Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity networks (FCNs) have provided insights into explaining individual variation in cognitive and behavioral traits. In these studies, how to accurately construct FCNs is always important and challenging. In this paper, we propose a hypergraph learning based method, which constructs a hypergraph similarity matrix to represent the FCN with hyperedges being generated by sparse regression and their weights being learned by hypergraph learning. The proposed method is capable of better capturing the relations among multiple brain regions than the traditional graph based methods and the existing unweighted hypergraph based method. We then validate the effectiveness of our proposed method on the Philadelphia Neurodevelopmental Cohort data for classifying subjects’ learning ability levels, and discover potential imaging biomarkers which may account for a proportion of the variance in learning ability.
Functional connectivity (FC) analysis, which measures the connection between different brain regions, has been widely used to study brain function and development. However, FC-based analysis breaks the local structure in MRI images, resulting in a challenge for applying advanced deep learning models, e.g., convolutional neural networks (CNN). To fit the data in a non-Euclidean domain, graph convolutional neural network (GCN) was proposed, which can work on graphs rather than raw images, making it a suitable model for brain FC study. The small sample size is another challenge. Compared with natural images, medical images are usually limited in data sample size. Moreover, labeling medical images requires laborious annotation and is time-consuming. These limitations result in low accuracy and overfitting problem when training a conventional deep learning model on medical images. To address this problem, we employed a semi-supervised GCN with a Laplacian regularization term. By exploiting the between-sample information, semi-supervised GCN can achieve better performance on data with limited sample size. We applied the semi-supervised GCN model to a brain imaging cohort to classify the groups with different Wide Range Achievement Test (WRAT) scores. Experimental results showed semi-supervised GCN can improve classification accuracy, demonstrating the superior power of semi-supervised GCN on small datasets.
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