fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging
Abstract Multimodal functional neuroimaging by integrating functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has the promise of recovering brain activities with high spatiotemporal resolution, which is crucial for neuroscience research and clinical diagnosis. However, the misalignment of the localizations between fMRI and EEG activities may degrade the accuracy of the fMRI-constrained EEG source imaging (ESI) technique. To leverage the complementary spatiotemporal resolution of fMRI and EEG in a data-driven fashion, we propose an asymmetric approach for EEG/fMRI fusion, termed fMRI-informed source imaging based on spatiotemporal basis functions (fMRI-SI-STBF). fMRI-SI-STBF employs the covariance components (CCs) derived from clusters defined by fMRI and EEG signals as spatial priors within the empirical Bayesian framework. Additionally, fMRI-SI-STBF represents the current source matrix as a linear combination of several unknown temporal basis functions (TBFs) by matrix decomposition. The relative contribution of each of the fMRI-informed and EEG-informed CCs, as well as the number and profiles of the TBFs, are all automatically determined based on the EEG data using variational Bayesian inference. Our results demonstrate that fMRI-SI-STBF can effectively utilize valid fMRI information for ESI and is robust to invalid fMRI priors. This robustness is essential for practical ESI since the validity of fMRI priors is often unclear considering that fMRI is an indirect measure of neural activity. Moreover, fMRI-SI-STBF can achieve performance improvement by incorporating temporal constraints compared to methods that use spatial constraints only. For the numerical simulations, fMRI-SI-STBF reconstructs the source extents, locations and time courses more accurately than existing EEG-fMRI ESI methods (i.e., fwMNE, fMRI-SI-SBF) and ESI methods without fMRI priors (i.e., wMNE, LORETA, SBL, SI-STBF, SI-SBF), indicated by the smaller spatial dispersion (average SD 5 mm), distance of localization error (average DLE 2 mm), shape error (average SE 0.9 ) and larger model evidence values.
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fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging
Semantic Scholar · Engineering · 2021
Abstract
Abstract Multimodal functional neuroimaging by integrating functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has the promise of recovering brain activities with high spatiotemporal resolution, which is crucial for neuroscience research and clinical diagnosis. However, the misalignment of the localizations between fMRI and EEG activities may degrade the accuracy of the fMRI-constrained EEG source imaging (ESI) technique. To leverage the complementary spatiotemporal resolution of fMRI and EEG in a data-driven fashion, we propose an asymmetric approach for EEG/fMRI fusion, termed fMRI-informed source imaging based on spatiotemporal basis functions (fMRI-SI-STBF). fMRI-SI-STBF employs the covariance components (CCs) derived from clusters defined by fMRI and EEG signals as spatial priors within the empirical Bayesian framework. Additionally, fMRI-SI-STBF represents the current source matrix as a linear combination of several unknown temporal basis functions (TBFs) by matrix decomposition. The relative contribution of each of the fMRI-informed and EEG-informed CCs, as well as the number and profiles of the TBFs, are all automatically determined based on the EEG data using variational Bayesian inference. Our results demonstrate that fMRI-SI-STBF can effectively utilize valid fMRI information for ESI and is robust to invalid fMRI priors. This robustness is essential for practical ESI since the validity of fMRI priors is often unclear considering that fMRI is an indirect measure of neural activity. Moreover, fMRI-SI-STBF can achieve performance improvement by incorporating temporal constraints compared to methods that use spatial constraints only. For the numerical simulations, fMRI-SI-STBF reconstructs the source extents, locations and time courses more accurately than existing EEG-fMRI ESI methods (i.e., fwMNE, fMRI-SI-SBF) and ESI methods without fMRI priors (i.e., wMNE, LORETA, SBL, SI-STBF, SI-SBF), indicated by the smaller spatial dispersion (average SD 5 mm), distance of localization error (average DLE 2 mm), shape error (average SE 0.9 ) and larger model evidence values.