Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection.

ABSTRACT: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performanc

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11951341 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature