Electroencephalography during functional echo-planar imaging: Detection of epileptic spikes using post-processing methods

Hoffmann, Alexander; Jäger, L.; Werhahn, Konrad J.; Jaschke, M; Noachtar, Soheyl; Reiser, Maximilian

doi:10.1002/1522-2594(200011)44:5<791::aid-mrm17>3.0.co;2-2

Cited by 141 publications

(67 citation statements)

References 19 publications

(24 reference statements)

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“…The fMRI data were acquired in runs of 120 images taking 6 min each. The gradient artifact was removed off-line using FEMR software (Schwarzer; Hoffmann et al (2000)). The patient's head was immobilized using a vacuumbag filled with polystyrene spheres (S&S X-ray products, New York, USA).…”

Section: Data Acquisitionmentioning

confidence: 99%

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

et al. 2008

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In order to analyze where epileptic spikes are generated, we assessed the level of concordance between EEG source localization using distributed source models and simultaneous EEG-fMRI which measures the hemodynamic correlates of EEG activity. Data to be compared were first estimated on the same cortical surface and two comparison strategies were used: (1) MEMconcordance: a comparison between EEG sources localized with the Maximum Entropy on the Mean (MEM) method and fMRI clusters showing a significant hemodynamic response. Minimal geodesic distances between local extrema and overlap measurements between spatial extents of EEG sources and fMRI clusters were used to quantify MEM-concordance. (2) fMRI-relevance: estimation of the fMRI-relevance index α quantifying if sources located in an fMRI cluster could explain some scalp EEG data, when this fMRI cluster was used to constrain the EEG inverse problem. Combining MEM-concordance and fMRI-relevance (α) indexes, each fMRI cluster showing a significant hemodynamic response (p <0.05 corrected) was classified according to its concordance with EEG data. Nine patients with focal epilepsy who underwent EEG-fMRI examination followed by EEG recording outside the scanner were selected for this study. Among the 62 fMRI clusters analyzed (7 patients), 15 (24%) found in 6 patients were highly concordant with EEG according to both MEM-concordance and fMRI-relevance. EEG concordance was found for 5 clusters (8%) according to α only, suggesting sources missed by the MEM. No concordance with EEG was found for 30 clusters (48%) and for 10 clusters (16%) α was significantly negative, suggesting EEG-fMRI discordance. We proposed two complementary strategies to assess and classify EEG-fMRI concordance. We showed that for most patients, part of the hemodynamic response to spikes was highly concordant with EEG sources, whereas other fMRI clusters in response to the same spikes were found distant or discordant with EEG sources.Corresponding

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Section: Data Acquisitionmentioning

confidence: 99%

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

et al. 2008

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“…The earliest attempts at monitoring EEG signals in the MRI scanner were performed using EEG-triggered fMRI (Warach et al, 1996;Seeck et al, 1998;Krakow et al, 1999;Symms et al, 1999). Later, filtering techniques allowed for simultaneous EEG/fMRI acquisition (Allen et al, 1998;Allen et al, 2000;Hoffmann et al, 2000;Kim et al, 2004). These methods use scalp EEG to detect the timing of the spikes or other events of interest that occur during the fMRI acquisition and then the image series is analyzed using conventional event-related fMRI methods (Josephs et al, 1997) to localize BOLD activation associated with the spikes or events.…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis detection of functional MRI activity in temporal lobe epilepsy

