  Autism spectrum disorder (ASD) is a prevalent psychiatric condition
characterized by atypical cognitive, emotional, and social patterns. Timely and
accurate diagnosis is crucial for effective interventions and improved outcomes
in individuals with ASD. In this study, we propose a novel Multi-Atlas Enhanced
Transformer framework, METAFormer, ASD classification. Our framework utilizes
resting-state functional magnetic resonance imaging data from the ABIDE I
dataset, comprising 406 ASD and 476 typical control (TC) subjects. METAFormer
employs a multi-atlas approach, where flattened connectivity matrices from the
AAL, CC200, and DOS160 atlases serve as input to the transformer encoder.
Notably, we demonstrate that self-supervised pretraining, involving the
reconstruction of masked values from the input, significantly enhances
classification performance without the need for additional or separate training
data. Through stratified cross-validation, we evaluate the proposed framework
and show that it surpasses state-of-the-art performance on the ABIDE I dataset,
with an average accuracy of 83.7% and an AUC-score of 0.832. The code for our
framework is available at https://github.com/Lugges991/METAFormer
