Simultaneous recording of EEG and BOLD responses: A historical perspective

A spontaneous dissociative episode during an EEG experiment

A depersonalization episode occurred unexpectedly during an electroencephalogram (EEG) recording for a study. Experience reports tracked the time course of this event and, in conjunction, with EEG data, were analyzed. The source activity across canonical frequency bands was analyzed across four periods ended by retrospective experience reports (depersonalization was reported in the 2nd period). Delta and theta decreases occurred across all time periods with no relation to reported events. Theta and alpha increases occurred in right secondary visual areas following depersonalization, which also coincided with surges in beta and gamma. The largest increases occurred in bilateral fronto-polar and medial prefrontal cortex, followed by inferior left lateral fronto-insula-temporal cortices and right secondary visual cortex. A high frequency functional network with a principal hub in left insula closely overlapped inferior left cortical gamma band-power increases. Bilateral frontal increases in gamma are consistent with studies of dissociation. We interpret gamma and later beta, alpha, and theta band increases as arising from the generation of visual priors, in the absence of precise visual signals, which constrain interoceptive and proprioceptive predictions to reestablish a stable sense of physiological-self. Beta showed local increases following the pattern of gamma but showed no changes in functional connectivity.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

Recent advances in imaging techniques of renal masses

Multiphasic multidetector computed tomography (CT) forms the mainstay for the characterization of renal masses whereas magnetic resonance imaging (MRI) acts as a problem-solving tool in some cases. However, a few of the renal masses remain indeterminate even after evaluation by conventional imaging methods. To overcome the deficiency in current imaging techniques, advanced imaging methods have been devised and are being tested. This review will cover the role of contrast-enhanced ultrasonography, shear wave elastography, dual-energy CT, perfusion CT, MR perfusion, diffusion-weighted MRI, blood oxygen level-dependent MRI, MR spectroscopy, positron emission tomography (PET)/prostate-specific membrane antigen-PET in the characterization of renal masses.

Segmenting electroencephalography wires reduces radiofrequency shielding artifacts in simultaneous electroencephalography and functional magnetic resonance imaging at 7 T

Purpose

Simultaneous scalp electroencephalography and functional magnetic resonance imaging (EEG‐fMRI) enable noninvasive assessment of brain function with high spatial and temporal resolution. However, at ultra‐high field, the data quality of both modalities is degraded by mutual interactions. Here, we thoroughly investigated the radiofrequency (RF) shielding artifact of a state‐of‐the‐art EEG‐fMRI setup, at 7 T, and design a practical solution to limit this issue.

Methods

Electromagnetic field simulations and MR measurements assessed the shielding effect of the EEG setup, more specifically the EEG wiring. The effectiveness of segmenting the wiring with resistors to reduce the transmit field disruption was evaluated on a wire‐only EEG model and a simulation model of the EEG cap.

Results

The EEG wiring was found to exert a dominant effect on the disruption of the transmit field, whose intensity varied periodically as a function of the wire length. Breaking the electrical continuity of the EEG wires into segments shorter than one quarter RF wavelength in air (25 cm at 7 T) reduced significantly the RF shielding artifacts. Simulations of the EEG cap with segmented wires indicated similar improvements for a moderate increase of the power deposition.

Conclusion

We demonstrated that segmenting the EEG wiring into shorter lengths using commercially available nonmagnetic resistors is effective at reducing RF shielding artifacts in simultaneous EEG‐fMRI. This prevents the formation of RF‐induced standing waves, without substantial specific absorption rate (SAR) penalties, and thereby enables benefiting from the functional sensitivity boosts achievable at ultra‐high field.

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

Functional neuroimaging in hearing research and audiology

The various methods of medical imaging are essential for many diagnostic issues in clinical routine, e.g., for the diagnostics and localisation of tumorous diseases, or for the clarification of other lesions in the central nervous system. In addition to these classical roles both positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for the investigation of functional processes in the human brain, when used in a specific way. The last 25 years have seen great progress, especially with respect to functional MRI, in terms of the available experimental paradigms as well as the data analysis strategies, so that a directed investigation of neurophysiological correlates of psychoacoustic performance is possible. This covers fundamental measures of sound perception like loudness and pitch, specific audiological symptoms like tinnitus, which often accompanies hearing disorders, but it also includes experiments on speech perception or on virtual acoustic environments. One important aspect common to many auditory neuroimaging studies is the central question at what stage in the human auditory pathway the sensory coding of the incoming sound is transformed into a universal and context-dependent perceptual representation, which is the basis for what we hear. This overview summarises findings from the literature as well as a few studies from our lab, to discuss the possibilities and the limits of the adoption of functional neuroimaging methods in audiology. Up to this stage, most auditory neuroimaging studies have investigated basic processes in normal hearing listeners. However, the hitherto existing results suggest that the methods of auditory functional neuroimaging - possibly complemented by electrophysiological methods like EEG and MEG - have a great potential to contribute to a deeper understanding of the processes and the impact of hearing disorders.