2007

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Purpose-The objective of this pilot study was to evaluate the ability of a novel method of cluster analysis, 2dTCA, for identifying and characterizing peak fluctuations in fMRI BOLD signals in the temporal lobes and the default-mode network in temporal lobe epilepsy (TLE) without EEG.Methods-BOLD fMRI images were acquired in seventeen TLE patients and compared to EEG. The timing of significant transient BOLD peaks was estimated by 2dTCA, and activation maps were determined.Results-Sixteen subjects (94%) showed apparent temporal lobe activation. Mesial temporal activation was present in 76.4% (13 patients). Temporal lobe or insula activations were detected ipsilateral to the EEG focus in 64.7% (11 patients), bilaterally with no predominance in 29.4% (5 patients), and exclusively contralateral to the EEG focus in none. Eleven subjects showed activation in the so-called default-mode network including posterior cingulate, bilateral posterior parietal cortex, and sometimes anterior cingulate cortex.Conclusions-These results demonstrate significant positive BOLD fluctuations in the temporal lobes and default-mode regions in a higher percentage of TLE patients than previously reported using other methods. These fluctuations appear physiologically relevant and suggest increased neural activity which may not be detected on scalp EEG, but which may be important in understanding the mechanisms and origins of epileptic discharges.

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“…The underlying rationale is an assumed separation between EEG-dominated and MGA-dominated parts of the PC spectra. Note that operating on PC spectra is not the same as manipulating the EEG signal spectrum directly, as has been suggested by Hoffmann et al(2000) [18]. In spite of certain transition effects or ringing that often result from such frequency-domain filters, the Wiener criterion excelled frequently in our comparisons of residual error power ( Figure 5).…”

Section: Wiener Filter Criterion (Wf)mentioning

confidence: 82%

Good practices in EEG-MRI: The utility of retrospective synchronization and PCA for the removal of MRI gradient artefacts

2010

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The electroencephalogram (EEG) recorded during magnetic resonance imaging (MRI) inside the scanner is obstructed by the MRI gradient artefact (MGA) originating from the electromagnetic interference of the MRI with the sensitive measurement of electrical scalp potentials. Post-processing algorithms based on average artefact subtraction (AAS) have proven to be efficient in removing the MGA. However, the residual MGA after AAS still limits the quality and usable bandwidth of the EEG data despite further reduction through re-sampling, principal component analysis (PCA), and regressive filtering. We recently demonstrated that the residual MGA can largely be avoided by means of hardware synchronisation. Here we present a new software synchronisation method, which substitutes hardware synchronisation and facilitates the removal of motion artefacts by PCA. The effectiveness of the retrospective synchronisation algorithm (Resync) is demonstrated by comparison to the aforementioned techniques. For this purpose we also developed a method for simulating the MGA and we propose new concepts for quantifying and comparing the performance of post-processing algorithms for EEG-MRI data. Results indicate that the benefits of (retrospective) synchronisation and PCA depend largely on the relative contribution of timing errors and motion artefacts to the residual MGA as well as the frequency range of interest. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ÔØ Å ÒÙ× Ö ÔØ A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTGood practices in EEG-MRI: The utility of retrospective synchronization and PCA for the removal of MRI gradient artefacts. AbstractThe electroencephalogram (EEG) recorded during magnetic resonance imaging (MRI) inside the scanner is obstructed by the MRI gradient artefact (MGA) originating from the electromagnetic interference of the MRI with the sensitive measurement of electrical scalp potentials. Post-processing algorithms based on average artefact subtraction (AAS) have proven to be efficient in removing the MGA. However, the residual MGA after AAS still limits the quality and usable bandwidth of the EEG data despite further reduction through re-sampling, principal component analysis (PCA), and regressive filtering.We recently demonstrated that the residual MGA can largely be avoided by means of hardware synchronisation. Here we present a new software synchronisation method, which substitutes hardware synchronisation and facilitates the removal of motion artefacts by PCA. The effectiveness of the retrospective synchronisation algorithm (Resync) is demonstrated by comparison to the aforementi...

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Electroencephalography during functional echo-planar imaging: Detection of epileptic spikes using post-processing methods

Cited by 141 publications

References 19 publications

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

Cluster analysis detection of functional MRI activity in temporal lobe epilepsy

Good practices in EEG-MRI: The utility of retrospective synchronization and PCA for the removal of MRI gradient artefacts

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