Comparison of analytical strategies for EEG-correlated fMRI data in patients with epilepsy

The simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to localize interictal epileptiform discharges (IEDs). Previous studies have reported varying degrees of concordance of EEG-fMRI with electroclinical findings. The aim of the present study is to evaluate to what extent this variability is determined by the analytical strategy or by the properties of the EEG data. For that purpose, 42 IED sets obtained in 29 patients with epilepsy were reanalyzed using a finite impulse response approach, which estimates the hemodynamic response function (HRF) from the data and allows non-causal effects. Cardiac effects were treated as additional confounders in the model. This approach was compared to the classical approach assuming a fixed HRF for each voxel in the brain. The performance of each method was assessed by comparing the fMRI results to the EEG focus. The flexible model revealed more significantly activated voxels, which resulted in more activated brain regions concordant with the EEG focus (26 vs. 16). Correction for cardiac effects improved the results in 7 out of the 42 data sets. Furthermore, design theory for event-related experiments was applied in order to determine the influence of the number of IEDs and their temporal distribution on the success of an experiment. It appeared that this success is highly dependent upon the number of IEDs present during the recording and less on their temporal spacing. We conclude that the outcome of EEG-fMRI can be improved by using an optimized analytical strategy, but also depends on the number of IEDs occurring during the recording.     

Continuous EEG-fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG

Purpose

To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG–functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source.

Methods

Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG–fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges).

Results

In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal.

Conclusions

Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG–fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.     

EEG topography-specific BOLD changes: a continuous EEG-fMRI study in a patient with focal epilepsy

Blood oxygenation level dependent (BOLD) response related to interictal activity was evaluated in a patient with post-traumatic focal epilepsy at repeated continuous electroencephalogram (EEG)-functional magnetic resonance imaging examinations. Lateralized interictal EEG activity induced a main cluster of activation co-localized with the anatomical lesion. Spreading of EEG interictal activity to both frontal lobes evoked bilateral clusters of activation indicating that topography of BOLD response might depend on the spatial distribution of epileptiform activity.     

Assessment of spatial BOLD sensitivity variations in fMRI using gradient-echo field maps

Clinical blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is becoming increasingly valuable in, e.g., presurgical planning, but the commonly used gradient-echo echo-planar imaging (GE-EPI) technique is sometimes hampered by macroscopic field inhomogeneities. This can affect the degree of signal change that will occur in the GE-EPI images as a response to neural activation and the subsequent blood oxygenation changes, i.e., the BOLD sensitivity (BS). In this study, quantitative BS maps were calculated directly from gradient-echo field maps obtainable on most clinical scanners. In order to validate the accuracy of the calculated BS-maps, known shim gradients were applied and field maps and GE-EPI images of a phantom were acquired. Measured GE-EPI image intensity was then compared with the calculated (predicted) image intensity (pII) which was obtained from the field maps using theoretical expressions for image-intensity loss. The validated expressions for pII were used to calculate the corresponding predicted BOLD sensitivity (pBS) maps in healthy volunteers. Since the field map is assumed to be valid throughout an entire fMRI experiment, the influence of subject motion on the pBS maps was also assessed. To demonstrate the usefulness of such maps, pBS was investigated for clinically important functional areas including hippocampus, Broca's area and primary motor cortex. A systematic left/right pBS difference was observed in Broca's area and in the hippocampus, most likely due to magnetic field inhomogeneity of the particular MRI-system used in this study. For all subjects, the hippocampus showed pBS values above unity with a clear anterior–posterior gradient and with an abrupt drop to zero pBS in the anterior parts of hippocampus. It is concluded that GE field maps can be used to accurately predict BOLD sensitivity and that this parameter is useful to assess spatial variations which will influence fMRI experiments.     

