Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging

Reduction of EEG artefacts induced by vibration in the MR-environment

The EEG acquired simultaneously with functional magnetic resonance imaging (fMRI) is distorted by a number of artefacts related to the presence of strong magnetic fields. In order to allow for a useful interpretation of the EEG data, it is necessary to reduce these artefacts. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. Due to their repetitive nature, such artefacts can be reduced by subtraction of the respective template retrieved by averaging across cycles. In this paper, we investigate additional artefacts related to the MR environment and propose a method for the reduction of the vibration artefact caused by the cryo-cooler compression pumps system. Data were collected from the EEG cap placed on an MR head phantom, in order to characterise the MR environment related artefacts. Since the vibration artefact was found to be repetitive, a template subtraction method was developed for its reduction, and this was then adjusted to meet the specific requirements of patient data. The developed methodology successfully reduced the vibration artefact by about 90% in five EEG-fMRI datasets collected from two epilepsy patients.

Neural substrates of individual differences in human fear learning: evidence from concurrent fMRI, fear-potentiated startle, and US-expectancy data

To provide insight into individual differences in fear learning, we examined the emotional and cognitive expressions of discriminative fear conditioning in direct relation to its neural substrates. Contrary to previous behavioral–neural (fMRI) research on fear learning—in which the emotional expression of fear was generally indexed by skin conductance—we used fear-potentiated startle, a more reliable and specific index of fear. While we obtained concurrent fear-potentiated startle, neuroimaging (fMRI), and US-expectancy data, healthy participants underwent a fear-conditioning paradigm in which one of two conditioned stimuli (CS⁺ but not CS^–) was paired with a shock (unconditioned stimulus [US]). Fear learning was evident from the differential expressions of fear (CS⁺ > CS^–) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula). We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation. This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS–US contingency or hippocampal activation). Our study provides the first evidence for the potential of examining startle potentiation in concurrent fMRI research on fear learning.

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

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

EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5 T scanner and 15 patients with epilepsy recorded in a 3 T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5T data) to 30% (3T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5T scanner show a small negative correlation between HBI and BCG amplitude. In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment.

EEG-fMRI Methods for the Study of Brain Networks during Sleep

Modern neuroimaging methods may provide unique insights into the mechanism and role of sleep, as well as into particular mechanisms of brain function in general. Many of the recent neuroimaging studies have used concurrent EEG and fMRI, which present unique technical challenges ranging from the difficulty of inducing sleep in the MRI environment to appropriate instrumentation and data processing methods to obtain artifact free data. In addition, the use of EEG-fMRI during sleep leads to unique data interpretation issues, as common approaches developed for the analysis of task-evoked activity do not apply to sleep. Reviewed are a variety of statistical approaches that can be used to characterize brain activity from fMRI data acquired during sleep, with an emphasis on approaches that investigate the presence of correlated activity between brain regions. Each of these approaches has advantages and disadvantages that must be considered in concert with the theoretical questions of interest. Specifically, fundamental theories of sleep control and function should be considered when designing these studies and when choosing the associated statistical approaches. For example, the notion that local brain activity during sleep may be triggered by local, use-dependent activity during wakefulness may be tested by analyzing sleep networks as statistically independent components. Alternatively, the involvement of regions in more global processes such as arousal may be investigated with correlation analysis.

Automatic sleep staging using fMRI functional connectivity data

Recent EEG-fMRI studies have shown that different stages of sleep are associated with changes in both brain activity and functional connectivity. These results raise the concern that lack of vigilance measures in resting state experiments may introduce confounds and contamination due to subjects falling asleep inside the scanner. In this study we present a method to perform automatic sleep staging using only fMRI functional connectivity data, thus providing vigilance information while circumventing the technical demands of simultaneous recording of EEG, the gold standard for sleep scoring. The features to classify are the linear correlation values between 20 cortical regions identified using independent component analysis and two regions in the bilateral thalamus. The method is based on the construction of binary support vector machine classifiers discriminating between all pairs of sleep stages and the subsequent combination of them into multiclass classifiers. Different multiclass schemes and kernels are explored. After parameter optimization through 5-fold cross validation we achieve accuracies over 0.8 in the binary problem with functional connectivities obtained for epochs as short as 60s. The multiclass classifier generalizes well to two independent datasets (accuracies over 0.8 in both sets) and can be efficiently applied to any dataset using a sliding window procedure. Modeling vigilance states in resting state analysis will avoid confounded inferences and facilitate the study of vigilance states themselves. We thus consider the method introduced in this study a novel and practical contribution for monitoring vigilance levels inside an MRI scanner without the need of extra recordings other than fMRI BOLD signals.

