  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. 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 data-driven 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.
