HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification

• URL: http://arxiv.org/abs/2311.02903v1
• Date: Mon, 6 Nov 2023 06:29:23 GMT
• Title: HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
• Authors: Parniyan Jalali, Mehran Safayani
• Abstract summary: We propose a hierarchical dynamic graph representation learning (HDGL) model, which is the first model designed to address all the aforementioned challenges. We evaluate the performance of the proposed model on the ABIDE and ADHD-200 datasets.
• Score: 1.7495515703051119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The human brain can be considered as complex networks, composed of various regions that continuously exchange their information with each other, forming the brain network graph, from which nodes and edges are extracted using resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, this graph can potentially depict abnormal patterns that have emerged under the influence of brain disorders. So far, numerous studies have attempted to find embeddings for brain network graphs and subsequently classify samples with brain disorders from healthy ones, which include limitations such as: not considering the relationship between samples, not utilizing phenotype information, lack of temporal analysis, using static functional connectivity (FC) instead of dynamic ones and using a fixed graph structure. We propose a hierarchical dynamic graph representation learning (HDGL) model, which is the first model designed to address all the aforementioned challenges. HDGL consists of two levels, where at the first level, it constructs brain network graphs and learns their spatial and temporal embeddings, and at the second level, it forms population graphs and performs classification after embedding learning. Furthermore, based on how these two levels are trained, four methods have been introduced, some of which are suggested for reducing memory complexity. We evaluated the performance of the proposed model on the ABIDE and ADHD-200 datasets, and the results indicate the improvement of this model compared to several state-of-the-art models in terms of various evaluation metrics.

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