View article

Autism spectrum disorder (ASD) is a neurological developmental disorder that typically causes impaired communication and compromised social interactions. The current clinical assessment of ASD is typically based on behavioral observations and lack of the understanding of the neurological mechanism and the progression of the brain development. The functional magnetic resonance imaging (fMRI) data is one of the commonly-used imaging modalities for understanding human brain mechanisms as well as the diagnosis and treatment of brain disorders such as ASD. In this paper, we proposed a semi-supervised autoencoder (AE) for autism diagnosis using functional connectivity (FC) pattern obtained from resting-state fMRI. An unsupervised autoencoder in combination with the supervised classification networks enables semi-supervised learning in which an autoencoder for learning hidden features and a …