Dual array EEG-fMRI: An approach for motion artifact suppression in EEG recorded simultaneously with fMRI.

OBJECTIVE: Although simultaneous recording of EEG and MRI has gained increasing popularity in recent years, the extent of its clinical use remains limited by various technical challenges. Motion interference is one of the major challenges in EEG-fMRI. Here we present an approach which reduces its impact with the aid of an MR compatible dual-array EEG (daEEG) in which the EEG itself is used both as a brain signal recorder and a motion sensor. METHODS: We implemented two arrays of EEG electrodes organized into two sets of nearly orthogonally intersecting wire bundles. The EEG was recorded using referential amplifiers inside a 3T MR-scanner. Virtual bipolar measurements were taken both along bundles (creating a small wire loop and therefore minimizing artifact) and across bundles (creating a large wire loop and therefore maximizing artifact). Independent component analysis (ICA) was applied. The resulting ICA components were classified into brain signal and noise using three criteria: 1) degree of two-dimensional spatial correlation between ICA coefficients along bundles and across bundles; 2) amplitude along bundles vs. across bundles; 3) correlation with ECG. The components which passed the criteria set were transformed back to the channel space. Motion artifact suppression and the ability to detect interictal epileptic spikes following daEEG and Optimal Basis Set (OBS) procedures were compared in 10 patients with epilepsy. RESULTS: The SNR achieved by daEEG was 11.05±3.10 and by OBS was 8.25±1.01 (p<0.00001). In 9 of 10 patients, more spikes were detected after daEEG than after OBS (p<0.05). SIGNIFICANCE: daEEG improves signal quality in EEG-fMRI recordings, expanding its clinical and research potential.

Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG neurofeedback.

The transition from being fully awake to pre-sleep occurs daily just before falling asleep; thus its disturbance might be detrimental. Yet, the neuronal correlates of the transition remain unclear, mainly due to the difficulty in capturing its inherent dynamics. We used an EEG theta/alpha neurofeedback to rapidly induce the transition into pre-sleep and simultaneous fMRI to reveal state-dependent neural activity. The relaxed mental state was verified by the corresponding enhancement in the parasympathetic response. Neurofeedback sessions were categorized as successful or unsuccessful, based on the known EEG signature of theta power increases over alpha, temporally marked as a distinct "crossover" point. The fMRI activation was considered before and after this point. During successful transition into pre-sleep the period before the crossover was signified by alpha modulation that corresponded to decreased fMRI activity mainly in sensory gating related regions (e.g. medial thalamus). In parallel, although not sufficient for the transition, theta modulation corresponded with increased activity in limbic and autonomic control regions (e.g. hippocampus, cerebellum vermis, respectively). The post-crossover period was designated by alpha modulation further corresponding to reduced fMRI activity within the anterior salience network (e.g. anterior cingulate cortex, anterior insula), and in contrast theta modulation corresponded to the increased variance in the posterior salience network (e.g. posterior insula, posterior cingulate cortex). Our findings portray multi-level neural dynamics underlying the mental transition from awake to pre-sleep. To initiate the transition, decreased activity was required in external monitoring regions, and to sustain the transition, opposition between the anterior and posterior parts of the salience network was needed, reflecting shifting from extra- to intrapersonal based processing, respectively.

An EEG Finger-Print of fMRI deep regional activation.

This work introduces a general framework for producing an EEG Finger-Print (EFP) which can be used to predict specific brain activity as measured by fMRI at a given deep region. This new approach allows for improved EEG spatial resolution based on simultaneous fMRI activity measurements. Advanced signal processing and machine learning methods were applied on EEG data acquired simultaneously with fMRI during relaxation training guided by on-line continuous feedback on changing alpha/theta EEG measure. We focused on demonstrating improved EEG prediction of activation in sub-cortical regions such as the amygdala. Our analysis shows that a ridge regression model that is based on time/frequency representation of EEG data from a single electrode, can predict the amygdala related activity significantly better than a traditional theta/alpha activity sampled from the best electrode and about 1/3 of the times, significantly better than a linear combination of frequencies with a pre-defined delay. The far-reaching goal of our approach is to be able to reduce the need for fMRI scanning for probing specific sub-cortical regions such as the amygdala as the basis for brain-training procedures. On the other hand, activity in those regions can be characterized with higher temporal resolution than is obtained by fMRI alone thus revealing additional information about their processing mode.

From animal model to human brain networking: dynamic causal modeling of motivational systems.

