EEG Signatures of Dynamic Functional Network Connectivity States.

The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/patterns between different brain networks that reliably occur across time and subjects. Whether these connectivity states correspond to meaningful electrophysiological signatures was not clear. In this study, using a dataset with concurrent EEG and resting state functional imaging data acquired during eyes open and eyes closed states, we demonstrate the replicability of previous findings in an independent sample, and identify EEG spectral signatures associated with these functional network connectivity changes. Eyes open and eyes closed conditions show common and different connectivity patterns that are associated with distinct EEG spectral signatures. Certain connectivity states are more prevalent in the eyes open case and some occur only in eyes closed state. Both conditions exhibit a state of increased thalamocortical anticorrelation associated with reduced EEG spectral alpha power and increased delta and theta power possibly reflecting drowsiness. This state occurs more frequently in the eyes closed state. In summary, we find a link between dynamic connectivity in fMRI data and concurrently collected EEG data, including a large effect of vigilance on functional connectivity. As demonstrated with EEG and fMRI, the stationarity of connectivity cannot be assumed, even for relatively short periods.

Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis.

Although the visual system has been extensively investigated, an integrated account of the spatiotemporal dynamics of long-range signal propagation along the human visual pathways is not completely known or validated. In this work, we used dynamic causal modeling approach to provide insights into the underlying neural circuit dynamics of pattern reversal visual-evoked potentials extracted from concurrent EEG-fMRI data. A recurrent forward-backward connectivity model, consisting of multiple interacting brain regions identified by EEG source localization aided by fMRI spatial priors, best accounted for the data dynamics. Sources were first identified in the thalamic area, primary visual cortex, as well as higher cortical areas along the ventral and dorsal visual processing streams. Consistent with hierarchical early visual processing, the model disclosed and quantified the neural temporal dynamics across the identified activity sources. This signal propagation is dominated by a feedforward process, but we also found weaker effective feedback connectivity. Using effective connectivity analysis, the optimal dynamic causal modeling revealed enhanced connectivity along the dorsal pathway but slightly suppressed connectivity along the ventral pathway. A bias was also found in favor of the right hemisphere consistent with functional attentional asymmetry. This study validates, for the first time, the long-range signal propagation timing in the human visual pathways. A similar modeling approach can potentially be used to understand other cognitive processes and dysfunctions in signal propagation in neurological and neuropsychiatric disorders. Significance statement: An integrated account of long-range visual signal propagation in the human brain is currently incomplete. Using computational neural modeling on our acquired concurrent EEG-fMRI data under a visual evoked task, we found not only a substantial forward propagation toward "higher-order" brain regions but also a weaker backward propagation. Asymmetry in our model's long-range connectivity accounted for the various observed activity biases. Importantly, the model disclosed the timing of signal propagation across these connectivity pathways and validates, for the first time, long-range signal propagation in the human visual system. A similar modeling approach could be used to identify neural pathways for other cognitive processes and their dysfunctions in brain disorders.

Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery.

Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain-computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

Fusing concurrent EEG-fMRI with dynamic causal modeling: application to effective connectivity during face perception.

Despite the wealth of research on face perception, the interactions between core regions in the face-sensitive network of the visual cortex are not well understood. In particular, the link between neural activity in face-sensitive brain regions measured by fMRI and EEG markers of face-selective processing in the N170 component is not well established. In this study, we used dynamic causal modeling (DCM) as a data fusion approach to integrate concurrently acquired EEG and fMRI data during the perception of upright compared with inverted faces. Data features derived from single-trial EEG variability were used as contextual modulators on fMRI-derived estimates of effective connectivity between key regions of the face perception network. The overall construction of our model space was highly constrained by the effects of task and ERP parameters on our fMRI data. Bayesian model selection suggested that the occipital face area (OFA) acted as a central gatekeeper directing visual information to the superior temporal sulcus (STS), the fusiform face area (FFA), and to a medial region of the fusiform gyrus (mFG). The connection from the OFA to the STS was strengthened on trials in which N170 amplitudes to upright faces were large. In contrast, the connection from the OFA to the mFG, an area known to be involved in object processing, was enhanced for inverted faces particularly on trials in which N170 amplitudes were small. Our results suggest that trial-by-trial variation in neural activity at around 170 ms, reflected in the N170 component, reflects the relative engagement of the OFA to STS/FFA network over the OFA to mFG object processing network for face perception. Importantly, the DCMs predicted the observed data significantly better by including the modulators derived from the N170, highlighting the value of incorporating EEG-derived information to explain interactions between regions as a multi-modal data fusion method for combined EEG-fMRI.

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.

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.

Regional Brain Correlates of Beta Bursts in Health and Psychosis: A Concurrent Electroencephalography and Functional Magnetic Resonance Imaging Study.

