroencephalography-functional magnetic resonance imaging (EEG-fMRI) and a sliding-window approach to explore the dFC of motor cerebellum with cortex in 36 JME patients compared with 30 and age-matched health controls (HCs). The motor cerebellum was divided into five lobules (I-V, VI, VIIb, VIIIa, and VIIIb). Additionally, correlation analyses were conducted between the variability of dFC and clinical variables in the Juvenile Myoclonic Epilepsy (JME) group, such as disease duration, age at disease onset, and frequency score of myoclonic seizures. Results: Compared to HCs, the JME group presented increased dFC between the motor cerebellum with SMN and DMN. Specifically, connectivity between lobule VIIb and left precentral gyrus and right inferior parietal lobule (IPL); between lobule VIIIa and right inferior frontal gyrus (IFG) and left IPL; and between lobule VIIIb and left middle frontal gyrus (MFG), bilateral superior parietal gyrus (SPG), and left precuneus. In addition, within the JME group, the strength of dFC between lobule VIIIb and left precuneus was negatively (r = -0.424, p = 0.025, Bonferroni correction) related with the frequency score of myoclonic seizures. Conclusion: In patients with JME, there is a functional dysregulation between the motor cerebellum with DMN and SMN, and the variability of dynamic functional connectivity may be closely associated with the occurrence of motor symptoms in JME.

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI.

Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.

Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain.

There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated. We acquired simultaneous ECoG-fMRI in the sensorimotor cortex of three patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was co-localised with fMRI measures across all subjects. The best model of fMRI changes across states was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than any others that were based either on the classical frequency bands or on summary measures of cross-spectral changes. The region-specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

Temporal cluster-based organization of sleep spindles underlies motor memory consolidation.

Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remain unclear. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organized patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only. Altogether, our findings suggest that spindles' clustering and rhythmic occurrence during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronized oscillatory activity within a subcortical-cortical network involved during learning.

Electroencephalography-functional magnetic resonance imaging for clinical evaluation in focal epilepsy.

OBJECTIVE: We aimed to evaluate the contribution of simultaneous recording of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) in the diagnosis of epilepsy syndrome, localization of the epileptogenic zone (EZ), and decision-making regarding surgical treatment. METHODS: We performed a retrospective study to evaluate patients with focal epilepsy who underwent EEG-fMRI. Two evaluators assessed epilepsy syndrome, presumed focus, and surgical candidacy and defined confidence levels. They assessed these clinical characteristics first without EEG-fMRI and then including EEG-fMRI to assess how the results of EEG-fMRI changed the evaluations. We also determined how the clinical evaluation was affected by the concordance level between the blood oxygen level-dependent (BOLD) response and the presumed focus location, and by the confidence level of the BOLD response itself based on the t-value of the primary and secondary clusters. RESULTS: Fifty-one scans from 48 patients were included. The BOLD map affected 66.7% of the evaluations by altering evaluation items (epilepsy syndrome, presumed focus, or surgical candidacy) or their confidence levels. EEG-fMRI results increased the confidence levels of epilepsy syndrome, presumed focus, or surgical candidacy in 47.1% of patients but reduced clinical confidence in these features in 11.8%. More specifically, the confidence levels increased for epilepsy syndrome in 28.5%, identification of presumed focus in 33.9%, and determination of surgical candidacy in 29.4%. The BOLD signal confidence level, whether high or low, did not influence these clinical factors. SIGNIFICANCE: Previous studies have emphasized the utility of EEG-fMRI for the localization of the epileptogenic zone. This study demonstrated the potential of EEG-fMRI to influence clinical confidence when determining epilepsy syndrome, the presumed epileptic focus, and surgical candidacy.

Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study.

INTRODUCTION: Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes. METHODS: We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults. RESULTS: Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes. CONCLUSIONS: Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings.

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Rapid Processing of Observed Touch through Social Perceptual Brain Regions: An EEG-fMRI Fusion Study.

Seeing social touch triggers a strong social-affective response that involves multiple brain networks, including visual, social perceptual, and somatosensory systems. Previous studies have identified the specific functional role of each system, but little is known about the speed and directionality of the information flow. Is this information extracted via the social perceptual system or from simulation from somatosensory cortex? To address this, we examined the spatiotemporal neural processing of observed touch. Twenty-one human participants (seven males) watched 500-ms video clips showing social and nonsocial touch during electroencephalogram (EEG) recording. Visual and social-affective features were rapidly extracted in the brain, beginning at 90 and 150 ms after video onset, respectively. Combining the EEG data with functional magnetic resonance imaging (fMRI) data from our prior study with the same stimuli reveals that neural information first arises in early visual cortex (EVC), then in the temporoparietal junction and posterior superior temporal sulcus (TPJ/pSTS), and finally in the somatosensory cortex. EVC and TPJ/pSTS uniquely explain EEG neural patterns, while somatosensory cortex does not contribute to EEG patterns alone, suggesting that social-affective information may flow from TPJ/pSTS to somatosensory cortex. Together, these findings show that social touch is processed quickly, within the timeframe of feedforward visual processes, and that the social-affective meaning of touch is first extracted by a social perceptual pathway. Such rapid processing of social touch may be vital to its effective use during social interaction.SIGNIFICANCE STATEMENT Seeing physical contact between people evokes a strong social-emotional response. Previous research has identified the brain systems responsible for this response, but little is known about how quickly and in what direction the information flows. We demonstrated that the brain processes the social-emotional meaning of observed touch quickly, starting as early as 150 ms after the stimulus onset. By combining electroencephalogram (EEG) data with functional magnetic resonance imaging (fMRI) data, we show for the first time that the social-affective meaning of touch is first extracted by a social perceptual pathway and followed by the later involvement of somatosensory simulation. This rapid processing of touch through the social perceptual route may play a pivotal role in effective usage of touch in social communication and interaction.