Traveling EEG slow oscillation along the dorsal attention network initiates spontaneous perceptual switching

An ambiguous figure such as the Necker cube causes spontaneous perceptual switching (SPS). The mechanism of SPS in multistable perception has not yet been determined. Although early psychological studies suggested that SPS may be caused by fatigue or satiation of orientation, the neural mechanism of SPS is still unknown. Functional magnetic resonance imaging (fMRI) has shown that the dorsal attention network (DAN), which mainly controls voluntary attention, is involved in bistable perception of the Necker cube. To determine whether neural dynamics along the DAN cause SPS, we performed simultaneous electroencephalography (EEG) and fMRI during an SPS task with the Necker cube, with every SPS reported by pressing a button. This EEG–fMRI integrated analysis showed that (a) 3–4 Hz spectral EEG power modulation at fronto-central, parietal, and centro-parietal electrode sites sequentially appeared from 750 to 350 ms prior to the button press; and (b) activations correlating with the EEG modulation traveled along the DAN from the frontal to the parietal regions. These findings suggest that slow oscillation initiates SPS through global dynamics along the attentional system such as the DAN.

EEG-fMRI validation studies in comparison with icEEG: a review

Simultaneous EEG-fMRI is a non-invasive investigation technique developed to localize the generators of interictal epileptiform discharges (IED) in patients with epilepsy. Although the value of EEG-fMRI in epilepsy presurgical evaluation is being assessed clinically, its utility is still controversial. In this review, we considered EEG-fMRI applications in epilepsy presurgical evaluation with a focus on validation studies that compared the results of EEG-fMRI with those of the current "gold standard" intracranial EEG (icEEG) in order to assess its utility of seizure focus localization and the possibility for EEG-fMRI to reduce the need for invasive techniques such as icEEG. Since the advances of EEG-fMRI partially rely on the maturation of its data analysis, we also reviewed the methodological developments in EEG-fMRI analysis. It is possible that combining with other neuroimaging modalities such as MEG/MSI and ESI, EEG-fMRI may play a greater role in epilepsy presurgical evaluation.

Learning acts on distinct processes for visual form perception in the human brain

Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learning-dependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

Influence of dense-array EEG cap on fMRI signal

Dense-array (>64 channel) electroencephalography (EEG) systems are increasingly being used in simultaneous EEG-functional magnetic resonance imaging (fMRI) studies. However, with increasing channel count, dense-array EEG caps can induce more severe signal dropout in the MRI images than conventional systems due to the radiofrequency shielding effect of the denser wire bundle. This study investigates the influence of a 256-channel EEG cap on MRI image quality and detection sensitivity of blood oxygen level dependent fMRI signal. A theoretical model is first established to describe the impact of the EEG cap on anatomic signal, noise, signal-to-noise ratio, and contrast-to-noise ratio of blood oxygen level dependent signal. Seven subjects were scanned to measure and compare the T(2)-weighted image quality and fMRI detection sensitivity with and without the EEG cap using an auditory/visual/sensorimotor task. The results show that the dense-array EEG cap can substantially reduce the anatomic signal in the brain areas (visual cortex) near the conducting wires (average percent decrease ≈ 38%). However, the image signal-to-noise ratio with and without the EEG cap was comparable (percent decrease < 8%, not statistically significant), and there was no statistically significant difference in the extent of blood oxygen level dependent activation. This suggests that the ability to detect fMRI signal is nearly unaffected by dense-array EEG caps in simultaneous EEG-fMRI experiments.

