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Search Results: 1 - 10 of 2003 matches for " simultaneous EEG-fMRI "
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Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands
Fani Deligianni,Maria Centeno,David W. Carmichael,Jonathan D. Clayden
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00258
Abstract: Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.
Investigating the effect of modifying the EEG cap lead configuration on the gradient artifact in simultaneous EEG-fMRI
Karen J. Mullinger,Muhammad E. H. Chowdhury
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00226
Abstract: EEG data recorded during simultaneous fMRI are contaminated by large voltages generated by time-varying magnetic field gradients. Correction of the resulting gradient artifact (GA) generally involves low-pass filtering to attenuate the high-frequency voltage fluctuations of the GA, followed by subtraction of a GA template produced by averaging over repeats of the artifact waveforms. This average artifact subtraction (AAS) process relies on the EEG amplifier having a large enough dynamic range to characterize the artifact voltages and on invariance of the artifact waveform over repeated image acquisitions. Saturation of the amplifiers and changes in subject position can leave unwanted residual GA after AAS. Previous modeling work suggested that modifying the lead layout and the exit position of the cable bundle on the EEG cap could reduce the GA amplitude. Here, we used simulations and experiments to evaluate the effect of modifying the lead paths on the magnitude of the GA and on the residual artifact after AAS. The modeling work showed that for wire paths following great circles, the smallest overall GA occurs when the leads converge at electrode Cz. The performance of this new cap design was compared with a standard cap in experiments on a spherical agar phantom and human subjects. Using gradient pulses applied separately along the three Cartesian axes, we found that the GA due to the foot-head gradient was most significantly reduced relative to a standard cap for the phantom, whereas the anterior-posterior GA was most attenuated for human subjects. In addition, there was an overall 37% reduction in the RMS GA amplitude produced by a standard EPI sequence when comparing the two caps on the phantom. In contrast, the subjects showed an 11% increase in the average RMS of the GA. This work shows that the optimal design reduces the GA on a spherical phantom however; these gains are not translated to human subjects, probably due to the differences in geometry.
Separation and Reconstruction of BCG and EEG Signals during Continuous EEG and fMRI Recordings
Hongjing Xia,Dan Ruan,Mark S. Cohen
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00163
Abstract: Despite considerable effort to remove it, the ballistocardiogram (BCG) remains a major artifact in electroencephalographic data (EEG) acquired inside magnetic resonance imaging (MRI) scanners, particularly in continuous (as opposed to event-related) recordings. In this study, we have developed a new Direct Recording – Prior Encoding (DRPE) method to extract and separate the BCG and EEG components from contaminated signals, and have demonstrated its performance by comparing it quantitatively to the popular Optimal Basis Set (OBS) method. Our modified recording configuration allows us to obtain representative bases of the BCG- and EEG-only signals. Further, we have developed an optimization-based reconstruction approach to maximally incorporate prior knowledge of the BCG/EEG subspaces, and of the signal characteristics within them. Both OBS and DRPE methods were tested with experimental data, and compared quantitatively using cross-validation. In the challenging continuous EEG studies, DRPE outperforms the OBS method by nearly 7 fold in separating the continuous BCG and EEG signals.
Removing ballistocardiogram (BCG) artifact from full-scalp EEG acquired inside the MR scanner with Orthogonal Matching Pursuit (OMP)
Hongjing Xia,Dan Ruan,Mark S. Cohen
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00218
Abstract: Ballistocardiogram (BCG) artifact remains a major challenge that renders electroencephalographic (EEG) signals hard to interpret in simultaneous EEG and functional MRI (fMRI) data acquisition. Here, we propose an integrated learning and inference approach that takes advantage of a commercial high-density EEG cap, to estimate the BCG contribution in noisy EEG recordings from inside the MR scanner. To estimate reliably the full-scalp BCG artifacts, a near-optimal subset (20 out of 256) of channels first was identified using a modified recording setup. In subsequent recordings inside the MR scanner, BCG-only signal from this subset of channels was used to generate continuous estimates of the full-scalp BCG artifacts via inference, from which the intended EEG signal was recovered. The reconstruction of the EEG was performed with both a direct subtraction and an optimization scheme. We evaluated the performance on both synthetic and real contaminated recordings, and compared it to the benchmark Optimal Basis Set (OBS) method. In the challenging non-event-related-potential (non-ERP) EEG studies, our reconstruction can yield more than fourteen-fold improvement in reducing the normalized RMS error of EEG signals, compared to OBS.
Transfer Function between EEG and BOLD Signals of Epileptic Activity
Marco Leite,Alberto Leal,Patrícia Figueiredo
Frontiers in Neurology , 2013, DOI: 10.3389/fneur.2013.00001
Abstract: Simultaneous electroencephalogram (EEG)-functional Magnetic Resonance Imaging (fMRI) recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a hemodynamic response function to model the associated Blood Oxygen Level Dependent (BOLD) changes. Although more flexible approaches may allow a higher degree of complexity for the hemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent, and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity, and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.
