Tucker Decomposition for Extracting Shared and Individual Spatial Maps from Multi-Subject Resting-State fMRI Data

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1110 - 1114
• Main Authors: Han, Yue, Lin, Qiu-Hua, Kuang, Li-Dan, Gong, Xiao-Feng, Cong, Fengyu, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.06.2021
• Subjects: Brain modeling; Couplings; Data models; Functional magnetic resonance imaging; individual spatial maps; Multi-subject fMRI data; resting-state fMRI data; shared spatial maps; Signal processing; Spatial databases; Tensors; Tucker decomposition
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9413958

Tucker decomposition (TKD) has been utilized to identify functional connectivity patterns using processed fMRI data, but seldom focuses on originally acquired fMRI data. This study proposes to decompose multi-subject fMRI data in a natural three-way of voxel × time × subject via TKD. Different from existing tensor decomposition algorithms such as canonical polyadic decomposition (CPD) for extracting shared spatial maps (SMs), we propose to extract both shared and individual SMs by exploring spatial-temporal-subject relationship contained in the core tensor. We test the proposed method using multi-subject resting-state fMRI data with comparison to CPD for evaluating shared SMs and independent vector analysis (IVA) for assessing individual SMs under different model orders. The results show that the proposed method yields better and more robust shared SMs than CPD and more consistent individual SMs than IVA, indicating the potential of TKD in providing group and individual brain networks in a high-dimensional coupling way.