Mapping hemodynamic correlates of seizures using fMRI: A review

Functional magnetic resonance imaging (fMRI) is able to detect changes in blood oxygenation level associated with neuronal activity throughout the brain. For more than a decade, fMRI alone or in combination with simultaneous EEG recording (EEG-fMRI) has been used to investigate the hemodynamic changes associated with interictal and ictal epileptic discharges. This is the first literature review to focus on the various fMRI acquisition and data analysis methods applied to map epileptic seizure-related hemodynamic changes from the first report of an fMRI scan of a seizure to the present day. Two types of data analysis approaches, based on temporal correlation and data driven, are explained and contrasted. The spatial and temporal relationship between the observed hemodynamic changes using fMRI and other non-invasive and invasive electrophysiological and imaging data is considered. We then describe the role of fMRI in localizing and exploring the networks involved in spontaneous and triggered seizure onset and propagation. We also discuss that fMRI alone and combined with EEG hold great promise in the investigation of seizure-related hemodynamic changes non-invasively in humans. We think that this will lead to significant improvements in our understanding of seizures with important consequences for the treatment of epilepsy.

Learning shapes spatiotemporal brain patterns for flexible categorical decisions

Learning is thought to facilitate our ability to perform complex perceptual tasks and optimize brain circuits involved in decision making. However, little is known about the experience-dependent mechanisms in the human brain that support our ability to make fine categorical judgments. Previous work has focused on identifying spatial brain patterns (i.e., areas) that change with learning. Here, we take advantage of the complementary high spatial and temporal resolution of simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to identify the spatiotemporal dynamics between cortical networks involved in flexible category learning. Observers were trained to use different decision criteria (i.e., category boundaries) when making fine categorical judgments on morphed stimuli (i.e., radial vs. concentric patterns). Our findings demonstrate that learning acts on a feedback-based circuit that supports fine categorical judgments. Experience-dependent changes in the behavioral decision criterion were associated with changes in later perceptual processes engaging higher occipitotemporal and frontoparietal circuits. In contrast, category learning did not modulate early processes in a medial frontotemporal network that are thought to support the coarse interpretation of visual scenes. These findings provide evidence that learning flexible criteria for fine categorical judgments acts on distinct spatiotemporal brain circuits and shapes the readout of sensory signals that provide evidence for categorical decisions.

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

How ongoing neuronal oscillations account for evoked fMRI variability

Variability of evoked single-trial responses despite constant input or task is a feature of large-scale brain signals recorded by fMRI. Initial evidence signified relevance of fMRI signal variability for perception and behavior. Yet the underlying intrinsic neuronal sources have not been previously substantiated. Here, we address this issue using simultaneous EEG-fMRI and real-time classification of ongoing alpha-rhythm states triggering visual stimulation in human subjects. We investigated whether spontaneous neuronal oscillations-as reflected in the posterior alpha rhythm-account for variability of evoked fMRI responses. Based on previous work, we specifically hypothesized linear superposition of fMRI activity related to fluctuations of ongoing alpha rhythm and a visually evoked fMRI response. We observed that spontaneous alpha-rhythm power fluctuations largely explain evoked fMRI response variance in extrastriate, thalamic, and cerebellar areas. For extrastriate areas, we confirmed the linear superposition hypothesis. We hence linked evoked fMRI response variability to an intrinsic rhythm's power fluctuations. These findings contribute to our conceptual understanding of how brain rhythms can account for trial-by-trial variability in stimulus processing.

Donepezil impairs memory in healthy older subjects: behavioural, EEG and simultaneous EEG/fMRI biomarkers

Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs), by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil) on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL)) and electrophysiological measures (resting EEG power) that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta), right frontal-parietal network (Alpha), and default-mode network (Beta). We demonstrate the utility of simple cognitive and EEG measures in evaluating drug responses after acute and chronic donepezil administration. The presentation of previously established markers of age-related cognitive decline indicates that AChEIs can impair cognitive function in healthy older individuals. To our knowledge this is the first study to identify the precise neuroanatomical origins of EEG drug markers using simultaneous EEG/fMRI. The results of this study may be useful for evaluating novel drugs for cognitive enhancement.