Simultaneous and independent electroencephalography and magnetic resonance imaging: A multimodal neuroimaging dataset.

We introduce an open access, multimodal neuroimaging dataset comprising simultaneously and independently collected Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data from twenty healthy, young male individuals (mean age = 26 years; SD = 3.8 years). The dataset adheres to the BIDS standard specification and is structured into two components: 1) EEG data recorded outside the Magnetic Resonance (MR) environment, inside the MR scanner without image collection and during simultaneous functional MRI acquisition (EEG-fMRI) and 2) Functional MRI data acquired with and without simultaneous EEG recording and structural MRI data obtained with and without the participants wearing the EEG cap. EEG data were recorded with an MR-compatible EEG recording system (GES 400 MR, Electrical Geodesics Inc.) using a 32-channel sponge-based EEG cap (Geodesic Sensor Net). Eyes-closed resting-state EEG data were recorded for two minutes in both the outside and inside scanner conditions and for ten minutes during simultaneous EEG-fMRI. Eyes-open resting-state EEG data were recorded for two minutes under each condition. Participants also performed an eyes opening-eyes closure block-design task outside the scanner (two minutes) and during simultaneous EEG-fMRI (four minutes). The EEG data recorded outside the scanner provides a reference signal devoid of MR-related artifacts. The data collected inside the scanner without image acquisition captures the contribution of the ballistocardiographic (BCG) without the gradient artifact, making it suitable for testing and validating BCG artifact correction methods. The EEG-fMRI data is affected by both the gradient and BCG artifacts. Brain images were acquired using a 3T GE MR750-Discovery MR scanner equipped with a 32-channel head coil. Whole-brain functional images were obtained using a GRE-EPI T2* weighted sequence (TR = 2000 ms, TE = 40 ms, 35 interleaved axial slices with 4 mm isometric voxels). Structural images were acquired using an SPGR sequence (TR = 8.1 ms, TE = 3.2 ms, flip angle = 12°, 176 sagittal slices with 1 mm isometric voxels). This stands as one of the largest open access EEG-fMRI datasets available, which allows researchers to: 1) Assess the impact of gradient and BCG artifacts on EEG data, 2) Evaluate the effectiveness of novel artifact removal techniques to minimize artifact contribution and preserve EEG signal integrity, 3) Conduct hardware/setup comparison studies, 4) Evaluate the quality of structural and functional MRI data obtained with this particular EEG system, and 5) Implement and validate multimodal integrative analysis approaches on simultaneous EEG-fMRI data.

Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings.

Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.

Estimation of fMRI responses related to epileptic discharges using Bayesian hierarchical modeling.

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a unique and noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null-hypothesis tests, the conventional analysis may overestimate fMRI response amplitudes related to interictal epileptic discharges (IEDs), especially when IEDs are rare. We aimed to estimate fMRI response amplitudes represented by blood oxygen level dependent (BOLD) percentage changes related to IEDs using a hierarchical model. It involves the local and distributed hemodynamic response homogeneity to regularize estimations. Bayesian inference was applied to fit the model. Eighty-two epilepsy patients who underwent EEG-fMRI and subsequent surgery were included in this study. A conventional voxel-wise general linear model was compared to the hierarchical model on estimated fMRI response amplitudes and on the concordance between the highest response cluster and the surgical cavity. The voxel-wise model overestimated fMRI responses compared to the hierarchical model, evidenced by a practically and statistically significant difference between the estimated BOLD percentage changes. Only the hierarchical model differentiated brief and long-lasting IEDs with significantly different BOLD percentage changes. Overall, the hierarchical model outperformed the voxel-wise model on presurgical evaluation, measured by higher prediction performance. When compared with a previous study, the hierarchical model showed higher performance metric values, but the same or lower sensitivity. Our results demonstrated the capability of the hierarchical model of providing more physiologically reasonable and more accurate estimations of fMRI response amplitudes induced by IEDs. To enhance the sensitivity of EEG-fMRI for presurgical evaluation, it may be necessary to incorporate more appropriate spatial priors and bespoke decision strategies.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI.

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus' real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation.

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.