Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification
Open Access
- 11 January 2021
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Cell and Developmental Biology
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).Funding Information
- National Natural Science Foundation of China (51877013)
- Natural Science Foundation of Jiangsu Province (BK20181463)
This publication has 64 references indexed in Scilit:
- Effective connectivity analysis of fMRI data based on network motifsThe Journal of Supercomputing, 2013
- BrainNet Viewer: A Network Visualization Tool for Human Brain ConnectomicsPLOS ONE, 2013
- Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identificationBrain Structure and Function, 2013
- Identification of MCI individuals using structural and functional connectivity networksNeuroImage, 2012
- High Classification Accuracy for Schizophrenia with Rest and Task fMRI DataFrontiers in Human Neuroscience, 2012
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Enriched white matter connectivity networks for accurate identification of MCI patientsNeuroImage, 2011
- Partial Correlation Estimation by Joint Sparse Regression ModelsJournal of the American Statistical Association, 2009
- Partial correlation for functional brain interactivity investigation in functional MRINeuroImage, 2006
- High-dimensional graphs and variable selection with the LassoThe Annals of Statistics, 2006