  Autism spectrum disorder is one of the leading neurodevelopmental disorders
in our world, present in over 1% of the population and rapidly increasing in
prevalence, yet the condition lacks a robust, objective, and efficient
diagnostic. Clinical diagnostic criteria rely on subjective behavioral
assessments, which are prone to misdiagnosis as they face limitations in terms
of their heterogeneity, specificity, and biases. This study proposes a novel
convolutional neural network-based classification tool that aims to identify
the potential of different neuroimaging features as autism biomarkers. The
model is constructed using a set of sequential layers specifically designed to
extract relevant features from brain imaging data. Trained and tested on over
300,000 distinct features across three imaging types, the model shows promise
in classifying individuals with autism from typical controls, outperforming
metrics of current gold standard diagnostics by achieving an accuracy of 95.4%
on a dataset of 1,111 samples with 521 autistic subjects (260 male and 261
female) and 590 controls (297 male and 293 female). 32 optimal features from
the training data were identified and classified as candidate biomarkers using
an independent samples t-test, in which functional features such as
connectivity and the time series of signal intensity from each voxel exhibited
the highest mean value differences between individuals with autism and typical
control subjects. The p-values of these biomarkers were < 0.001, proving the
statistical significance of the results and indicating that this research could
pave the way towards the usage of neuroimaging in conjunction with behavioral
criteria in clinics.
