Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification

Abstract

Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer's disease (AD).

Keywords: classification; dynamic functional network (DFN); early mild cognitive impairment (eMCI); tensor low-rank approximation (TLA); weighted regularization (WR).

FIGURE 1

The framework of constructing DFN via WRTLA for eMCI classification, including the following steps: (A) Preprocessing the original resting fMRI data of normal subjects and eMCI subjects, (B) Extracting the time series containing all brain regions according to the AAL template, (C) Using sliding window method to divide the entire time series into several overlapping segments, (D) Introducing the WR term into the construction of DFNs to obtain WRDFNs, (E) Stacking all WRDFNs of subjects into a tensor and optimizing it using TLA and obtaining WRTDFNs, (F) Calculating and extracting the weighted-graph local clustering coefficient of each brain region in WRTDFNs as the effective feature, and using the t-test to select features, and (G) Training a linear SVM classifier to classify the WRTDFNs of all subjects and evaluating the classification performance.

FIGURE 2

Visualized results of brain functional networks in the same time window. The brain functional network in (A) is dense, whereas the brain functional network in (B) is sparse, and there is a lot of noise in it. The topologies of brain functional networks in (F,G) are clearer than those in (A,B) respectively, which indicate that TLA has not changed topologies much but effectively removed some noise connections to improve qualities of brain functional networks, which have certain modularity. The brain functional network in (E) is sparser than that in (F), and the brain functional network in (H) and (I) is sparser and more modular than those in (A,B) respectively. In (D,J), some strong functional connections are enhanced, while some weak functional connections are suppressed, which reflects the effectiveness of introducing the weight penalty regularization term. In addition, as shown in (C), we obtain a clearer brain functional network with Hub structure because of the WR term, but some strong functional connections are also penalized.

FIGURE 3

Classification performance of WRTLA with different regularization parameters.

FIGURE 4

Visualization of discriminative brain regions. (A) Cornal plane. (B) Axis plane. (C) Sagittal plane.

FIGURE 5

Top 100 functional connections with the highest correlation coefficients in AFN. (A) Normal subjects. (B) eMCI subjects.

FIGURE 6

Functional connections between discriminative regions in AFN. (A) Normal subjects. (B) eMCI subjects.