An organism's behavior is sensitive to different reinforcements in the environment. Based on extensive animal literature, the reinforcement sensitivity theory (RST) proposes three separate neurobehavioral systems to account for such context-sensitive behavior, affecting the tendency to react to punishment, reward, or goal-conflict stimuli. The translation of animal findings to complex human behavior, however, is far from obvious. To examine whether the neural networks underlying humans' motivational processes are similar to those proposed by the RST model, we conducted a functional MRI study, in which 24 healthy subjects performed an interactive game that engaged the different motivational systems using distinct time periods (states) of punishment, reward, and conflict. Crucially, we found that the different motivational states elicited activations in brain regions that corresponded exactly to the brain systems underlying RST. Moreover, dynamic causal modeling of each motivational system confirmed that the coupling strengths between the key brain regions of each system were enabled selectively by the appropriate motivational state. These results may shed light on the impairments that underlie psychopathologies associated with dysfunctional motivational processes and provide a translational validity for the RST.

Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities.

The dark side of the alpha rhythm: fMRI evidence for induced alpha modulation during complete darkness.

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes-open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data-driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes-open/eyes-closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time-course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top-down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom-up processing of sensory input.

Portraying emotions at their unfolding: a multilayered approach for probing dynamics of neural networks.

Dynamic functional integration of distinct neural systems plays a pivotal role in emotional experience. We introduce a novel approach for studying emotion-related changes in the interactions within and between networks using fMRI. It is based on continuous computation of a network cohesion index (NCI), which is sensitive to both strength and variability of signal correlations between pre-defined regions. The regions encompass three clusters (namely limbic, medial prefrontal cortex (mPFC) and cognitive), each previously was shown to be involved in emotional processing. Two sadness-inducing film excerpts were viewed passively, and comparisons between viewer's rated sadness, parasympathetic, and inter-NCI and intra-NCI were obtained. Limbic intra-NCI was associated with reported sadness in both movies. However, the correlation between the parasympathetic-index, the rated sadness and the limbic-NCI occurred in only one movie, possibly related to a "deactivated" pattern of sadness. In this film, rated sadness intensity also correlated with the mPFC intra-NCI, possibly reflecting temporal correspondence between sadness and sympathy. Further, only for this movie, we found an association between sadness rating and the mPFC-limbic inter-NCI time courses. To the contrary, in the other film in which sadness was reported to commingle with horror and anger, dramatic events coincided with disintegration of these networks. Together, this may point to a difference between the cinematic experiences with regard to inter-network dynamics related to emotional regulation. These findings demonstrate the advantage of a multi-layered dynamic analysis for elucidating the uniqueness of emotional experiences with regard to an unguided processing of continuous and complex stimulation.

Spatio-temporal indications of sub-cortical involvement in leftward bias of spatial attention.

A leftward bias is well known in humans and animals, and commonly related to the right hemisphere dominance for spatial attention. Our previous fMRI study suggested that this bias is mediated by faster conduction from the right to left parietal cortices, than the reverse (Siman-Tov et al., 2007). However, the limited temporal resolution of fMRI and evidence on the critical involvement of sub-cortical regions in orienting of spatial attention suggested further investigation of the leftward bias using multi-scale measurement. In this simultaneous EEG-fMRI study, healthy participants were presented with face pictures in either the right or left visual fields while performing a central fixation task. Temporo-occipital event related potentials, time-locked to the stimulus onset, showed an association between faster conduction from the right to the left hemisphere and higher fMRI activation in the left pulvinar nucleus following left visual field stimulation. This combined-modal finding provides original evidence of the involvement of sub-cortical central attention-related regions in the leftward bias. This assertion was further strengthened by a DCM analysis designated at cortical (i.e., inferior parietal sulcus; IPS) and sub-cortical (pulvinar nucleus) attention-related nodes that revealed: 1. Stronger inter-hemispheric connections from the right to left than vice versa, already at the pulvinar level. 2. Stronger connections within the right than the left hemisphere, from the pulvinar to the IPS. This multi-level neural superiority can guide future efforts in alleviating attention deficits by focusing on improving network connectivity.

A software tool for interactive exploration of intrinsic functional connectivity opens new perspectives for brain surgery.