Clarifying Problems in Behavioural Control: Interface, Lateness and Consciousness

The target paper highlights a number of unresolved issues that, I believe, continue to impede the construction of a viable model of behavioural control in personality psychology; namely, (a) the relationship between controlled and automatic processing (the ‘interface’ problem’) and (b) the time it takes for controlled processes, including consciousness, to be generated (the ‘lateness’ problem). The diversity of views expressed in the commentatories indicates that these are, indeed, real and unresolved problems. This response is structured around the following key questions. (1) How long–term goal planning interfaces with the automatic machinery of behaviour? (2) The extent of the impact of the ‘lateness’ of controlled (including conscious) processes for building models of behavioural control? (3) How best to characterise the personality traits associated with the FFFS, BIS and BAS? (4) How does the BIS control mismatch detection, the generation of error signals, and response inhibition and switching? (5) Is consciousness really a necessary explanatory construct in models of behavioural control? (6) Might neural ‘crosstalk’ of encapsulated action–goal response systems point to the functional significance of consciousness? (7) What are the implications of issues raised in the target paper for lexical and social–cognitive approaches to personality? I conclude by re–iterating the importance of the problems of ‘lateness’ and ‘interface’ for the construction of a viable model of behavioural control sufficient for the fostering of theoretical integration within personality psychology as well as affording the building of conceptual bridges with general psychology. Copyright © 2010 John Wiley & Sons, Ltd.

Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T

This study investigates radiofrequency (RF)-induced heating in a head model with a 256-channel electroencephalogram (EEG) cap during magnetic resonance imaging (MRI). Nine computational models were implemented each with different EEG lead electrical conductivity, ranging from 1 to 5.8 × 107 S/m. The peak values of specific absorption rate (SAR) averaged over different volumes were calculated for each lead conductivity. Experimental measurements were also performed at 3-T MRI with a Gracilaria Lichenoides (GL) phantom with and without a low-conductive EEG lead cap ("InkNet"). The simulation results showed that SAR was a nonlinear function of the EEG lead conductivity. The experimental results were in line with the numerical simulations. Specifically, there was a ΔT of 1.7 °C in the GL phantom without leads compared to ΔT of 1.8 °C calculated with the simulations. Additionally, there was a ΔT of 1.5 °C in the GL phantom with the InkNet compared to a ΔT of 1.7 °C in the simulations with a cap of similar conductivity. The results showed that SAR is affected by specific location, number of electrodes, and the volume of tissue considered. As such, SAR averaged over the whole head, or even SAR averaged over volumes of 1 or 0.1 g, may conceal significant heating effects and local analysis of RF heating (in terms of peak SAR and temperature) is needed.

Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials

Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus.

Incremental benefits of EEG informed fMRI in the study of disorders related to meso-corticolimbic dopamine pathway dysfunction: A systematic review of recent literature

Functional magnetic resonance imaging (fMRI) is a non-invasive imaging modality that enables the assessment of neural connectivity and oxygen utility of the brain using blood oxygen level dependent (BOLD) imaging sequence. Electroencephalography (EEG), on the other hands, looks at cortical electrical impulses of the brain thus detecting brainwave patterns during rest and thought processing. The combination of these two modalities is called fMRI with simultaneous EEG (fMRI-EEG), which has emerged as a new tool for experimental neuroscience assessments and has been applied clinically in many settings, most commonly in epilepsy cases. Recent advances in imaging has led to fMRI-EEG being utilized in behavioural studies which can help in giving an objective assessment of ambiguous cases and help in the assessment of response to treatment by providing a non-invasive biomarker of the disease processes. We aim to review the role and interpretation of fMRI-EEG in studies pertaining to psychiatric disorders and behavioral abnormalities.

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG-fMRI recordings

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady‐state‐visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase‐reversing Gabor patches. We test the hypothesis that fluctuations in visuo‐cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC‐based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG‐fMRI covariance. Furthermore xMCC converged with other extant methods for EEG‐fMRI analysis.