Modulation of visually evoked cortical FMRI responses by phase of ongoing occipital alpha oscillations

Using simultaneous electroencephalography as a measure of ongoing activity and functional magnetic resonance imaging (fMRI) as a measure of the stimulus-driven neural response, we examined whether the amplitude and phase of occipital alpha oscillations at the onset of a brief visual stimulus affects the amplitude of the visually evoked fMRI response. When accounting for intrinsic coupling of alpha amplitude and occipital fMRI signal by modeling and subtracting pseudo-trials, no significant effect of prestimulus alpha amplitude on the evoked fMRI response could be demonstrated. Regarding the effect of alpha phase, we found that stimuli arriving at the peak of the alpha cycle yielded a lower blood oxygenation level-dependent (BOLD) fMRI response in early visual cortex (V1/V2) than stimuli presented at the trough of the cycle. Our results therefore show that phase of occipital alpha oscillations impacts the overall strength of a visually evoked response, as indexed by the BOLD signal. This observation complements existing evidence that alpha oscillations reflect periodic variations in cortical excitability and suggests that the phase of oscillations in postsynaptic potentials can serve as a mechanism of gain control for incoming neural activity. Finally, our findings provide a putative neural basis for observations of alpha phase dependence of visual perceptual performance.

Functional magnetic resonance imaging blood oxygenation level-dependent signal and magnetoencephalography evoked responses yield different neural functionality in reading

It is often implicitly assumed that the neural activation patterns revealed by hemodynamic methods, such as functional magnetic resonance imaging (fMRI), and electrophysiological methods, such as magnetoencephalography (MEG) and electroencephalography (EEG), are comparable. In early sensory processing that seems to be the case, but the assumption may not be correct in high-level cognitive tasks. For example, MEG and fMRI literature of single-word reading suggests differences in cortical activation, but direct comparisons are lacking. Here, while the same human participants performed the same reading task, analysis of MEG evoked responses and fMRI blood oxygenation level-dependent (BOLD) signals revealed marked functional and spatial differences in several cortical areas outside the visual cortex. Divergent patterns of activation were observed in the frontal and temporal cortex, in accordance with previous separate MEG and fMRI studies of reading. Furthermore, opposite stimulus effects in the MEG and fMRI measures were detected in the left occipitotemporal cortex: MEG evoked responses were stronger to letter than symbol strings, whereas the fMRI BOLD signal was stronger to symbol than letter strings. The EEG recorded simultaneously during MEG and fMRI did not indicate neurophysiological differences that could explain the observed functional discrepancies between the MEG and fMRI results. Acknowledgment of the complementary nature of hemodynamic and electrophysiological measures, as reported here in a cognitive task using evoked response analysis in MEG and BOLD signal analysis in fMRI, represents an essential step toward an informed use of multimodal imaging that reaches beyond mere combination of location and timing of neural activation.

Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors

In this paper, we propose a novel symmetrical EEG/fMRI fusion method which combines EEG and fMRI by means of a common generative model. We use a total variation (TV) prior to model the spatial distribution of the cortical current responses and hemodynamic response functions, and utilize spatially adaptive temporal priors to model their temporal shapes. The spatial adaptivity of the prior model allows for adaptation to the local characteristics of the estimated responses and leads to high estimation performance for the cortical current distribution and the hemodynamic response functions. We utilize a Bayesian formulation with a variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulations with synthetic data and experiments with real data from a multimodal study on face perception demonstrate the performance of the proposed method.

Neurophysiological origin of human brain asymmetry for speech and language

The physiological basis of human cerebral asymmetry for language remains mysterious. We have used simultaneous physiological and anatomical measurements to investigate the issue. Concentrating on neural oscillatory activity in speech-specific frequency bands and exploring interactions between gestural (motor) and auditory-evoked activity, we find, in the absence of language-related processing, that left auditory, somatosensory, articulatory motor, and inferior parietal cortices show specific, lateralized, speech-related physiological properties. With the addition of ecologically valid audiovisual stimulation, activity in auditory cortex synchronizes with left-dominant input from the motor cortex at frequencies corresponding to syllabic, but not phonemic, speech rhythms. Our results support theories of language lateralization that posit a major role for intrinsic, hardwired perceptuomotor processing in syllabic parsing and are compatible both with the evolutionary view that speech arose from a combination of syllable-sized vocalizations and meaningful hand gestures and with developmental observations suggesting phonemic analysis is a developmentally acquired process.

Update on neuroimaging in epilepsy

Neuroimaging in epilepsy is a very large and growing field. Researchers in this area have quickly adopted new methods, resulting in a lively literature. Basic features of common epilepsies are well known, but, outside of the specific area of epilepsy surgery evaluation, new methods evolving in the last few years have had limited new beneficial clinical impact. Here, an overview of the epilepsy neuroimaging literature of the last 5 years, with an emphasis on mesial temporal lobe epilepsy, idiopathic generalized epilepsies, presurgical evaluation and new developments in functional MRI is presented. The need for attention to clinical translation, as well as immediate opportunities and future trends in this field, are discussed.

Imaging foci of epileptic discharges from simultaneous EEG and fMRI using the canonical HRF

PURPOSE

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is considered as a powerful and non-invasive method that allows definition of the irritative zone. However, the complex interictal epileptic discharge (IED) may be present in some patients, and sometimes no active foci can be localized using General Linear Model (GLM) which is a widely adopted tool in EEG-fMRI study. The purpose of this study is to develop a new scheme to improve the detectability and localize the canonical HRF localizable foci.

METHOD

Various IEDs are classified using a combination of an independent component analysis (ICA) and a temporal correlation analysis between the independent components and the raw EEG channel; and the classified IEDs are then separately used for foci localization. This scheme is tested by ten patients with variable IEDs, including two patients whose activity could not be identified by common method.

RESULT

Applying this scheme to the two patients, some foci consistent with electroclinical data were localized. When it was applied to the remaining eight patients with positive results using common method, 2-4 types of IEDs were classified, and the activity could be identified from at least one type of IED. The results were similar to that received from common method.

CONCLUSION

These results indicate that the proposed scheme could enhance the imaging of the localizable foci by isolating its IEDs. This scheme is potentially a useful tool for epilepsy clinic.

Physical modeling of pulse artefact sources in simultaneous EEG/fMRI

The collection of electroencephalography (EEG) data during simultaneous functional magnetic resonance imaging (fMRI) is impeded by large artefacts in the EEG recordings, with the pulse artefact (PA) being particularly challenging because of its persistence even after application of artefact correction algorithms. Despite several possible causes of the PA having been hypothesized, few studies have rigorously quantified the contributions from the different putative sources. This article presents analytic expressions and simulations describing two possible sources of the PA corresponding to different movements in the strong static field of the MR scanner: cardiac-pulse-driven head rotation and blood-flow-induced Hall voltages. Models of head rotation about a left-right axis and flow in a deep artery running in the anterior-posterior direction reproduced properties of the PA including the left/right spatial variation of polarity. Of these two sources, head rotation was shown to be the most likely source of the PA with simulated magnitudes of >200 muV being generated at 3 T, similar to the in vivo PA magnitudes, for an angular velocity of just 0.5 degrees /s. Smaller artefact voltages of less than 10 muV were calculated for flow in a model artery with physical characteristics similar to the internal carotid artery. A deeper physical understanding of the PA is a key step in working toward production of higher fidelity EEG/fMRI data: analytic expressions for the artefact voltages can guide a redesign of the wiring layout on EEG caps to minimize intrinsic artefact pickup, while simulated artefact maps could be incorporated into selective filters.

EEG-fMRI reciprocal functional neuroimaging

OBJECTIVE

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been pursued in an effort to achieve greater spatio-temporal resolution of imaging dynamic brain activity. We report a data-driven approach to image spatio-temporal features of neural oscillatory activity and event-related activity from continuously recorded EEG and fMRI signals.

