An Accelerated Rank-(L,L,1,1) Block Term Decomposition Of Multi-Subject Fmri Data Under Spatial Orthonormality Constraint

• Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3933 - 3937
• Main Authors: Kuang, Li-Dan, Wang, Biao, Lin, Qiu-Hua, Zhang, Hao-Peng, Zhang, Jianming, Li, Wenjun, Li, Feng, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2022
• Subjects: 1) BTD; acceleration; ALS; fMRI; Functional magnetic resonance imaging; Loading; Memory management; rank-(L; Signal processing; Signal processing algorithms; Spatial databases; Spatiotemporal phenomena; tensor decomposition
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP43922.2022.9746404

The decomposition of multi-subject fMRI data using rank-(L,L,1,1) block term decomposition (BTD) can preserve higher-way data structure and is more robust to noise effects by decomposing shared spatial maps (SMs) into a product of two rank-L loading matrices. However, since the number of whole-brain voxels is very large and rank L is larger than 1, the rank-(L,L,1,1) BTD requires high computation and memory. Therefore, we propose an accelerated rank-(L,L,1,1) BTD algorithm based upon the method of alternating least squares (ALS). We speed up updates of loading matrices by reducing fMRI data into subspaces, and add an orthonormality constraint on shared SMs to improve the performance. Moreover, we evaluate the rank-L effect on the proposed method for actual task-related fMRI data. The proposed method shows better performance when L=35. Meanwhile, experimental comparison results verify that the proposed method largely reduced (17.36 times) computation time compared to ALS while also providing satisfying separation performance.