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

Abstract

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 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 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).

Keywords: Alzheimer’s disease; dynamic brain functional network; hyper-graph; manifold regularization; mild cognitive impairment.

FIGURE 1

The framework of constructing SDBFNs via SHMR for MCI classification. The area marked in red box is the key research part. (a) Preprocessing the obtained resting-state fMRI data of two types of subjects; (b) registering the preprocessed resting-state fMRI data to 90 brain regions according to the AAL template, and obtaining the time series of all brain regions; (c) dividing the entire time series into multiple overlapping sub-sequence segments by sliding window method; (d) constructing DBFNs based on the PC method and transforming it into an optimized model; (e) constructing hyper-graphs based on DBFNs and obtaining hyper-graph Laplacian matrices; (f) constructing the manifold regularizer by hyper-graph Laplacian matrices, and introducing the manifold regularizer and L1-norm regularizer into the optimization model of the PC method to obtain SDBFNs; (g) extracting the weighted-graph local clustering coefficient of each brain region in SDBFNs, and using the t-test for feature selection; and (h) training a linear kernel SVM classifier to classify the SDBFNs of all subjects and analyzing the classification performance.

FIGURE 2

Classification performance of SDBFNs obtained by different regularization parameters: (A) ACC, (B) SEN, (C) SPE, and (D) AUC.

FIGURE 3

Visualization results of constructing the BFN in the same time window by different methods. (A) PC, (B) SR, (C) MR, (D) SMR, (E) HMR, and (F) SHMR.

FIGURE 4

Number of features selected by different methods in 10-fold cross-validation.

FIGURE 5

The layouts of discriminative brain regions. (A) Coronary figure. (B) Axis figure. (C) Sagittal figure.