A two-compartment gel phantom for optimization and quality assurance in clinical BOLD fMRI

Clinical applications of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) depend heavily on robust paradigms, imaging methods and analysis procedures. In this work, as a means to optimize and perform quality assurance of the entire imaging and analysis chain, a phantom that provides a well known and reproducible signal change similar to a block type fMRI experiment is presented. It consists of two gel compartments with slightly different T2 that dynamically enter and leave the imaged volume. The homogeneous gel in combination with a cylindrical geometry results in a well-defined T*2 difference causing a signal difference between the two compartments in T*2-weighted MR images. From time series data obtained with the phantom, maps of percent signal change (PSC) and t-values are calculated. As an example of image parameter optimisation, the phantom is demonstrated to be useful for accurate determination of the influence of echo time (TE) on BOLD fMRI results, taking the t-value as a measure of sensitivity. In addition, the phantom is proposed as a tool for quality assurance (QA) since reproducible time series and t-maps are obtained in a series of independent repeat experiments. The phantom is relatively simple to build and can therefore be used by any clinical fMRI center.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

In contrast to BOLD: signal enhancement by extravascular water protons as an alternative mechanism of endogenous fMRI signal change

Despite the popularity and widespread application of functional magnetic resonance imaging (fMRI) in recent years, the physiological bases of signal change are not yet fully understood. Blood oxygen level-dependant (BOLD) contrast — attributed to local changes in blood flow and oxygenation, and therefore magnetic susceptibility — has become the most prevalent means of functional neuroimaging. However, at short echo times, spin-echo sequences show considerable deviations from the BOLD model, implying a second, non-BOLD component of signal change. This has been dubbed “signal enhancement by extravascular water protons” (SEEP) and is proposed to result from proton-density changes associated with cellular swelling. Given that such changes are independent of magnetic susceptibility, SEEP may offer new and improved opportunities for carrying out fMRI in regions with close proximity to air–tissue and/or bone–tissue interfaces (e.g., the prefrontal cortex and spinal cord), as well as regions close to large blood vessels, which may not be ideally suited for BOLD imaging. However, because of the interdisciplinary nature of the literature, there has yet to be a thorough synthesis, tying together the various and sometimes disparate aspects of SEEP theory. As such, we aim to provide a concise yet comprehensive overview of SEEP, including recent and compelling evidence for its validity, its current applications and its future relevance to the rapidly expanding field of functional neuroimaging. Before presenting the evidence for a non-BOLD component of endogenous functional contrast, and to enable a more critical review for the nonexpert reader, we begin by reviewing the fundamental principles underlying BOLD theory.     

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals.     

Nonlinear blood oxygen level-dependent responses for transient activations and deactivations in V1 - insights into the hemodynamic response function with the balloon model

We report studies of the nonlinear nature of blood oxygen level-dependent (BOLD) responses to short transient deactivations in human visual cortex. Both functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) have been used to compare and contrast the hemodynamic response functions (HRFs) associated with transient activation and deactivation in primary visual cortex. We show that signal decreases for short duration deactivations are smaller than corresponding signal increases in activation studies. Moreover, the standard balloon model of BOLD effects may be modified to account for the observed nonlinear nature of deactivations by appropriate changes to simple hemodynamic parameters without recourse to new assumptions about the nature of the coupling between activity and oxygen use.     

A nonlinear BOLD model accounting for refractory effect by applying the longitudinal relaxation in NMR to the linear BOLD model

A mathematical model to regress the nonlinear blood oxygen level-dependent (BOLD) fMRI signal has been developed by incorporating the refractory effect into the linear BOLD model of the biphasic gamma variate function. The refractory effect was modeled as a relaxation of two separate BOLD capacities corresponding to the biphasic components of the BOLD signal in analogy with longitudinal relaxation of magnetization in NMR. When tested with the published fMRI data of finger tapping, the nonlinear BOLD model with the refractory effect reproduced the nonlinear BOLD effects such as reduced poststimulus undershoot and saddle pattern in a prolonged stimulation as well as the reduced BOLD signal for repetitive stimulation.     

Modelling large motion events in fMRI studies of patients with epilepsy

EEG-correlated fMRI can provide localisation information on the generators of epileptiform discharges in patients with focal epilepsy. To increase the technique's clinical potential, it is important to consider ways of optimising the yield of each experiment while minimizing the risk of false-positive activation. Head motion can lead to severe image degradation and result in false-positive activation and is usually worse in patients than in healthy subjects. We performed general linear model fMRI data analysis on simultaneous EEG-fMRI data acquired in 34 cases with focal epilepsy. Signal changes associated with large inter-scan motion events (head jerks) were modelled using modified design matrices that include 'scan nulling' regressors. We evaluated the efficacy of this approach by mapping the proportion of the brain for which F-tests across the additional regressors were significant. In 95% of cases, there was a significant effect of motion in 50% of the brain or greater; for the scan nulling effect, the proportion was 36%; this effect was predominantly in the neocortex. We conclude that careful consideration of the motion-related effects in fMRI studies of patients with epilepsy is essential and that the proposed approach can be effective.     