Actualidad en la investigación de electroencefalograma - resonancia magnética funcional simultáneos en el estudio de epilepsia y dolor Current Status of simultaneous EEG-fMRI research applied to epilepsy and pain study
César Augusto Aldana Ramirez,Elías Buitrago Bolivar
Revista Cubana de Investigaciones Biom??dicas , 2013,
Abstract: Los recientes avances en las técnicas de neuroimagen han contribuido en la comprensión de la dinámica funcional del cerebro. Especialmente, los estudios simultáneos de EEG-fMRI han aportado valiosa información estudiando dicha dinámica desde dos frentes, la actividad eléctrica y la hemodinámica. En el siguiente artículo se realiza una revisión de la técnica, el hardware requerido, las formas de análisis, sus principales inconvenientes y los logros obtenidos en el estudio de la epilepsia y el dolor. Recent advances in neuroimaging techniques have contributed in functional dynamics comprehension of the brain. Specially, the simultaneous studies of EEG-fMRI have provided valuable information, by studying that brain dynamics from two points of view: bioelectricity and hemodynamics. In this paper, we review the technique, the required hardware and the methods of analysis. The main drawbacks and achievements obtained in the study of epilepsy and pain are presented, as well.
EEG spike source localization before and after surgery for temporal lobe epilepsy: a BOLD EEG-fMRI and independent component analysis study
Sercheli, M.S.;Bilevicius, E.;Alessio, A.;Ozelo, H.;Pereira, F.R.S.;Rondina, J.M.;Cendes, F.;Covolan, R.J.M.;
Brazilian Journal of Medical and Biological Research , 2009, DOI: 10.1590/S0100-879X2009000600017
Abstract: simultaneous measurements of eeg-functional magnetic resonance imaging (fmri) combine the high temporal resolution of eeg with the distinctive spatial resolution of fmri. the purpose of this eeg-fmri study was to search for hemodynamic responses (blood oxygen level-dependent - bold responses) associated with interictal activity in a case of right mesial temporal lobe epilepsy before and after a successful selective amygdalohippocampectomy. therefore, the study found the epileptogenic source by this noninvasive imaging technique and compared the results after removing the atrophied hippocampus. additionally, the present study investigated the effectiveness of two different ways of localizing epileptiform spike sources, i.e., bold contrast and independent component analysis dipole model, by comparing their respective outcomes to the resected epileptogenic region. our findings suggested a right hippocampus induction of the large interictal activity in the left hemisphere. although almost a quarter of the dipoles were found near the right hippocampus region, dipole modeling resulted in a widespread distribution, making eeg analysis too weak to precisely determine by itself the source localization even by a sophisticated method of analysis such as independent component analysis. on the other hand, the combined eeg-fmri technique made it possible to highlight the epileptogenic foci quite efficiently.
Dynamic BOLD functional connectivity in humans and its electrophysiological correlates
Enzo Tagliazucchi,Frederic von Wegner,Helmut Laufs
Frontiers in Human Neuroscience , 2012, DOI: 10.3389/fnhum.2012.00339
Abstract: Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations. This dynamic functional connectivity has recently been demonstrated in spontaneous fluctuations of the Blood Oxygen Level-Dependent (BOLD) signal, measured with functional Magnetic Resonance Imaging (fMRI). We analyzed temporal fluctuations in BOLD connectivity and their electrophysiological correlates, by means of long (≈50 min) joint electroencephalographic (EEG) and fMRI recordings obtained from two populations: 15 awake subjects and 13 subjects undergoing vigilance transitions. We identified positive and negative correlations between EEG spectral power (extracted from electrodes covering different scalp regions) and fMRI BOLD connectivity in a network of 90 cortical and subcortical regions (with millimeter spatial resolution). In particular, increased alpha (8–12 Hz) and beta (15–30 Hz) power were related to decreased functional connectivity, whereas gamma (30–60 Hz) power correlated positively with BOLD connectivity between specific brain regions. These patterns were altered for subjects undergoing vigilance changes, with slower oscillations being correlated with functional connectivity increases. Dynamic BOLD functional connectivity was reflected in the fluctuations of graph theoretical indices of network structure, with changes in frontal and central alpha power correlating with average path length. Our results strongly suggest that fluctuations of BOLD functional connectivity have a neurophysiological origin. Positive correlations with gamma can be interpreted as facilitating increased BOLD connectivity needed to integrate brain regions for cognitive performance. Negative correlations with alpha suggest a temporary functional weakening of local and long-range connectivity, associated with an idling state.
Deficits during Voluntary Selection in Adult Patients with ADHD: New Insights from Single-Trial Coupling of Simultaneous EEG/fMRI
Julia Voelker,Matthias Ertl,Gregor Leicht,Christoph Mulert
Frontiers in Psychiatry , 2014, DOI: 10.3389/fpsyt.2014.00041
Abstract: Deficits in executive functions, including voluntary decisions are among the core symptoms of ADHD patients. In order to clarify the spatio-temporal characteristics of these deficits, a simultaneous EEG/fMRI study was performed. Single-trial coupling was used to integrate temporal EEG information in the fMRI analyses and to correlate the trial by trial variation in the different ERP amplitudes with fMRI BOLD responses. The results demonstrated that during voluntary selection early electrophysiological responses (N2) were associated with responses in similar brain regions in healthy participants as well as in ADHD patients e.g. in the medial frontal cortex and the inferior parietal gyrus. However, ADHD patients presented significantly reduced N2 related BOLD responses compared to healthy controls especially in these frontal areas. These results support the hypothesis that in ADHD patients executive deficits are accompanied by early dysfunctions, especially in frontal brain areas.
Sensitivity and Specificity of Interictal EEG-fMRI for Detecting the Ictal Onset Zone at Different Statistical Thresholds
Simon Tousseyn,Patrick Dupont,Karolien Goffin
Frontiers in Neurology , 2014, DOI: 10.3389/fneur.2014.00131
Abstract: There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4–3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ.
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