A1 Journal article (refereed)
Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data (2022)

Hu, G., Li, H., Zhao, W., Hao, Y., Bai, Z., Nickerson, L. D., & Cong, F. (2022). Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data. Neuroimage, 255, Article 119193. https://doi.org/10.1016/j.neuroimage.2022.119193

JYU authors or editors

Publication details

All authors or editors: Hu, Guoqiang; Li, Huanjie; Zhao, Wei; Hao, Yuxing; Bai, Zonglei; Nickerson, Lisa D.; Cong, Fengyu

Journal or series: Neuroimage

ISSN: 1053-8119

eISSN: 1095-9572

Publication year: 2022

Volume: 255

Article number: 119193

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.neuroimage.2022.119193

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80711

Web address of parallel published publication (pre-print): https://www.biorxiv.org/content/10.1101/2021.01.14.426756v1

Abstract

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.

Keywords: brain research; neural networks (biology); functional magnetic resonance imaging; signal analysis; signal processing

Free keywords: Tensor components analysis; Naturalistic stimuli; fMRI; Inter-subject correlation

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 2