Abstract-Autism spectrum disorder (ASD) is regarded as a brain disease with globally disrupted neuronal networks. Even though fMRI studies have revealed abnormal functional connectivity in ASD, they have not reached a consensus of the disrupted patterns. Here, a deep learning-based feature extraction method identifies multivariate and nonlinear functional connectivity patterns of ASD. Resting-state fMRI data of 972 subjects (465 ASD 507 normal controls) acquired from the Autism Brain Imaging Data Exchange were used. A functional connectivity matrix of each subject was generated using 90 predefined brain regions. As a data-driven feature extraction method without prior knowledge such as subjects' diagnosis, variational autoencoder (VAE) summarized the functional connectivity matrix into 2 features. Those feature values of ASD patients were statistically compared with those of controls. A feature was significantly different between ASD and normal controls (p < 1×10-6). The extracted features were visualized by VAE-based generator which can produce virtual functional connectivity matrices. The ASD-related feature was associated with frontoparietal connections, interconnections of the dorsal medial frontal cortex and corticostriatal connections. It also showed a trend of negative correlation with full-scale IQ. A datadriven feature extraction based on deep learning could identify complex patterns of functional connectivity of ASD. This approach will help discover complex patterns of abnormalities in brain connectivity in various brain disorders. Index Terms-Autism spectrum disorder, deep learning, variational autoencoder, functional connectivity, fMRI
Deep learning-based feature extraction method has been used by Choi to identify multivariate and nonlinear functional connectivity patterns of ASD using DPARSF pipeline data in REF .
Functional connectivity patterns of autism spectrum disorder identified by deep feature learning
ArXiv
ArXiv
Computer Science Biology