ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding

Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu

Abstract: Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication and social interaction. Accurate and early diagnosis of ASD is crucial for effective interventions. The brain functional connectome, which refers to the statistical relationships between different brain regions measured through neuroimaging, provides crucial insights into brain function. Traditional static brain analysis methods often fail to capture the dynamic nature of the brain activity. In contrast, dynamic brain analysis provides a more comprehensive view by capturing the temporal variations in the brain. This work proposes a novel graph-level dynamic brain network embedding approach using Temporal Random Walk with Transformer-based model (BrainTWT) that captures the temporal evolution of brain connectivity over time and considers also the dynamics between different temporal network snapshots. BrainTWT employs temporal random walks to capture dynamics across different temporal network snapshots and leverages the Transformer’s ability to model long-term dependencies in sequential data to learn discriminative embeddings from these temporal sequences using temporal structure prediction tasks. Experimental evaluation using the Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrates that BrainTWT outperforms baseline methods in ASD classification.

External IDs:dblp:conf/ijcnn/PiriyasatitYK25