Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

• Published in: Frontiers in aging neuroscience Vol. 15; p. 1101879
• Main Authors: Du, Yue, Wang, Guangyu, Wang, Chengcheng, Zhang, Yangyang, Xi, Xiaoming, Zhang, Limei, Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 16.02.2023; Frontiers Media S.A
• Subjects: Aging Neuroscience; Alzheimer's disease; Brain diseases; Brain mapping; Brain research; Classification; Cognitive ability; disease identification; functional brain network; Functional magnetic resonance imaging; Information processing; Laplacian matrix; Methods; mild cognitive impairment; modularity; Neurodegenerative diseases; Neuroimaging; Neurological diseases; Time series; functional brain network; Laplacian matrix; mild cognitive impairment; disease identification; modularity
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2023.1101879

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain. To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules. To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.