SPAE: Spatial Preservation-based Autoencoder for ADHD functional brain networks modelling

Spatio-temporal modelling based on resting-state functional magnetic resonance imaging (rsfMRI) of ADHD has been a major concern in the neuroimaging community, given the differences in the role of brain regions between attention deficit hyperactivity disorder (ADHD) patients versus typical developmental control group (TC). Several spatio-temporal deep learning models are proposed for rsfMRI, however, due to the high dimensionality and few samples of brain data, most models use dimension-reduced data as input for modelling, which suffer from the loss of original spatial relationships in the brain data. Although Recurrent Neural Network (RNN) and Attention mechanism (Attention) proposed in recent years can extract local correlations and long-distance dependency (LDD), the spatio-temporal relationships they rely on have lost their original high-dimensional spatial relevance. Therefore, a spatial preservation-based autoencoder for modelling ADHD functional brain networks (FBNs) is proposed by embedding the spatial information and combining both RNN and Transformer to address the issue that the dimension-reduced data cannot preserve the original high-dimensional spatial correlations. Firstly, a spatial preservation module is designed to fill the gap between the original data and the dimension-reduced data. Secondly, the dimension reduction module and feature extraction module are designed to improve the representation of spatio-temporal correlations. Thirdly, the extracted FBNs are applied to the disease classification on the ADHD-200 dataset, which show the model's effectiveness in classifying ADHD compared with the state-of-the-art methods. Finally, we investigate the differences in regional correlations between ADHD and TC.