METHODS

This approach starts with using the independent component analysis (ICA) to decompose the spatio-temporal EEG data into a linear combination of scalp potential maps and time courses. The time course of each independent component (IC) is used to construct a regressor to fit the fMRI time series. The resultant fMRI map then feeds back as a spatial constraint to the estimation of the source distribution underlying the corresponding IC's scalp map. The estimated source distributions multiplied by the corresponding IC time courses are summed across all ICs, giving rise to the reconstructed spatio-temporal brain activity. Functional connectivity between cortical areas can be further revealed from the imaged source signals using phase synchrony measures. We tested the method using both simulated oscillatory activity and event-related neural activity at various cortical regions. We also used this method to study the alpha-band EEG modulations in an eyes-open-eyes-closed human experiment.

RESULTS

In the simulation study, reliable reconstruction of the localization, time-frequency feature and cortical functional connection were achieved for the simulated oscillatory and event-related activities. In the experimental study, the alpha rhythmic modulation was localized mainly in the occipital visual area and the parieto-occipital sulcus. Within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition.

CONCLUSION

Our results suggest that the proposed approach is well suited to image continuously oscillatory activities and their functional connectivity.

SIGNIFICANCE

Such ability promises to facilitate the investigation of the long-term neural behaviors and large-scale cortical interactions involved in spontaneous brain activity and cognitive tasks.

fMRI-Compatible Registration of Jaw Movements Using a Fiber-Optic Bend Sensor

A functional magnetic resonance imaging (fMRI)-compatible fiber-optic bend sensor was investigated to assess whether the device could be used effectively to monitor opening and closing of the jaw during an fMRI experiment at 3 T. In contrast to surface electromyography, a bend sensor fixed to the chin of the participant is fast and easy to use and is not affected by strong electromagnetic fields. Bend sensor recordings are characterized by high validity (compared with concurrent video recordings of mouth opening) and high reliability (comparing two independent measurements). The results of this study indicate that a bend sensor is able to record the opening and closing of the jaw associated with different overt speech conditions (producing the utterances /a/, /pa/, /pataka/) and the opening of the mouth without speech production. Data post-processing such as filtering was not necessary. There are several potential applications for bend sensor recordings of speech-related jaw movements. First, bend sensor recordings are a valuable tool to assess behavioral performance, such as response latencies, accuracies, and completion times, which is particularly important in children, seniors, or patients with various neurological or psychiatric conditions. Second, the timing information provided by bend sensor data may improve the predicted hemodynamic response that is used for fMRI analysis based on the general linear model (GLM). Third, bend sensor recordings may be included in GLM analyses not for statistical contrast purposes, but as a covariate of no interest, accounting for part of the data variance to model fMRI artifacts due to motion outside the field of view.

Current themes in neuroimaging of epilepsy: brain networks, dynamic phenomena, and clinical relevance

Brain scanning methods were first applied in patients with epilepsy more than 30years ago. A very substantial literature now exists in this field, which is exponentially increasing. Contemporary neuroimaging studies in epilepsy reflect new concepts in the epilepsies, as well as current methodological developments. In particular, this area is emphasising the role of networks in epileptogenicity, the existence of dynamic phenomena which can be captured by imaging, and is beginning to validate the implementation of neuroimaging in the clinic. Here, recent studies of the last 5years are reviewed, covering the full range of neuroimaging methods with SPECT, PET and MRI in epilepsy.

Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans

There is currently a great interest to combine electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study brain function. Earlier studies have shown different EEG components to correlate well with the fMRI signal arguing for a complex relationship between both measurements. In this study, using separate EEG and fMRI measurements, we show that (1) 0.1 ms visual stimulation evokes detectable hemodynamic and visual-evoked potential (VEP) responses, (2) the negative VEP deflection at approximately 80 ms (N2) co-varies with stimulus duration/intensity such as with blood oxygenation level-dependent (BOLD) response; the positive deflection at approximately 120 ms (P2) does not, and (3) although the N2 VEP-BOLD relationship is approximately linear, deviation is evident at the limit of zero N2 VEP. The latter finding argues that, although EEG and fMRI measurements can co-vary, they reflect partially independent processes in the brain tissue. Finally, it is shown that the stimulus-induced impulse response function (IRF) at 0.1 ms and the intrinsic IRF during rest have different temporal dynamics, possibly due to predominance of neuromodulation during rest as compared with neurotransmission during stimulation. These results extend earlier findings regarding VEP-BOLD coupling and highlight the component- and context-dependency of the relationship between evoked potentials and hemodynamic responses.

A parallel framework for simultaneous EEG/fMRI analysis: methodology and simulation

Concurrent EEG/fMRI recordings represent multiple, simultaneously active, regionally overlapping neuronal mass responses. To address the problems caused by the overlapping nature of these responses, we propose a parallel framework for Spatial-Temporal EEG/fMRI Fusion (STEFF). This technique adopts Independent Component Analysis (ICA) to recover the time-course and spatial mapping components from EEG and fMRI separately. These components are then linked concurrently in the spatial and temporal domain using an Empirical Bayesian (EB) model. This approach enables information one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. Consequently, STEFF achieves flexible and sparse matching among EEG and fMRI components with common neuronal substrates. Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain.

Simultaneous EEG-fMRI in patients with Unverricht-Lundborg disease: event-related desynchronization/synchronization and hemodynamic response analysis

We performed simultaneous acquisition of EEG-fMRI in seven patients with Unverricht-Lundborg disease (ULD) and in six healthy controls using self-paced finger extension as a motor task. The event-related desynchronization/synchronization (ERD/ERS) analysis showed a greater and more diffuse alpha desynchronization in central regions and a strongly reduced post-movement beta-ERS in patients compared with controls, suggesting a significant dysfunction of the mechanisms regulating active movement and movement end. The event-related hemodynamic response obtained from fMRI showed delayed BOLD peak latency in the contralateral primary motor area suggesting a less efficient activity of the neuronal populations driving fine movements, which are specifically impaired in ULD.

Bayesian quantitative electrophysiology and its multiple applications in bioengineering

Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bioengineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.

Detection of irregular, transient fMRI activity in normal controls using 2dTCA: comparison to event-related analysis using known timing

When events occur spontaneously during the acquisition of a series of images, traditional modeling methods for detecting functional MRI activation detection cannot be employed. The two-dimensional temporal clustering algorithm, 2dTCA, has been shown to accurately detect random, transient activations in computer simulations without the use of known event timings. In this study we applied the 2dTCA technique to detect the timings and spatial locations of sparse, irregular, transient activations of the visual, auditory, and motor cortices in 12 normal controls. Experiments with one and two independent types of stimuli were employed. Event-related activation using known timing was compared with event-related activation using 2dTCA-detected timing in individuals and across groups. The 2dTCA algorithm detected the activation from all presented stimuli in every subject. When compared with block-design results using a measure of correlation between activation maps, no significant difference was found between the 2dTCA activation maps and the event-related maps using known timing across all subjects. Therefore, 2dTCA has the potential to be an accurate and more practical method for detection of spontaneous, transient events using fMRI.

Model driven EEG/fMRI fusion of brain oscillations

This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of forward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian computations the practical estimation of very large scale EEG/fMRI models shall soon be possible.

Brain Networks Involved in Early versus Late Response Anticipation and Their Relation to Conflict Processing

Previous electrophysiological studies have clearly identified separable neural events underlying early and late components of response anticipation. Functional neuroimaging studies, however, have so far failed to account for this separation. Here, we performed functional magnetic resonance imaging (fMRI) of an anticipation paradigm in 12 healthy adult subjects that reliably produced early and late expectancy waves in the electroencephalogram. We furthermore compared fMRI activations elicited during early and late anticipation to those associated with response conflict. Our results demonstrate the existence of distinct cortical and subcortical brain regions underlying early and late anticipation. Although late anticipatory behavior was associated with activations in dorsal ACC, frontal cortex, and thalamus, brain responses linked to the early expectancy wave were localized mainly in motor and premotor cortical areas as well as the caudate nucleus. Additionally, late anticipation was associated with increased activity in midbrain dopaminergic nuclei, very likely corresponding to the substantia nigra. Furthermore, whereas regions involved in late anticipation proved to be very similar to activations elicited by response conflict, this was not the case for early anticipation. The current study supports a distinction between early and late anticipatory processes, in line with a plethora of neurophysiological work, and for the first time describes the brain structures differentially involved in these processes.