A novel ADHD classification method based on resting state temporal templates (RSTT) using spatiotemporal attention auto-encoder

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and related disorders based on resting state functional magnetic resonance imaging (rfMRI). Although a variety of deep learning models have been proposed for modeling rfMRI, the dominant models are limited in capturing the long-distance dependency (LDD) due to their sequential nature. In this work, we propose a spatiotemporal attention auto-encoder (STAAE) to discover global features that address LDDs in volumetric rfMRI. The unsupervised STAAE framework can spatiotemporally model the rfMRI sequence and decompose the rfMRI into spatial and temporal patterns. The spatial patterns have been extensively explored and are also known as resting state networks (RSNs), yet the temporal patterns are underestimated in last decades. To further explore the application of temporal patterns, we developed a resting state temporal template (RSTT)-based classification framework using the STAAE model and tested it with attention-deficit hyperactivity disorder (ADHD) classification. Five datasets from ADHD-200 were used to evaluate the performance of our method. The results showed that the proposed STAAE outperformed three recent methods in deriving ten well-known RSNs. For ADHD classification, the proposed RSTT-based classification framework outperformed methods in recent studies by achieving a high accuracy of 72.5%. Besides, we found that the RSTTs derived from NYU dataset still work on the other four datasets, but the accuracy on different test datasets decreased with the increase in the age gap to NYU dataset, which likely supports the idea of that there exist age differences of brain activity among ADHD patients.