Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data
Guoqiang Hu,
Huanjie Li,
Wei Zhao,
Yuxing Hao,
Zonglei Bai,
Lisa D. Nickerson,
Fengyu Cong
Affiliations
Guoqiang Hu
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Huanjie Li
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Wei Zhao
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Yuxing Hao
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Zonglei Bai
School of Electronics Engineering and Computer Science, Peking University, Beijing, China
Lisa D. Nickerson
Brain Imaging Center, Mclean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Corresponding authors.
Fengyu Cong
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland; Corresponding authors.
The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.