BACKGROUND: Functional connectivity analysis of resting-state functional magnetic resonance imaging data (fcrs-fMRI) has been shown to be a robust non-invasive method for localization of functional networks (without using specific tasks) and to be promising for presurgical planning. However, in order to transfer the approach to everyday clinical practice, fcrs-fMRI needs to be further validated and made easily accessible to neurosurgeons. This paper addresses the latter by presenting a software tool designed for neurosurgeons for analyzing and visualizing fcrs-fMRI data. METHODS: A prototypical interactive visualization tool was developed to enable neurosurgeons to explore functional connectivity data and evaluate its usability. The implementation builds upon LIPSIA, an established software package for the assessment of functional neuroimaging data, and integrates the selection of a region-of-interest with the computation and visualization of functionally connected areas. The tool was used to explore data from a healthy participant and eight brain lesion patients. The usability of the software was evaluated with four neurosurgeons previously unacquainted with the methodology, who were asked to identify prominent, large-scale cortical networks. FINDINGS: With this novel tool, previously published findings, such as tumor displacement of the sensorimotor cortex and other disturbances of functional networks, were reproduced. The neurosurgeons were able to consistently obtain results similar to the results of an expert, with the exception of the language network. Immediate feedback helped to pinpoint functional networks quickly and intuitively, with even inexperienced users requiring less than 3 min per network. CONCLUSIONS: Although fcrs-fMRI is a nascent method still undergoing evaluation with respect to established standards, the interactive software is nonetheless a promising tool for non-invasive exploration of individual functional connectivity networks in neurosurgical practice, both for well-known networks and for those less typically addressed.

Event-related potential and functional MRI measures of face-selectivity are highly correlated: a simultaneous ERP-fMRI investigation.

A face-selective neural signal is reliably found in humans with functional MRI and event-related potential (ERP) measures, which provide complementary information about the spatial and temporal properties of the neural response. However, because most neuroimaging studies so far have studied ERP and fMRI face-selective markers separately, the relationship between them is still unknown. Here we simultaneously recorded fMRI and ERP responses to faces and chairs to examine the correlations across subjects between the magnitudes of fMRI and ERP face-selectivity measures. Findings show that the face-selective responses in the temporal lobe (i.e., fusiform gyrus--FFA) and superior temporal sulcus (fSTS), but not the face-selective response in the occipital cortex (OFA), were highly correlated with the face-selective N170 component. In contrast, the OFA was correlated with earlier ERPs at about 110 ms after stimulus-onset. Importantly, these correlations reveal a temporal dissociation between the face-selective area in the occipital lobe and face-selective areas in the temporal lobe. Despite the very different time-scale of the fMRI and EEG signals, our data show that a correlation analysis across subjects may be informative with respect to the latency in which different brain regions process information.

Bihemispheric leftward bias in a visuospatial attention-related network.

Asymmetry of spatial attention has long been described in both disease (hemispatial neglect) and healthy (pseudoneglect) states. Although right-hemisphere specialization for spatial attention has been suggested, the exact neural mechanisms of asymmetry have not been deciphered yet. A recent functional magnetic resonance imaging study from our laboratory serendipitously revealed bihemispheric left-hemifield superiority in activation of a visuospatial attention-related network. Nineteen right-handed healthy adult females participated in two experiments of visual half-field presentation. Either facial expressions (experiment 1) or house images (experiment 2) were presented unilaterally and parafoveally for 150 ms while subjects were engaging a central fixation task. Brain regions previously associated with a visuospatial attention network, in both hemispheres, were found to be more robustly activated by left visual field stimuli. The consistency of this finding with manifestations of attention lateralization is discussed, and a revised model based on neural connectivity asymmetry is proposed. Support for the revised model is given by a dynamic causal modeling analysis. Unraveling the basis for attention asymmetry may lead to better understanding of the pathogenesis of attention disorders, followed by improved diagnosis and treatment. Additionally, the proposed model for asymmetry of visuospatial attention might provide important insights into the mechanisms underlying functional brain lateralization in general.

The validity of the face-selective ERP N170 component during simultaneous recording with functional MRI.

Despite the wide interest in the neural mechanisms of face processing and numerous event-related potential (ERP) and functional MRI (fMRI) studies of face-selective neural responses, no study, to date, has collected these two measures simultaneously. The main reason for the absence of such an investigation is that MRI data acquisition generates major artifacts, which completely conceals the EEG signal. Recently, artifact removal algorithms have been developed. Our goal was to examine the validity of the face-selective ERP component N170 and its functional effects such as category selectivity and hemispherical laterality, when recorded simultaneously with functional MRI. In our experiment, half of the scans were collected during fMRI acquisition and half without fMRI acquisition. The validity of the N170 was then measured for its amplitude, latency, face selectivity (the difference between the amplitude to faces and objects), laterality (the difference between the amplitude to faces over the right and the left hemispheres) and the laterality of the face selectivity effect, by correlating these measures across subjects between data collected without fMRI and with fMRI data acquisition, after applying artifact removal procedures. We found high validity coefficients for all N170 measures. Furthermore, ERP data collected outside the scanner on a different day were highly correlated with data collected during MR acquisition for the N170 amplitude, latency, and selectivity index but moderate for laterality indices. Our study demonstrates that face-selective ERP effects are preserved in simultaneous recording with fMRI. These findings will hopefully encourage researchers to combine the two complementary neuroimaging techniques in future research.

