Abstract
Background Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.
Highlights
Emotional regulation is known as a unique ability of human beings to control experience and expression of their emotions
Despite the widely developed approaches to analyze EEGfMRI data, there is still no method that focuses on two challenging issues of simultaneous EEG-fMRI fusion; one is to handle the mutual interference between EEG and fMRI, and the other is to handle the nonlinearity of EEG-fMRI data. Aiming to resolve these challenges, we propose an effective fusion framework based on Canonical correlation analysis (CCA)
FMRI feature to be fused is defined as Y fMRI, which are obtained by calculating mean values in anatomical automatic labeling (AAL) cerebral regions under the constraints of fMRI masks
Summary
Emotional regulation is known as a unique ability of human beings to control experience and expression of their emotions. Two wellestablished emotional regulation strategies are widely applied to control emotional experiences, including expressive suppression and cognitive reappraisal. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. In these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is suitable for other neuroimaging technologies using simultaneous EEG-fMRI data
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