BACKGROUND: There is emerging evidence for abnormal beta oscillations in psychosis. Beta oscillations are likely to play a key role in the coordination of sensorimotor information that is crucial to healthy mental function. Growing evidence suggests that beta oscillations typically manifest as transient beta bursts that increase in probability following a motor response, observable as post-movement beta rebound. Evidence indicates that post-movement beta rebound is attenuated in psychosis, with greater attenuation associated with greater symptom severity and impairment. Delineating the functional role of beta bursts therefore may be key to understanding the mechanisms underlying persistent psychotic illness. METHODS: We used concurrent electroencephalography and functional magnetic resonance imaging to identify blood oxygen level-dependent correlates of beta bursts during the n-back working memory task and intervening rest periods in healthy control participants (n = 30) and patients with psychosis (n = 48). RESULTS: During both task blocks and intervening rest periods, beta bursts phasically activated regions implicated in task-relevant content while suppressing currently tonically active regions. Patients showed attenuated post-movement beta rebound that was associated with persisting disorganization symptoms as well as impairments in cognition and role function. Patients also showed greater task-related reductions in overall beta burst rate and showed greater, more extensive, beta burst-related blood oxygen level-dependent activation. CONCLUSIONS: Our evidence supports a model in which beta bursts reactivate latently maintained sensorimotor information and are dysregulated and inefficient in psychosis. We propose that abnormalities in the mechanisms by which beta bursts coordinate reactivation of contextually appropriate content can manifest as disorganization, working memory deficits, and inaccurate forward models and may underlie a core deficit associated with persisting symptoms and impairment.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition.

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal.

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG-fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.

Using concurrent EEG and fMRI to probe the state of the brain in schizophrenia.

Perceptional abnormalities in schizophrenia are associated with hallucinations and delusions, but also with negative symptoms and poor functional outcome. Perception can be studied using EEG-derived event related potentials (ERPs). Because of their excellent temporal resolution, ERPs have been used to ask when perception is affected by schizophrenia. Because of its excellent spatial resolution, functional magnetic resonance imaging (fMRI) has been used to ask where in the brain these effects are seen. We acquired EEG and fMRI data simultaneously to explore when and where auditory perception is affected by schizophrenia. Thirty schizophrenia (SZ) patients and 23 healthy comparison subjects (HC) listened to 1000 Hz tones occurring about every second. We used joint independent components analysis (jICA) to combine EEG-based event-related potential (ERP) and fMRI responses to tones. Five ERP-fMRI joint independent components (JIC) were extracted. The "N100" JIC had temporal weights during N100 (peaking at 100 ms post-tone onset) and fMRI spatial weights in superior and middle temporal gyri (STG/MTG); however, it did not differ between groups. The "P200" JIC had temporal weights during P200 and positive fMRI spatial weights in STG/MTG and frontal areas, and negative spatial weights in the nodes of the default mode network (DMN) and visual cortex. Groups differed on the "P200" JIC: SZ had smaller "P200" JIC, especially those with more severe avolition/apathy. This is consistent with negative symptoms being related to perceptual deficits, and suggests patients with avolition/apathy may allocate too few resources to processing external auditory events and too many to processing internal events.

Length matters: Improved high field EEG-fMRI recordings using shorter EEG cables.

BACKGROUND: The use of concurrent EEG-fMRI recordings has increased in recent years, allowing new avenues of medical and cognitive neuroscience research; however, currently used setups present problems with data quality and reproducibility. NEW METHOD: We propose a compact experimental setup for concurrent EEG-fMRI at 4T and compare it to a more standard reference setup. The compact setup uses short EEG cables connecting to the amplifiers, which are placed right at the back of the head RF coil on a form-fitting extension force-locked to the patient MR bed. We compare the two setups in terms of sensitivity to MR-room environmental noise, interferences between measuring devices (EEG or fMRI), and sensitivity to functional responses in a visual stimulation paradigm. RESULTS: The compact setup reduces the system sensitivity to both external noise and MR-induced artefacts by at least 60%, with negligible EEG noise induced from the mechanical vibrations of the cryogenic cooling compression pump. COMPARISON WITH EXISTING METHODS: The compact setup improved EEG data quality and the overall performance of MR-artifact correction techniques. Both setups were similar in terms of the fMRI data, with higher reproducibility for cable placement within the scanner in the compact setup. CONCLUSIONS: This improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG-fMRI experimental setup to external noise sources, setting up an EEG-fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed.

Reproducible paired sources from concurrent EEG-fMRI data using BICAR.

We introduce BICAR, an algorithm for obtaining robust, reproducible pairs of temporal and spatial components at the individual subject level from concurrent electroencephalographic and functional magnetic resonance imaging data. BICAR assigns a task-independent measure of component quality, reproducibility, to each paired source. Under BICAR a reproducibility cutoff is derived that can be used to objectively discard spuriously paired EEG-fMRI components. BICAR is run on minimally processed data: fMRI images undergo the standard preprocessing steps (alignment, motion correction, etc.) and EEG data, after scanner artifact removal, are simply bandpass filtered. This minimal processing allows the secondary scoring of the same set of BICAR components for a variety of different endpoint analyses; in this manuscript we propose a general method for scoring components for task event synchronization (evoked response analysis), but scoring using many other criteria, for example frequency content, are possible. BICAR is applied to five subjects performing a visual search task, and among the most reproducible components we find biologically relevant paired sources involved in visual processing, motor planning, execution, and attention.