Signal fluctuations in fMRI data acquired with 2D-EPI and 3D-EPI at 7 Tesla

Segmented three-dimensional echo planar imaging (3D-EPI) provides higher image signal-to-noise ratio (SNR) than standard single-shot two-dimensional echo planar imaging (2D-EPI), but is more sensitive to physiological noise. The aim of this study was to compare physiological noise removal efficiency in single-shot 2D-EPI and segmented 3D-EPI acquired at 7 Tesla. Two approaches were investigated based either on physiological regressors (PR) derived from cardiac and respiratory phases, or on principal component analysis (PCA) using additional resting-state data. Results show that, prior to physiological noise removal, 2D-EPI data had higher temporal SNR (tSNR), while spatial SNR was higher in 3D-EPI. Blood oxygen level dependent (BOLD) sensitivity was similar for both methods. The PR-based approach allowed characterization of relative contributions from different noise sources, confirming significant increases in physiological noise from 2D to 3D prior to correction. Both physiological noise removal approaches produced significant increases in tSNR and BOLD sensitivity, and these increases were larger for 3D-EPI, resulting in higher BOLD sensitivity in the 3D-EPI than in the 2D-EPI data. The PCA-based approach was the most effective correction method, yielding higher tSNR values for 3D-EPI than for 2D-EPI postcorrection.     

Post-stimulus response in hemodynamics observed by functional magnetic resonance imaging--difference between the primary sensorimotor area and the supplementary motor area

The blood oxygen level dependency (BOLD) contrast is a useful tool for functional neuroimaging based on the hemodynamic response to neuronal activation. We observed different hemodynamic responses in the BOLD signal between the primary sensorimotor area (SM1) and the supplementary motor area (SMA) in the sequential finger movement task. In the SMA, a stronger initial overshoot and a post-stimulus overshoot were observed. It was hypothesized from the time course analysis that the stronger initial overshoot reflected the activation of the SMA for motor control programming in the initial phase. Although the post-stimulus overshoot may be partially explained by cerebral blood flow (CBF) cerebral blood volume (CBV) uncoupling, its mechanism remained unknown. In the SM1, only the initial overshoot was observed and the level of BOLD signal was almost constant after the initial overshoot during the task period. These observations suggested that the BOLD signal is characterized by both CBF-CBV uncoupling and the neuronal activation characteristics in each region.     

Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions

This paper investigates how well different kinds of fMRI functional connectivity analysis reflect the underlying interregional neural interactions. This is hard to evaluate using real experimental data where such relationships are unknown. Rather, we use a biologically realistic neural model to simulate both neuronal activities and multiregional fMRI data from a blocked design. Because we know how every element in the model is related to every other element, we can compare functional connectivity measurements across different spatial and temporal scales. We focus on (1) psycho-physiological interaction (PPI) analysis, which is a simple brain connectivity method that characterizes the activity in one brain region by the interaction between another region's activity and a psychological factor, and (2) interregional correlation analysis. We investigated the neurobiological underpinnings of PPI using simulated neural activities and fMRI signals generated by a large-scale neural model that performs a visual delayed match-to-sample task. Simulated fMRI data are generated by convolving integrated synaptic activities (ISAs) with a hemodynamic response function. The simulation was done under three task conditions: high-attention, low-attention and a control task ('passive viewing'). We investigated how biological and scanning parameters affect PPI and compared these with functional connectivity measures obtained using correlation analysis. We performed correlational and PPI analyses with three types of time-series data: ISA, fMRI and deconvolved fMRI (which yields estimated neural signals) obtained using a deconvolution algorithm. The simulated ISA can be considered as the 'gold standard' because it represents the underlying neural activity. Our main findings show (1) that evaluating the change in an interregional functional connection using the difference in regression coefficients (as is essentially done in the PPI method) produces results that better reflect the underlying changes in neural interrelationships than does evaluating the functional connectivity difference as a change in correlation coefficient; (2) that using fMRI and deconvolved fMRI data led to similar conclusions in the PPI-based functional connectivity results, and these generally agreed with the nature of the underlying neural interactions; and (3) the functional connectivity correlation measures often led to different conclusions regarding significance for different scanning and hemodynamic parameters, but the significances of the PPI regression parameters were relatively robust. These results highlight the way in which neural modeling can be used to help validate the inferences one can make about functional connectivity based on fMRI data.     

