Accessing dynamic functional connectivity using l0-regularized sparse-smooth inverse covariance estimation from fMRI
Document Type
Article
Publication Date
7-5-2021
Abstract
Inferring dynamic functional connectivity (dFC) from functional magnetic resonance imaging (fMRI) is crucial to understand the time-variant functional inter-relationships among brain regions. Because of the sparse property of functional connectivity networks, sparsity-promoting dFC estimation methods, which are mainly based on l1-norm regularization, are gaining popularity. However, l1-norm regularization cannot provide the maximum sparsity solution as the most natural sparsity promoting norm, the l0-norm. But l0-norm is seldom used to infer sparse dFC because an efficient algorithm to address the non-convexity problem of l0-norm is lacking. In this work, we develop a new l0-norm regularization-based inverse covariance estimation method for estimating dFC from fMRI. This novel method employs l0-norm regularizations on both spatial and temporal scales to enhance the spatial sparsity and temporal smoothness of dFC estimates. To overcome the non-convexity of l0-norm, we further propose an effective optimization algorithm based on the coordinate descent (CD). The performance of the proposed l0-norm-based sparse-smooth regularization (L0-SSR) method is examined using a series of synthetic datasets concerning various types of network topology. We further apply the proposed L0-SSR method to real fMRI data recorded in block-design motor tasks from 45 participants for the exploration of task induced dFC. Results on synthetic and real-world fMRI data show that, the L0-SSR method can achieve more accurate and interpretable dFC estimates than conventional l1-norm-based dFC estimation methods. Hence, the proposed L0-SSR method could serve as a powerful analytical tool to infer highly complex, variable, and sparse dFC patterns.
Identifier
85103108600 (Scopus)
Publication Title
Neurocomputing
External Full Text Location
https://doi.org/10.1016/j.neucom.2021.02.081
e-ISSN
18728286
ISSN
09252312
First Page
147
Last Page
161
Volume
443
Grant
81871443
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Zhang, Li; Fu, Zening; Zhang, Wenwen; Huang, Gan; Liang, Zhen; Li, Linling; Biswal, Bharat B.; Calhoun, Vince D.; and Zhang, Zhiguo, "Accessing dynamic functional connectivity using l0-regularized sparse-smooth inverse covariance estimation from fMRI" (2021). Faculty Publications. 3969.
https://digitalcommons.njit.edu/fac_pubs/3969
