How to use fMRI functional localizers to improve EEG/MEG source estimation

Cottereau, Benoit R.; Ales, Justin; Norcia, Anthony M.

doi:10.1016/j.jneumeth.2014.07.015

Cited by 48 publications

(48 citation statements)

References 99 publications

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“…where a subject performs a task in an MRI scanner while wearing an EEG net. This approach has yielded new methods for obtaining temporal components that can be associated with clusters of activation determined by fMRI that contribute to the generation of the electrical signal (see Huster et al, 2013, for a review), but does suffer from complications due to artifacts produced from the simultaneous methodology (e.g., Mulert and Lemieux, 2009;Ullsperger and Debener, 2010;Cottereau et al, 2015). By contrast, the method we have proposed here focuses on linking parameters of a cognitive model to both behavior and multiple brain measures.…”

Section: Contrasts With Data Fusion Methodsmentioning

confidence: 99%

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

et al. 2016

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The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

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Section: Contrasts With Data Fusion Methodsmentioning

confidence: 99%

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

et al. 2016

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“…Some of these methods, especially those devised for simple source models, can be effectively used to reconstruct functional entities, extracted by the proposed technology. The fact, that proposed technology splits MEG into elementary oscillations with relatively simple patterns, can revive few-channel measurements, including those combined with MRI (Zotev et al, 2008 ; Cottereau et al, 2015 ; Fukushima et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

et al. 2015

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A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in 10 healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in 10 healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject's head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space.

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“…In the visually evoked experiments, the estimated source distribution produced by FITC had a narrow spread, and we observed dynamic activity from lower‐tier to high‐tier visual areas. This observed dynamic activity generally agrees with previous studies (Bonmassar, Schwartz, & Liu, ; Cottereau, Ales, & Norcia, ; Vanni et al, ). There are differences between the activation pattern of the source estimates of EEG and MEG data since EEG and MEG have different sensitivities to different configured brain activities.…”

Section: Discussionmentioning

confidence: 99%

“…Brodmann area 19 is present in MNE, but not in fMNE at 175 ms. FITC and wFITC removed several diffuse activation areas in the MNE results and detected activation in the visual area (third and fourth rows in Figure 9c). Liu, 2001;Cottereau, Ales, & Norcia, 2015;Vanni et al, 2004). There are differences between the activation pattern of the source estimates of EEG and MEG data since EEG and MEG have different sensitivities to different configured brain activities.…”

Section: Visual Stimulation Experimentsmentioning

confidence: 99%

EEG/MEG source imaging using fMRI informed time‐variant constraints

Sheng

Qian

et al. 2018

Human Brain Mapping

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Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) or magnetoencephalography (MEG) is able to provide high spatiotemporal resolution mapping of brain activity. However, the accuracy of fMRI-constrained EEG/MEG source imaging may be degraded by potential spatial mismatches between the locations of fMRI activation and electrical source activities. To address this problem, we propose a novel fMRI informed time-variant constraint (FITC) method. The weights in FITC are determined by combining the fMRI activities and electrical source activities in a time-variant manner to reduce the impact of the fMRI extra sources. The fMRI weights are modified using cross-talk matrix and normalized partial area under the curve to reduce the impact of fMRI missing sources. Monte Carlo simulations were performed to compare the source estimates produced by L2-minimum norm estimation (MNE), fMRI-weighted minimum norm estimation (fMNE), FITC, and depth-weighted FITC (wFITC) algorithms with various spatial mismatch conditions. Localization error and temporal correlation were calculated to compare the four algorithms under different conditions. The simulation results indicated that the FITC and wFITC methods were more robust than the MNE and fMNE algorithms. Moreover, FITC and wFITC were significantly better than fMNE under the fMRI missing sources condition. A human visual-stimulus EEG, MEG, and fMRI test was performed, and the experimental data revealed that FITC and wFITC displayed more focal areas than fMNE and MNE. In conclusion, the proposed FITC method is able to better resolve the spatial mismatch problems encountered in fMRI-constrained EEG/MEG source imaging.

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How to use fMRI functional localizers to improve EEG/MEG source estimation

Cited by 48 publications

References 99 publications

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

EEG/MEG source imaging using fMRI informed time‐variant constraints

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