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Lurie, D.J., Kessler, D., Bassett, D.S., Betzel, R.F., Breakspear, M., Kheilholz, S., Kucyi, A., Liegeois, R., Lindquist, M.A., McIntosh, A.R., et al. (2020) Questions and Controversies in the Study of Time-Varying Functional Connectivity in Resting fMRI. Network Neuroscience, 4, 30-69.
https://doi.org/10.1162/netn_a_00116

has been cited by the following article:

• TITLE: Semi-Supervised Graph Learning for Brain Disease Identification

  AUTHORS: Kunpeng Zhang, Yining Zhang, Xueyan Liu

  KEYWORDS: Graph Learning, Mild Cognitive Impairment, Autism Spectrum Disorder

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.7, July 18, 2023

  ABSTRACT: Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely used, where graph represents the similarity between patients or brain regions of interest. In these models, constructing high-quality graphs is of paramount importance. Researchers have proposed various methods for constructing graphs from different perspectives, among which the simplest and most popular one is Pearson Correlation (PC). Although existing methods have achieved significant results, these graphs are usually fixed once they are constructed, and are generally operated separately from downstream task. Such a separation may result in neither the constructed graph nor the extracted features being ideal. To solve this problem, we use the graph-optimized locality preserving projection algorithm to extract features and the population graph simultaneously, aiming in higher identification accuracy through a task-dependent automatic optimization of the graph. At the same time, we incorporate supervised information to enable more flexible modelling. Specifically, the proposed method first uses PC to construct graph as the initial feature for each subject. Then, the projection matrix and graph are iteratively optimized through graph-optimization locality preserving projections based on semi-supervised learning, which fully employs the knowledge in various transformation spaces. Finally, the obtained projection matrix is applied to construct the subject-level graph and perform classification using support vector machines. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs), and the results showed that the classification performance of our method is better than that of the baseline method.