Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification

• Published in: Frontiers in neuroscience Vol. 15; p. 669345
• Main Authors: Ji, Yixin, Zhang, Yutao, Shi, Haifeng, Jiao, Zhuqing, Wang, Shui-Hua, Wang, Chuang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 01.04.2021; Frontiers Media S.A
• Subjects: Alzheimer's disease; Brain diseases; Brain research; Classification; Cognitive ability; Diagnosis; dynamic brain functional network; Feature selection; Generalized linear models; hyper-graph; Magnetic resonance imaging; manifold regularization; Methods; mild cognitive impairment; Neurodegenerative diseases; Neuroscience; Sparsity; Time series; manifold regularization; dynamic brain functional network; hyper-graph; mild cognitive impairment; Alzheimer’s disease
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2021.669345

Brain functional networks (BFNs) constructed via manifold regularization (MR) have emerged as a powerful tool in finding new biomarkers for brain disease diagnosis. However, they only describe the pair-wise relationship between two brain regions, and cannot describe the functional interaction between multiple brain regions, or the high-order relationship, well. To solve this issue, we propose a method to construct dynamic BFNs (DBFNs) via hyper-graph MR (HMR) and employ it to classify mild cognitive impairment (MCI) subjects. First, we construct DBFNs via Pearson ’s correlation (PC) method and remodel the PC method as an optimization model. Then, we use k -nearest neighbor (KNN) algorithm to construct the hyper-graph and obtain the hyper-graph manifold regularizer based on the hyper-graph. We introduce the hyper-graph manifold regularizer and the L 1-norm regularizer into the PC-based optimization model to optimize DBFNs and obtain the final sparse DBFNs (SDBFNs). Finally, we conduct classification experiments to classify MCI subjects from normal subjects to verify the effectiveness of our method. Experimental results show that the proposed method achieves better classification performance compared with other state-of-the-art methods, and the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under the curve (AUC) reach 82.4946 ± 0.2827%, 77.2473 ± 0.5747%, 87.7419 ± 0.2286%, and 0.9021 ± 0.0007, respectively. This method expands the MR method and DBFNs with more biological significance. It can effectively improve the classification performance of DBFNs for MCI, and has certain reference value for the research and auxiliary diagnosis of Alzheimer’s disease (AD).