Cue-induced changes in the stability of finger force-production tasks revealed by the uncontrolled manifold analysis

A motor system configured to maximize the stability of its current state cannot dexterously transition between states. Yet, we routinely resolve the stability-dexterity conflict and rapidly change our current behavior without allowing it to become unstable before the desired transition. The phenomenon called anticipatory synergy adjustment (ASA) partly describes how the central nervous system handles this conflict. ASA is a continuous decrease in the stability of the current motor state beginning 150–400 ms before a rapid state transition accomplished using redundant sets of motor inputs (more input variables than task-specific output variables). So far, ASAs have been observed only when the timing of the upcoming transition is known. We utilized a multifinger, isometric force-production task to demonstrate that compared with a condition where no state transition is expected, the stability of the current state is lower by ~12% when a participant is cued to make a transition, even when the nature and timing of that transition are unknown. This result (stage 1 ASA) is distinct from its traditional version (stage 2 ASA), and it describes early destabilization that occurs solely in response to the expectation to move. Stage 2 ASA occurs later, only if the timing of the transition is known sufficiently in advance. Stage 1 ASA lasts much longer (~1.5 s) and may scale in response to the perceived difficulty of the upcoming task. Therefore, this work reveals a much refined view of the processes that underlie the resolution of the stability-dexterity conflict.

NEW & NOTEWORTHY We compared the stability of multifinger, isometric force-production tasks for trials in which force changes of unknown direction and timing were expected with trials in which there was no expectation of any force change. Mere expectation of a change caused the stability of the current motor state to drop. This novel result provides a much refined view of the processes that facilitate dexterous switching between motor states.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

The Effect of Apolipoprotein E ε4 (APOE ε4) on Visuospatial Working Memory in Healthy Elderly and Amnestic Mild Cognitive Impairment Patients: An Event-Related Potentials Study

Background: Apolipoprotein E (APOE) ε4 is the only established risk gene for late-onset, sporadic Alzheimer’s disease (AD). Previous studies have provided inconsistent evidence for the effect of APOE ε4 status on the visuospatial working memory (VSWM).

Objective: The aim was to investigate the effect of APOE ε4 on VSWM with an event-related potential (ERP) study in healthy controls (HC) and amnestic mild cognitive impairment (aMCI) patients.

Methods: The study recorded 39 aMCI patients (27 APOE ε4 non-carriers and 12 APOE ε4 carriers) and their 43 matched controls (25 APOE ε4 non-carriers and 18 APOE ε4 carriers) with an 64-channel electroencephalogram. Participants performed an N-back task, a VSWM paradigm that manipulated the number of items to be stored in memory.

Results: The present study detected reduced accuracy and delayed mean correct response time (RT) in aMCI patients compared to HC. P300, a positive component that peaks between 300 and 500 ms, was elicited by the VSWM task. In addition, aMCI patients showed decreased P300 amplitude at the central–parietal (CP1, CPz, and CP2) and parietal (P1, Pz, and P2) electrodes in 0- and 1-back task compared to HC. In both HC and aMCI patients, APOE ε4 carriers showed reduced P300 amplitude with respect to non-carriers, whereas no significant differences in accuracy or RT were detected between APOE ε4 carriers and non-carriers. Additionally, standardized low-resolution brain electromagnetic tomography analysis (s-LORETA) showed enhanced brain activation in the right parahippocampal gyrus (PHG) during P300 time range in APOE ε4 carriers with respect to non-carriers in aMCI patients.

Conclusion: It demonstrated that P300 amplitude could predict VSWM deficits in aMCI patients and contribute to early detection of VSWM deficits in APOE ε4 carriers.

[Disorders of the will in psychopathology]

At the beginning of modern psychopathology the notion of the will had a high significance. Thus, the works of Eugen Bleuler, Emil Krapelin and Karl Jaspers show an intensive study of disorders of the will, such as abulia, ambivalence or disorders of impulse control. Retrospectively, changes of the scientific paradigms in psychology could be one of the reasons for a break, which led to giving up the concept of the will in psychopathology. With increasing interest in issues of agency and free will, however, a reactivation of this central concept could close a gap in psychopathology as well as in therapeutic practice. Methodologically, a psychopathology of the will may be founded on a differential typological phenomenology. To this purpose, the article first proposes a classification along the structural components of conation, suspension and volition, then gives a temporal analysis of the predecisional, the decisional and the postdecisional phases. The aim of the article is to help identify different disorders of the will, thus also furthering a psychotherapy of will, which can be connected with both cognitive behavioral and psychodynamic approaches.