Time-varying spectral entropy differentiates between positive and negative feed back-related EEG activity in a hypothesis testing paradigm.

Positive and negative performance feedbacks have been shown to differentially modulate amplitudes of the associated electroencephalographic (EEG) brain activity. In the present study, we tested whether feedback also modulates the organization of neuronal oscillations. Ten college students serially tested hypotheses concerning a hidden rule by judging its presence or absence in triplets of digits and revised them on the basis of an exogenous performance feedback. The EEG signal time-locked to feedback was convolved with a family of complex wavelets. The time-varying spectral entropy of the resulting time-frequency representation was then computed. The results showed that feedback differentially modulated spectral organization at frontal and posterior scalp regions around 200 ms and in the 300-500 ms range. Spatio-temporal principal component analysis (PCA) indicated that feedback-specific modulations essentially resulted from the interplay between fronto-polar, fronto-central, and parieto-occipital components. Functional and methodological implications were discussed.

Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality.

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans.

Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity.

Stressful experiences modulate neuro-circuitry function, and the temporal trajectory of these alterations, elapsing from early disturbances to late recovery, heavily influences resilience and vulnerability to stress. Such effects of stress may depend on processes that are engaged during resting-state, through active recollection of past experiences and anticipation of future events, all known to involve the default mode network (DMN). By inducing social stress and acquiring resting-state functional magnetic resonance imaging (fMRI) before stress, immediately following it, and 2 h later, we expanded the time-window for examining the trajectory of the stress response. Throughout the study repeated cortisol samplings and self-reports of stress levels were obtained from 51 healthy young males. Post-stress alterations were investigated by whole brain resting-state functional connectivity (rsFC) of two central hubs of the DMN: the posterior cingulate cortex (PCC) and hippocampus. Results indicate a 'recovery' pattern of DMN connectivity, in which all alterations, ascribed to the intervening stress, returned to pre-stress levels. The only exception to this pattern was a stress-induced rise in amygdala-hippocampal connectivity, which was sustained for as long as 2 h following stress induction. Furthermore, this sustained enhancement of limbic connectivity was inversely correlated to individual stress-induced cortisol responsiveness (AUCi) and characterized only the group lacking such increased cortisol (i.e., non-responders). Our observations provide evidence of a prolonged post-stress response profile, characterized by both the comprehensive balance of most DMN functional connections and the distinct time and cortisol dependent ascent of intra-limbic connectivity. These novel insights into neuro-endocrine relations are another milestone in the ongoing search for individual markers in stress-related psychopathologies.

Robust modeling based on optimized EEG bands for functional brain state inference.

The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30 s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.

Enhanced functional synchronization of medial and lateral PFC underlies internally-guided action planning.

Actions are often internally guided, reflecting our covert will and intentions. The dorsomedial prefrontal cortex, including the pre-Supplementary Motor Area (pre-SMA), has been implicated in the internally generated aspects of action planning, such as choice and intention. Yet, the mechanism by which this area interacts with other cognitive brain regions such as the dorsolateral prefrontal cortex, a central node in decision-making, is still unclear. To shed light on this mechanism, brain activity was measured via fMRI and intracranial EEG in two studies during the performance of visually cued repeated finger tapping in which the choice of finger was guided by either a presented number (external) or self-choice (internal). A functional-MRI (fMRI) study in 15 healthy participants demonstrated that the pre-SMA, compared to the SMA proper, was more active and also more functionally correlated with the dorsolateral prefrontal cortex during internally compared to externally guided action planning (p < 0.05, random effect). In a similar manner, an intracranial-EEG study in five epilepsy patients showed greater inter-regional gamma-related connectivity between electrodes situated in medial and lateral aspects of the prefrontal cortex for internally compared to externally guided actions. Although this finding was observed for groups of electrodes situated both in the pre-SMA and SMA-proper, increased intra-cluster gamma-related connectivity was only observed for the pre-SMA (sign-test, p < 0.0001). Overall our findings provide multi-scale indications for the involvement of the dorsomedial prefrontal cortex, and especially the pre-SMA, in generating internally guided motor planning. Our intracranial-EEG results further point to enhanced functional connectivity between decision-making- and motor planning aspects of the PFC, as a possible neural mechanism for internally generated action planning.

Never resting brain: simultaneous representation of two alpha related processes in humans.

Brain activity is continuously modulated, even at "rest". The alpha rhythm (8-12 Hz) has been known as the hallmark of the brain's idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An 'induced alpha' that was correlated with the paradigm (eyes open/ eyes closed), and a 'spontaneous alpha' that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the 'induced map' included left lateral temporal cortical regions and the hippocampus; the 'spontaneous map' included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the 'resting brain'; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change.