Quantification of venous blood signal contribution to BOLD functional activation in the auditory cortex at 3 T

Most modern techniques for functional magnetic resonance imaging (fMRI) rely on blood-oxygen-level-dependent (BOLD) contrast as the basic principle for detecting neuronal activation. However, the measured BOLD effect depends on a transfer function related to neurophysiological changes accompanying electrical neural activation. The spatial accuracy and extension of the region of interest are determined by vascular effect, which introduces incertitude on real neuronal activation maps. Our efforts have been directed towards the development of a new methodology that is capable of combining morphological, vascular and functional information; obtaining new insight regarding foci of activation; and distinguishing the nature of activation on a pixel-by-pixel basis. Six healthy volunteers were studied in a parametric auditory functional experiment at 3 T; activation maps were overlaid on a high-resolution brain venography obtained through a novel technique. The BOLD signal intensities of vascular and nonvascular activated voxels were analyzed and compared: it was shown that nonvascular active voxels have lower values for signal peak (P<10(-7)) and area (P<10(-8)) with respect to vascular voxels. The analysis showed how venous blood influenced the measured BOLD signals, supplying a technique to filter possible venous artifacts that potentially can lead to misinterpretation of fMRI results. This methodology, although validated in the auditory cortex activation, maintains a general applicability to any cortical fMRI study, as the basic concepts on which it relies on are not limited to this cortical region. The results obtained in this study can represent the basis for new methodologies and tools that are capable of adding further characterization to the BOLD signal properties.     

The effects of respiratory CO2 fluctuations in the resting-state BOLD signal differ between eyes open and eyes closed

Resting fluctuations in arterial CO₂ (a cerebral vasodilator) are believed to be an important source of low-frequency blood oxygenation level dependent (BOLD) signal fluctuations. In this study we focus on the two commonly used resting-states in functional magnetic resonance imaging experiments, eyes open and eyes closed, and quantify the degree to which measured spontaneous fluctuations in the partial pressure of end-tidal CO₂ (Pet_co2) relate to BOLD signal time series. A significantly longer latency of BOLD signal changes following Pet_co2 fluctuations was found in the eyes closed condition compared to with eyes open, which may reveal different intrinsic vascular response delays in CO₂ reactivity or an alteration in the net BOLD signal arising from Pet_co2 fluctuations and altered neural activity with eyes closed. By allowing a spatially varying time delay for the compensation of this temporal difference, a more spatially consistent CO₂ correlation map can be obtained. Finally, Granger-causality analysis demonstrated a “causal” relationship between Pet_co2 and BOLD. The identified dominant Pet_co2→BOLD directional coupling supports the notion that Pet_co2 fluctuations are indeed a cause of resting BOLD variance in the majority of subjects.     

The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents

Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The 'static' respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the 'dynamic' cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity.     

Normalized linear variance decay dimension density and its application of dynamical complexity detection in physiological (fMRI) time series

The upper and lower bounds of the linear variance decay (LVD) dimension density are analytically deduced using multivariate series with uncorrelated and perfectly correlated component series. Then, the normalized LVD dimension density (δnormLVD) is introduced. In order to measure the complexity of a scalar series with δnormLVD, a pseudo-multivariate series was constructed from the scalar time series using time-delay embedding. Thus, δnormLVD is used to characterize the complexity of the pseudo-multivariate series. The results from the model systems and fMRI data of anxiety subjects reveal that this method can be used to analyze short and noisy time series.     

Spectral response modeling and analysis of p-n-p In_(0.53)Ga_(0.47)As/InP HPTs

We report our results on the modeling of the spectral response of the near-infrared(NIR) lattice-matched p-n-p In_(0.53)Ga_(0.47)As/InP heterojunction pbototransistors(HPTs).The spectral response model is developed from the solution of the steady state continuity equations that dominate the excess optically generated minority-carriers in the active regions of the HPTs with accurate boundary-conditions.In addition,a detailed optical-power absorption profile is constructed for the device modeling.The calculated responsivity is in good agreement with the measured one for the incident radiation at980 nm,1310 nm,and 1550 nm.Furthermore,the variation in the responsivity of the device with the base region width is analyzed.