  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.
