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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 3018356, 11 pages
https://doi.org/10.1155/2018/3018356
Research Article

Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

1School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
2Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, China
3School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
4Changzhou No. 2 People’s Hospital Affiliated with Nanjing Medical University, Changzhou, Jiangsu 213164, China

Correspondence should be addressed to Ling Zou; nc.ude.uzcc@gniluoz and Jianbo Xiang; moc.361@bob_xh

Received 5 March 2018; Accepted 21 May 2018; Published 2 July 2018

Academic Editor: Miguel García-Torres

Copyright © 2018 Biao Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.