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.

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.

A randomized controlled trial into the effects of neurofeedback, methylphenidate, and physical activity on EEG power spectra in children with ADHD

BACKGROUND

The clinical and neurophysiological effects of neurofeedback (NF) as treatment for children with ADHD are still unclear. This randomized controlled trial (RCT) examined electroencephalogram (EEG) power spectra before and after NF compared to methylphenidate (MPH) treatment and physical activity (PA) - as semi-active control group - during resting and active (effortful) task conditions to determine whether NF can induce sustained alterations in brain function.

METHODS

Using a multicentre three-way parallel group RCT design, 112 children with a DSM-IV diagnosis of ADHD, aged between 7 and 13 years, were initially included. NF training consisted of 30 sessions of theta/beta training at Cz over a 10-week period. PA training was a semi-active control group, matched in frequency and duration. Methylphenidate was titrated using a double-blind placebo controlled procedure in 6 weeks, followed by a stable dose for 4 weeks. EEG power spectra measures during eyes open (EO), eyes closed (EC) and task (effortful) conditions were available for 81 children at pre- and postintervention (n = 29 NF, n = 25 MPH, n = 27 PA).

CLINICAL TRIALS REGISTRATION

Train Your Brain? Exercise and Neurofeedback Intervention for ADHD, https://clinicaltrials.gov/show/;NCT01363544, Ref. No. NCT01363544.

RESULTS

Both NF and MPH resulted in comparable reductions in theta power from pre- to postintervention during the EO condition compared to PA (ηp (2)  = .08 and .12). For NF, greater reductions in theta were related to greater reductions in ADHD symptoms. During the task condition, only MPH showed reductions in theta and alpha power compared to PA (ηp (2)  = .10 and .12).

CONCLUSIONS

This study provides evidence for specific neurophysiological effects after theta/beta NF and MPH treatment in children with ADHD. However, for NF these effects did not generalize to an active task condition, potentially explaining reduced behavioural effects of NF in the classroom.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality

PURPOSE

To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI.

METHODS

Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner.

RESULTS

Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets.

CONCLUSION

High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Reliability of information-based integration of EEG and fMRI data: a simulation study

Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.

MRI with and without a high-density EEG cap--what makes the difference?

Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis.

Application of partial directed coherence to the analysis of resting-state EEG-fMRI data

The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.

Psychophysiology-informed (multimodal) imaging

Electroencephalography (EEG) and magnetic resonance imaging are two popular methodologies for brain research. While EEG has a high temporal resolution, yet a low spatial resolution, MRI has the complete opposite, a high spatial resolution, yet a low temporal resolution. Obviously therefore, researchers have been searching for ways combining the two methodologies, for more than two decades. However, there are many issues that have to be solved before the methodologies can be successfully and, more importantly reliably, combined. Here, we give an overview of these issues, and present strategies that have been used over the past two decades to overcome them. We start with a general description of EEG and (f)MRI methodology, then present the difficulties involved in combining both methodologies, and lastly present and discuss the most popular strategies that have been used over the past two decades to solve these problems. We conclude that in spite of the many issues, the two methodologies can be combined successfully, provided that the correct procedures are followed.

Simultaneous EEG-fMRI brain signatures of auditory cue utilization

Optimal utilization of acoustic cues during auditory categorization is a vital skill, particularly when informative cues become occluded or degraded. Consequently, the acoustic environment requires flexible choosing and switching amongst available cues. The present study targets the brain functions underlying such changes in cue utilization. Participants performed a categorization task with immediate feedback on acoustic stimuli from two categories that varied in duration and spectral properties, while we simultaneously recorded Blood Oxygenation Level Dependent (BOLD) responses in fMRI and electroencephalograms (EEGs). In the first half of the experiment, categories could be best discriminated by spectral properties. Halfway through the experiment, spectral degradation rendered the stimulus duration the more informative cue. Behaviorally, degradation decreased the likelihood of utilizing spectral cues. Spectrally degrading the acoustic signal led to increased alpha power compared to nondegraded stimuli. The EEG-informed fMRI analyses revealed that alpha power correlated with BOLD changes in inferior parietal cortex and right posterior superior temporal gyrus (including planum temporale). In both areas, spectral degradation led to a weaker coupling of BOLD response to behavioral utilization of the spectral cue. These data provide converging evidence from behavioral modeling, electrophysiology, and hemodynamics that (a) increased alpha power mediates the inhibition of uninformative (here spectral) stimulus features, and that (b) the parietal attention network supports optimal cue utilization in auditory categorization. The results highlight the complex cortical processing of auditory categorization under realistic listening challenges.

