Activation of multiple cortical areas in response to somatosensory stimulation: Combined magnetoencephalographic and functional magnetic resonance imaging

Korvenoja, Antti; Huttunen, J.; Salli, Eero; Pohjonen, Hanna; Martinkauppi, S.; Palva, J. Matias; Lauronen, Leena; Virtanen, Juha; Ilmoniemi, Risto J.; Hj, Aronen

doi:10.1002/(sici)1097-0193(1999)8:1<13::aid-hbm2>3.0.co;2-b

Cited by 133 publications

(84 citation statements)

References 62 publications

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“…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%

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: 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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“…Depending on different EEG source models, the fMRI map can be used to constrain the locations of multiple current dipoles, namely the fMRI-constrained dipole fitting (Ahlfors et al, 1999;Korvenoja et al, 1999;Fujimaki et al, 2002;Vanni et al, 2004), or to constrain the distributed source distribution over the folded cortical surface or in the 3-D brain volume, namely the fMRI-constrained current density imaging (George et al, 1995;Liu et al, 1998;Dale et al, 2000;Wagner et al, 2000;Babiloni et al, 2005;Ahlfors and Simpson, 2004;Sato et al, 2004;Phillips et al, 2005;Liu et al, 2006b;Mattout et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

2008

NeuroImage

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In response to the need of establishing a high-resolution spatiotemporal neuroimaging technique, tremendous efforts have been focused on developing multimodal strategies that combine the complementary advantages of high-spatial-resolution functional magnetic resonance imaging (fMRI) and high-temporal-resolution electroencephalography (EEG) or magnetoencephalography (MEG). A critical challenge to the fMRI-EEG/MEG integration lies in the spatial mismatches between fMRI activations and instantaneous electrical source activities. Such mismatches are fundamentally due to the fact that fMRI and EEG/MEG signals are generated and collected in highly different time scales. In this paper, we propose a new theoretical framework to solve the problem of fMRI-EEG integrated cortical source imaging. The new framework has two principal technical advancements. First, by assuming a linear neurovascular coupling, a method is derived to quantify the fMRI signal in each voxel as proportional to the time integral of the power of local electrical current during the period of event related potentials (ERP). Second, the EEG inverse problem is solved for every time instant using an adaptive Wiener filter, in which the prior time-variant source covariance matrix is estimated based on combining the quantified fMRI responses and the segmented EEG signals before response averaging. A series of computer simulations were conducted to evaluate the proposed methods in terms of imaging the instantaneous cortical current density (CCD) distribution and estimating the source time courses with a millisecond temporal resolution. As shown in the simulation results, the instantaneous CCD reconstruction by using the proposed fMRI-EEG integration method was robust against both fMRI false positives and false negatives while retaining a spatial resolution nearly as high as that of fMRI. The proposed method could also reliably estimate the source waveforms when multiple sources were temporally correlated or uncorrelated, or were sustained or transient, or had some features in frequency or phase, or had even more complicated temporal dynamics. Moreover, applying the proposed method to real fMRI and EEG data acquired in a visual experiment yielded a time series of reconstructed CCD images, in agreement with the traditional view of hierarchical visual processing. In conclusion, the proposed method provides a reliable technique for the fMRI-EEG integration and represents a significant advancement over the conventional fMRI-weighted EEG (or MEG) source imaging techniques, and is also applicable to the fMRI-MEG integrated source imaging.

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“…Since the MEG/EEG inverse problem is illposed [Sarvas, 1987], spatial fMRI information is potentially useful in resolving the ambiguity. Often, because of difficulties in the true multimodal integration of MEG/ EEG and fMRI, the suggested solutions have been for instance direct comparison of the separate results [e.g., Ahlfors et al, 1999], using fMRI data as a basis to adjust the source variance parameters [Dale et al, 2000;Liu et al, 1998], utilization of the functional results for constraining the possible source positions [e.g., Korvenoja et al, 1999], or by directly seeding the fMRI locations and cortical orientations to be optimized with a suitable EEG source dipole model [Vanni et al, 2004a,b]. Lately, there has been great interest in Bayesian methods utilizing fMRI prior information, for instance, with distributed linear solutions of the MEG/EEG inverse problem [e.g., Dale et al, 2000;Phillips et al, 2005] and also on determining the relevance of the fMRI prior information included in the inverse solution [Daunizeau et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

Automatic fMRI‐guided MEG multidipole localization for visual responses

Auranen¹,

Nummenmaa²,

Vanni³

et al. 2008

Human Brain Mapping

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Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ([2005]; Neuroimage 28:84-98). However, the model's performance was limited by slow convergence and multimodality of the numerically estimated posterior distribution. In this paper, we present an intuitive way to exploit functional magnetic resonance imaging (fMRI) data in the Markov chain Monte Carlo sampling -based inverse estimation of magnetoencephalographic (MEG) data. We used simulated MEG and fMRI data to show that the convergence and localization accuracy of the method is significantly improved with the help of fMRI-guided proposal distributions. We further demonstrate, using an identical visual stimulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially close and temporally overlapping sources. Theoretically, the MEG inverse estimates are not biased and should yield the same results even without fMRI information, however, in practice the multimodality of the posterior distribution causes problems due to the limited mixing properties of the sampler. On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis.

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Activation of multiple cortical areas in response to somatosensory stimulation: Combined magnetoencephalographic and functional magnetic resonance imaging

Cited by 133 publications

References 62 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

fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

Automatic fMRI‐guided MEG multidipole localization for visual responses

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