Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification - Ji, Yixin; Zhang, Yutao; Shi, Haifeng; Jiao, Zhuqing; Wang, Shui-Hua; Wang, Chuang (Frontiers in Neuroscience, 2021)
Currently, brain functional networks (BFNs) constructed via manifold regularization (MR) have emerged as a powerful tool in finding the 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, that is the high-order relationship well. To solve this issue, we propose a method to construct dynamic brain functional networks (DBFNs) via hyper-graph manifold regularization (HMR), and employ it to classify mild cognitive impairment (MCI) subjects. Firstly, 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 L1-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 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).