Simultaneous EEG-fMRI: trial level spatio-temporal fusion for hierarchically reliable information discovery

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been pursued in an effort to integrate complementary noninvasive information on brain activity. The primary goal involves better information discovery of the event-related neural activations at a spatial region of the BOLD fluctuation with the temporal resolution of the electrical signal. Many techniques and algorithms have been developed to integrate EEGs and fMRIs; however, the relative reliability of the integrated information is unclear. In this work, we propose a hierarchical framework to ensure the relative reliability of the integrated results and attempt to understand brain activation using this hierarchical ideal. First, spatial Independent Component Analysis (ICA) of fMRI and temporal ICA of EEG were performed to extract features at the trial level. Second, the maximal information coefficient (MIC) was adopted to temporally match them across the modalities for both linear and non-linear associations. Third, fMRI-constrained EEG source imaging was utilized to spatially match components across modalities. The simultaneously occurring events in the above two match steps provided EEG-fMRI spatial-temporal reliable integrated information, resulting in the most reliable components with high spatial and temporal resolution information. The other components discovered in the second or third steps provided second-level complementary information for flexible and cautious explanations. This paper contains two simulations and an example of real data, and the results indicate that the framework is a feasible approach to reveal cognitive processing in the human brain.

General overview on the merits of multimodal neuroimaging data fusion

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.

The dual facet of gamma oscillations: separate visual and decision making circuits as revealed by simultaneous EEG/fMRI

It remains an outstanding question whether gamma-band oscillations reflect unitary cognitive processes within the same task. EEG/MEG studies do lack the resolution or coverage to address the highly debated question whether single gamma activity patterns are linked with multiple cognitive modules or alternatively each pattern associates with a specific cognitive module, within the same coherent perceptual task. One way to disentangle these issues would be to provide direct identification of their sources, by combining different techniques. Here, we directly examined these questions by performing simultaneous EEG/fMRI using an ambiguous perception paradigm requiring holistic integration. We found that distinct gamma frequency sub-bands reflect different neural substrates and cognitive mechanisms when comparing object perception states vs. no categorical perception. A low gamma sub-band (near 40 Hz) activity was tightly related to the decision making network, and in particular the anterior insula. A high gamma sub-band (∼60 Hz) could be linked to early visual processing regions. The demonstration of a clear functional topography for distinct gamma sub-bands within the same task shows that distinct gamma-band modulations underlie sensory processing and perceptual decision mechanisms.

[Simultaneous EEG-fMRI measurements: insights in applications and challenges]

BACKGROUND

The following article presents an introduction to simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) measurements which have undergone a huge development during the last few years.

OBJECTIVES

The idea behind combining both non-invasive methods is to join the excellent temporal resolution of EEG (ms) together with the superior spatial resolution of fMRI (mm). In this article the status quo of the method and perspectives regarding multimodal imaging are discussed.

MATERIAL AND METHODS

Simultaneous EEG-fMRI measurements are affected by scanner and cardioballistic artifacts. We present common artifact subtraction methods in order to achieve a feasible data quality and outline what to consider when planning and recording EEG and fMRI simultaneously. Moreover, we discuss different analysis strategies.

RESULTS

Combined EEG-fMRI measurements have already increased our knowledge about the underlying relationships between the blood oxygenation level-dependent (BOLD) response and the EEG signal and are applied to answer widespread research questions. Simultaneous measurements are an essential part of multimodal imaging in investigating the underlying processing mechanisms of the brain as well as in advancing our understanding of neuropsychiatric diseases.

CONCLUSIONS

Current developments in multimodal imaging focus on the combination of electrophysiological and MRI parameters within ultra-high field MRI as well as on positron emission tomography (PET) in a trimodal approach.

