The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

Sharon, Dahlia; Hämäläinen, Matti S.; Tootell, Roger B. H.; Halgren, Eric; Belliveau, John W.

doi:10.1016/j.neuroimage.2007.03.066

Cited by 189 publications

(172 citation statements)

References 68 publications

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“…In the occipital cortex, MEG and fMRI displayed similar functionality. The approximate colocalization of the MEG and fMRI activations is in line with previous studies on low-level visual processing (e.g., Sharon et al, 2007).…”

Section: Discussionsupporting

confidence: 90%

“…However, it is possible that MEG and fMRI are differentially sensitive to various characteristics of neural activity, such as synchronicity or duration of neural activation, or feedforward versus feedback influences on neural responses (Nunez and Silberstein, 2000). In direct comparisons, MEG evoked responses and fMRI BOLD signals have shown fairly good spatial convergence in low-level sensory and motor processing (e.g., Korvenoja et al, 1999;Sharon et al, 2007). Fewer studies have focused on complex cognitive tasks (e.g., Croizé et al, 2004;Liljeström et al, 2009), although any divergence between MEG and fMRI sensitivities is more likely to manifest itself in such experiments.…”

Section: Introductionmentioning

confidence: 99%

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Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Vartiainen¹,

Liljeström²,

Koskinen³

et al. 2011

J. Neurosci.

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

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Vartiainen¹,

Liljeström²,

Koskinen³

et al. 2011

J. Neurosci.

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“…5 More recently Sharon and colleagues compared source identification using EEG and MEG separately as well as combined. 6 Localization perfomance based on EEG data was similar to our earlier findings. However, when EEG and MEG are combined the source localization was substantially improved over either method alone.…”

Section: Combining Eeg and Meg For Source Identificationsupporting

confidence: 88%

“…Weighted norm methods can produce more localized sources. 2,3,4 Dipole source modeling methods have recently started to incorporate cortical constraints to reduce the problem of nearby sources 5,6 to improve isolation of the dominant dipole, the likely V1…”

Section: Introductionmentioning

confidence: 99%

Combining MRI and VEP imaging to isolate the temporal response of visual cortical areas

2008

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The human brain has well over 30 cortical areas devoted to visual processing. Classical neuro-anatomical as well as fMRI studies have demonstrated that early visual areas have a retinotopic organization whereby adjacent locations in visual space are represented in adjacent areas of cortex within a visual area. At the 2006 Electronic Imaging meeting we presented a method using sprite graphics to obtain high resolution retinotopic visual evoked potential responses using multi-focal m-sequence technology (mfVEP). We have used this method to record mfVEPs from up to 192 non overlapping checkerboard stimulus patches scaled such that each patch activates about 12 mm 2 of cortex in area V1 and even less in V2. This dense coverage enables us to incorporate cortical folding constraints, given by anatomical MRI and fMRI results from the same subject, to isolate the V1 and V2 temporal responses. Moreover, the method offers a simple means of validating the accuracy of the extracted V1 and V2 time functions by comparing the results between left and right hemispheres that have unique folding patterns and are processed independently. Previous VEP studies have been contradictory as to which area responds first to visual stimuli. This new method accurately separates the signals from the two areas and demonstrates that both respond with essentially the same latency. A new method is introduced which describes better ways to isolate cortical areas using an empirically determined forward model. The method includes a novel steady state mfVEP and complex SVD techniques. In addition, this evolving technology is put to use examining how stimulus attributes differentially impact the response in different cortical areas, in particular how fast nonlinear contrast processing occurs. This question is examined using both state triggered kernel estimation (STKE) and m-sequence "conditioned kernels". The analysis indicates different contrast gain control processes in areas V1 and V2. Finally we show that our m-sequence multi-focal stimuli have advantages for integrating EEG and MEG for improved dipole localization.

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“…We combined simultaneously acquired MEG and EEG data, which can give a more accurate source localization than either modality alone because of their different biophysical characteristics (33). In addition, our source model is based on individual cortical geometry from structural MRI and assumes that the principal orientation of the currents is normal to the cortex.…”

Section: Discussionmentioning

confidence: 99%

Multimodal neuroimaging dissociates hemodynamic and electrophysiological correlates of error processing

Agam

Hämäläinen

Lee

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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104

108

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Recognizing errors and adjusting responses are fundamental to adaptive behavior. The error-related negativity (ERN) and errorrelated functional MRI (fMRI) activation of the dorsal anterior cingulate cortex (dACC) index these processes and are thought to reflect the same neural mechanism. In the present study, we evaluated this hypothesis. Although errors elicited robust dACC activation using fMRI, combined electroencephalography and magnetoencephalography data localized the ERN to the posterior cingulate cortex (PCC). ERN amplitude correlated with fMRI activation in both the PCC and dACC, and these two regions showed coordinated activity based on functional connectivity MRI. Finally, increased microstructural integrity of the posterior cingulum bundle, as measured by diffusion tensor imaging, predicted faster error correction. These findings suggest that the PCC generates the ERN and communicates with the dACC to subserve error processing. They challenge current models that view fMRI activation of the dACC as the hemodynamic reflection of the ERN. U nderstanding the nature of brain mechanisms that flexibly modify behavior in response to its outcome is a basic goal of neuroscience. Errors provide critical information for adjusting behavior to optimize outcomes. Neuroimaging studies have identified two highly reliable neural correlates of errors: the error-related negativity (ERN), an event-related potential that peaks ∼100 ms following an error, and functional MRI (fMRI) activation of the dorsal anterior cingulate cortex (dACC) for erroneous compared with correct responses (1). Both electroencephalography (EEG) and magnetoencephalography (MEG) (2) studies of the ERN have reported a source in the dACC (a list of studies is presented in Table S1), which is consistent with models that attribute these error markers to a common underlying mechanism (1, 3, 4). The primary goal of the present study was to evaluate the hypothesis of a common mechanism by determining whether the ERN is generated by the dACC region that shows error-related fMRI activation.The ERN has been extensively characterized. Its amplitude is greater when accuracy is emphasized over speed (5), when errors are corrected (6), and when errors incur greater loss (7). Larger ERNs are associated with lower error rates (3) and greater posterror slowing of responses (8). ERN latency predicts the speed of self-corrections (9). These findings suggest that the ERN is sensitive to the value of outcomes and mediates dynamic performance adjustments. Like the ERN, greater error-related fMRI activation of the dACC is associated with fewer errors (10, 11) and increased posterror slowing (12)(13)(14).Although error-related dACC activation is the putative hemodynamic reflection of the ERN (1, 4), these error markers have largely been studied separately using different samples and paradigms. The few studies that have directly investigated their relationship report correlations of fMRI activation of the ACC with the ERN and/or the error waveform or response-locked electr...

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The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

Cited by 189 publications

References 68 publications

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Combining MRI and VEP imaging to isolate the temporal response of visual cortical areas

Multimodal neuroimaging dissociates hemodynamic and electrophysiological correlates of error processing

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