Robust brain network identification from multi-subject asynchronous fMRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 227; p. 117615
• Main Authors: Li, Jian, Wisnowski, Jessica L., Joshi, Anand A., Leahy, Richard M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2021; Elsevier Limited; Elsevier
• Subjects: Algorithms; Brain - diagnostic imaging; Brain mapping; Brain network identification; Computer Simulation; Connectome - methods; Deactivation; Decomposition; Functional magnetic resonance imaging; Functional MRI; Humans; Image Processing, Computer-Assisted; Language; Magnetic Resonance Imaging; Models, Neurological; Nerve Net - diagnostic imaging; Optimization; Tensor decomposition; Time series; Tensor decomposition; Brain network identification; Functional MRI; Optimization
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117615

We describe a novel method for robust identification of common brain networks and their corresponding temporal dynamics across subjects from asynchronous functional MRI (fMRI) using tensor decomposition. We first temporally align asynchronous fMRI data using the orthogonal BrainSync transform, allowing us to study common brain networks across sessions and subjects. We then map the synchronized fMRI data into a 3D tensor (vertices × time × subject/session). Finally, we apply Nesterov-accelerated adaptive moment estimation (Nadam) within a scalable and robust sequential Canonical Polyadic (CP) decomposition framework to identify a low rank tensor approximation to the data. As a result of CP tensor decomposition, we successfully identified twelve known brain networks with their corresponding temporal dynamics from 40 subjects using the Human Connectome Project's language task fMRI data without any prior information regarding the specific task designs. Seven of these networks show distinct subjects’ responses to the language task with differing temporal dynamics; two show sub-components of the default mode network that exhibit deactivation during the tasks; the remaining three components reflect non-task-related activities. We compare results to those found using group independent component analysis (ICA) and canonical ICA. Bootstrap analysis demonstrates increased robustness of networks found using the CP tensor approach relative to ICA-based methods. [Display omitted]