Towards neural correlates of auditory stimulus processing: a simultaneous auditory evoked potentials and functional magnetic resonance study using an odd-ball paradigm

Background

The neural underpinnings of auditory information processing have often been investigated using the odd-ball paradigm, in which infrequent sounds (deviants) are presented within a regular train of frequent stimuli (standards). Traditionally, this paradigm has been applied using either high temporal resolution (EEG) or high spatial resolution (fMRI, PET). However, used separately, these techniques cannot provide information on both the location and time course of particular neural processes. The goal of this study was to investigate the neural correlates of auditory processes with a fine spatio-temporal resolution. A simultaneous auditory evoked potentials (AEP) and functional magnetic resonance imaging (fMRI) technique (AEP-fMRI), together with an odd-ball paradigm, were used.

Material/Methods

Six healthy volunteers, aged 20–35 years, participated in an odd-ball simultaneous AEP-fMRI experiment. AEP in response to acoustic stimuli were used to model bioelectric intracerebral generators, and electrophysiological results were integrated with fMRI data.

Results

fMRI activation evoked by standard stimuli was found to occur mainly in the primary auditory cortex. Activity in these regions overlapped with intracerebral bioelectric sources (dipoles) of the N1 component. Dipoles of the N1/P2 complex in response to standard stimuli were also found in the auditory pathway between the thalamus and the auditory cortex. Deviant stimuli induced fMRI activity in the anterior cingulate gyrus, insula, and parietal lobes.

Conclusions

The present study showed that neural processes evoked by standard stimuli occur predominantly in subcortical and cortical structures of the auditory pathway. Deviants activate areas non-specific for auditory information processing.

Impaired auditory sampling in dyslexia: further evidence from combined fMRI and EEG

The aim of the present study was to explore auditory cortical oscillation properties in developmental dyslexia. We recorded cortical activity in 17 dyslexic participants and 15 matched controls using simultaneous EEG and fMRI during passive viewing of an audiovisual movie. We compared the distribution of brain oscillations in the delta, theta and gamma ranges over left and right auditory cortices. In controls, our results are consistent with the hypothesis that there is a dominance of gamma oscillations in the left hemisphere and a dominance of delta-theta oscillations in the right hemisphere. In dyslexics, we did not find such an interaction, but similar oscillations in both hemispheres. Thus, our results confirm that the primary cortical disruption in dyslexia lies in a lack of hemispheric specialization for gamma oscillations, which might disrupt the representation of or the access to phonemic units.

EEG-fMRI integration for the study of human brain function

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.

A modified oddball paradigm for investigation of neural correlates of attention: a simultaneous ERP-fMRI study

Introduction

The objective of the presented study was to develop and evaluate a P300 experimental protocol for simultaneous registration of event-related potentials (ERPs) and functional MRI (fMRI) data with continuous imaging. It may be useful for investigating attention and working memory processes in specific populations, such as children and neuropsychiatric patients.

Materials and methods

Eleven children were investigated with simultaneous ERP–fMRI. To fulfill requirements of both BOLD and electroencephalographic signal registration, a modified oddball task was used. To verify the ERP–fMRI protocol we also performed a study outside the scanner using a typical two-stimuli oddball paradigm.

Results

Localization of the P300 component of ERPs partially corresponded with fMRI results in the frontal and parietal brain regions. FMRI activations were found in: middle frontal gyrus, insula, SMA, parietal lobule, thalamus, and cerebellum. Our modified oddball task provided ERP–fMRI results with high level of significance (EEG SNR = 35, fMRI p < 0.05–Bonf.). ERPs obtained in the scanner were comparable with those registered outside the scanner, although some differences in the amplitude were noticed, mainly in the N100 component.

Conclusion

In our opinion the presented paradigm may be successfully applied for simultaneous ERP–fMRI registration of neural correlates of attention in vulnerable populations.

Does the brain “initiate” freely willed processes? A philosophy of science critique of Libet-type experiments and their interpretation

In the extensive, recent debates on free will, the pioneering experiments by Benjamin Libet continue to play a significant role. It is often claimed that these experiments demonstrate the illusory nature of freely willed actions. In this article, we provide a detailed analysis and evaluation of Libet’s experiments from a philosophy of science perspective. Our analysis focuses on Libet’s central notion of the “initiation” of freely willed processes by the brain. We examine four interpretations of the notion of initiation: in terms of a cause, a necessary condition, a correlation, and a regular succession. We argue that none of these four interpretations can be supported by the design and results of Libet’s experiments. In addition, we analyze two recent Libet-type experiments. Our general conclusion is that neither Libet’s original experiments nor later Libet-type experiments can justify the claim that allegedly freely willed processes are in fact initiated by the brain.

