  Autism spectrum disorder (ASD) is a highly disabling mental disease that
brings significant impairments of social interaction ability to the patients,
making early screening and intervention of ASD critical. With the development
of the machine learning and neuroimaging technology, extensive research has
been conducted on machine classification of ASD based on structural MRI
(s-MRI). However, most studies involve with datasets where participants' age
are above 5. Few studies conduct machine classification of ASD for participants
below 5-year-old, but, with mediocre predictive accuracy. In this paper, we
push the boundary of predictive accuracy (above 0.97) of machine classification
of ASD in children (age range: 0.92-4.83 years), based on s-MRI features
extracted using contrastive variational autoencoder (CVAE). 78 s-MRI, collected
from Shenzhen Children's Hospital, are used for training CVAE, which consists
of both ASD-specific feature channel and common shared feature channel. The ASD
participants represented by ASD-specific features can be easily discriminated
from TC participants represented by the common shared features, leading to high
classification accuracy. In case of degraded predictive accuracy when data size
is extremely small, a transfer learning strategy is proposed here as a
potential solution. Finally, we conduct neuroanatomical interpretation based on
the correlation between s-MRI features extracted from CVAE and surface area of
different cortical regions, which discloses potential biomarkers that could
help target treatments of ASD in the future.
