It is well-established that graph neural networks (GNNs) can effectively model networked data in a variety of fields. However, whether GNNs can outperform traditional shallow graph classification models such as graph kernels for brain network analysis remains unclear. To this end, we analyze different approaches for modeling brain networks, including graph kernel based SVM, basic GNNs and kernelized GNNs. These models are designed to aid in the analysis of diseases and mental disorders such as bipolar disorder, human immunodeficiency virus (HIV), post-traumatic stress disorder (PTSD), and depression. In particular, we conduct experiments with three methods: kernelized support vector machines (SVM), message passing graph neural networks (MPGNNs), and kernel graph neural networks (KerGNN). We conclude that 1) deep models (GNNs) generally outperform shallow models (SVM) and 2) models considering specific graph motifs do not seem to significantly improve performance. We also identify other graph kernels and GNN frameworks that show promise in motivating further research in brain network analysis.
To analyze the abundance of multidimensional data, tensor-based frameworks have been developed. Traditionally, the matrix singular value decomposition (SVD) is used to extract the most dominant features from a matrix containing the vectorized data. While the SVD is highly useful for data that can be appropriately represented as a matrix, this step of vectorization causes us to lose the high-dimensional relationships intrinsic to the data. To facilitate efficient multidimensional feature extraction, we utilize a projection-based classification algorithm using the t-SVDM, a tensor analog of the matrix SVD. Our work extends the t-SVDM framework and the classification algorithm, both initially proposed for tensors of order 3, to any number of dimensions. We then apply this algorithm to a classification task using the StarPlus fMRI dataset. Our numerical experiments demonstrate that there exists a superior tensor-based approach to fMRI classification than the best possible equivalent matrix-based approach. Our results illustrate the advantages of our chosen tensor framework, provide insight into beneficial choices of parameters, and could be further developed for classification of more complex imaging data. We provide our Python implementation at this https URL.
Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating the average displacements in these regions, which have recently been proposed as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In numerical experiments, the segmentations of both methods were similar to manual segmentations provided by trained experts. It was found that, overall, the neural-network-based method alone produced more accurate segmentations than the atlas-based method did alone, but that a combination of the two methods – in which the atlas-based method is used for the segmentation of the brain stem and the neural-network is used for the segmentation of the cerebellum – may be the most successful.
Due to the COVID-19 pandemic, there is an increasing demand for portable CT machines worldwide in order to diagnose patients in a variety of settings . This has lead to a need for CT image reconstruction algorithms that can produce high-quality images in the case when multiple types of geometry parameters have been perturbed. In this paper, we present an alternating descent algorithm to address this issue, where one step minimizes a regularized linear least squares problem, and the other minimizes a bounded non-linear least-square problem. Additionally, we survey existing methods to accelerate the convergence algorithm and discuss implementation details through the use of MATLAB packages such as IRtools and imfil. Finally, numerical experiments are conducted to show the effectiveness of our algorithm.