Methods for simultaneous EEG-fMRI: an introductory review

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

The neocortical network representing associative memory reorganizes with time in a process engaging the anterior temporal lobe

During encoding, the distributed neocortical representations of memory components are presumed to be associatively linked by the hippocampus. With time, a reorganization of brain areas supporting memory takes place, which can ultimately result in memories becoming independent of the hippocampus. While it is theorized that with time, the neocortical representations become linked by higher order neocortical association areas, this remains to be experimentally supported. In this study, 24 human participants encoded sets of face-location associations, which they retrieved 1 or 25 h later ("recent" and "remote" conditions, respectively), while their brain activity was recorded using whole-head magnetoencephalography. We investigated changes in the functional interactions between the neocortical representational areas emerging over time. To assess functional interactions, trial-by-trial high gamma (60-140 Hz) power correlations were calculated between the neocortical representational areas relevant to the encoded information, namely the fusiform face area (FFA) and posterior parietal cortex (PPC). With time, both the FFA and the PPC increased their functional interactions with the anterior temporal lobe (ATL). Given that the ATL is involved in semantic representation of paired associates, our results suggest that, already within 25 h after acquiring new memory associations, neocortical functional links are established via higher order semantic association areas.

Simultaneous electroencephalography-functional MRI at 3 T: an analysis of safety risks imposed by performing anatomical reference scans with the EEG equipment in place

PURPOSE

To describe heating effects to be expected in simultaneous electroencephalography (EEG) and magnetic resonance imaging (MRI) when deviating from the EEG manufacturer's instructions; to test which anatomical MRI sequences have a sufficiently low specific absorption rate (SAR) to be performed with the EEG equipment in place; and to suggest precautions to reduce the risk of heating.

MATERIALS AND METHODS

Heating was determined in vivo below eight EEG electrodes, using both head and body coil transmission and sequences covering the whole range of SAR values.

RESULTS

Head transmit coil: temperature increases were below 2.2°C for low SAR sequences, but reached 4.6°C (one subject, clavicle) for high SAR sequences; the equilibrium temperature T(eq) remained below 39°C. Body transmit coil: temperature increases were higher and more frequent over subjects and electrodes, with values below 2.6°C for low SAR sequences, reaching 6.9°C for high SAR sequences (T8 electrode) with T(eq) exceeding a critical level of 40°C.

CONCLUSION

Anatomical imaging should be based on T1-weighted sequences (FLASH, MPRAGE, MDEFT) with an SAR below values for functional MRI sequences based on gradient echo planar imaging. Anatomical sequences with a high SAR can pose a significant risk, which is reduced by using head coil transmission.

Effect of the static magnetic field of the MR-scanner on ERPs: evaluation of visual, cognitive and motor potentials

OBJECTIVE

This work investigates the influence of the static magnetic field of the MR-scanner on ERPs extracted from simultaneous EEG-fMRI recordings. The quality of the ERPs after BallistoCardioGraphic (BCG) artifact removal, as well as the reproducibility of the waveforms in different environments is investigated.

METHODS

We consider a Detection, a Go-Nogo and a Motor task, eliciting peaks that differ in amplitude, latency and scalp topography, repeated in two situations: outside the scanner room (0T) and inside the MR-scanner but without gradients (3T). The BCG artifact is removed by means of three techniques: the Average Artifact Subtraction (AAS) method, the Optimal Basis Set (OBS) method and the Canonical Correlation Analysis (CCA) approach.

RESULTS

The performance of the three methods depends on the amount of averaged trials. Moreover, differences are found on both amplitude and latency of ERP components recorded in two environments (0T vs 3T).

CONCLUSIONS

We showed that, while ERPs can be extracted from simultaneous EEG-fMRI data at 3T, the static magnetic field might affect the physiological processes under investigation.

SIGNIFICANCE

The reproducibility of the ERPs in different recording environments (0T vs 3T) is a relevant issue that deserves further investigation to clarify the equivalence of cognitive processes in both behavioral and imaging studies.

Cross-correlation of motor activity signals from dc-magnetoencephalography, near-infrared spectroscopy, and electromyography

Neuronal and vascular responses due to finger movements were synchronously measured using dc-magnetoencephalography (dcMEG) and time-resolved near-infrared spectroscopy (trNIRS). The finger movements were monitored with electromyography (EMG). Cortical responses related to the finger movement sequence were extracted by independent component analysis from both the dcMEG and the trNIRS data. The temporal relations between EMG rate, dcMEG, and trNIRS responses were assessed pairwise using the cross-correlation function (CCF), which does not require epoch averaging. A positive lag on a scale of seconds was found for the maximum of the CCF between dcMEG and trNIRS. A zero lag is observed for the CCF between dcMEG and EMG. Additionally this CCF exhibits oscillations at the frequency of individual finger movements. These findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.

Simultaneously recorded EEG-fMRI: removal of gradient artifacts by subtraction of head movement related average artifact waveforms

Electroencephalograms (EEGs) recorded simultaneously with functional magnetic resonance imaging (fMRI) are corrupted by large repetitive artifacts generated by the switched MR gradients. Several methods have been proposed to remove these distortions by subtraction of averaged artifact templates from the ongoing EEG. Here, we present a modification of this approach which accounts for head movements to improve the extracted template. Using the fMRI analysis package statistical parametric mapping (SPM; FIL London) the head displacement is determined at each half fMRI-volume. The basic idea is to apply a moving average algorithm for template extraction but to include only epochs that were obtained at the same head position as the artefact to be removed. This approach was derived from phantom EEG measurements demonstrating substantial variations of the artefact waveform in response to movements of the phantom in the MRI magnet. To further reduce the residual noise, we applied a resampling algorithm which aligns the EEG samples in a strict adaptive manner to the fMRI timing. Finally, we propose a new algorithm to suppress residual artifacts such as those occasionally observed in case of brief strong movements, which are not reflected by the movement indicator because of the limited temporal resolution of the fMRI sequence. On the basis of EEG recordings of six subjects these measures combined reduce the residual artefact activity quantified in terms of the spectral power at the gradient repetition rate and its harmonics by roughly 20 to 50% (depending on the amount of movement) predominantly in frequencies beyond 30 Hz.

Physiological recordings: basic concepts and implementation during functional magnetic resonance imaging

Combining human functional neuroimaging with other forms of psychophysiological measurement, including autonomic monitoring, provides an empirical basis for understanding brain-body interactions. This approach can be applied to characterize unwanted physiological noise, examine the neural control and representation of bodily processes relevant to health and morbidity, and index covert expression of affective and cognitive processes to enhance the interpretation of task-evoked regional brain activity. In recent years, human neuroimaging has been dominated by functional magnetic resonance imaging (fMRI) studies. The spatiotemporal information of fMRI regarding central neural activity is valuably complemented by parallel physiological monitoring, yet such studies still remain in the minority. This review article highlights fMRI studies that employed cardiac, vascular, respiratory, electrodermal, gastrointestinal and pupillary psychophysiological indices to address specific questions regarding interaction between brain and bodily state in the context of experience, cognition, emotion and behaviour. Physiological monitoring within the fMRI environment presents specific technical issues, most importantly related to safety. Mechanical and electrical hazards may present dangers to scanned subjects, operator and/or equipment. Furthermore, physiological monitoring may interfere with the quality of neuroimaging data, or itself be compromised by artefacts induced by the operation of the scanner. We review the sources of these potential problems and the current approaches and advice to enable the combination of fMRI and physiological monitoring in a safe and effective manner.

An information theoretic approach to EEG-fMRI integration of visually evoked responses

The integration of signals from electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), acquired simultaneously from the same observer, holds great potential for the elucidation of the neurobiological underpinnings of human brain function. However, the most appropriate way in which to combine the data in order to achieve this goal is not clear. Here, we apply a novel route to the integration of simultaneously acquired multimodal brain imaging data. We adopt a theoretical framework developed in the study of neuronal population codes which explicitly takes into account the experimentally observed stimulus-response signal probability distributions using the concept of mutual information. We study the implications of this framework using simulated data sets generated from a set of linear Gaussian models, and apply the framework to EEG-fMRI data acquired during checkerboard stimulation of low and high contrast. We focus our evaluation on single-trial time-domain signal features from both modalities and provide evidence for the informativeness of a subset of these features with respect to the stimulus and each other. Specifically, the framework was able to identify the contrast dependency of the haemodynamic response and the P100 peak of the visual evoked potential, and showed that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution was more informative than treating each one in isolation. In addition, the effect of different pre-processing strategies for EEG-fMRI data can be assessed quantitatively, indicating the improvements to be gained by more advanced methods. We conclude that the information theoretic framework is a promising methodology to quantify the relative importance of different response features in neural coding and neurovascular coupling, as well as the success of data pre-processing strategies.