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Autism Spectrum Disorder is one of the leading neurodevelopmental disordersin our world, present in over 1% of the population and rapidly increasing inprevalence, yet the condition lacks a robust, objective, and efficientdiagnostic. Clinical diagnostic criteria rely on subjective behavioralassessments, which are prone to misdiagnosis as they face limitations in termsof their heterogeneity, specificity, and biases. This study proposes a novelconvolutional-neural-network based classification tool that aims to identifythe potential of different neuroimaging features as autism biomarkers. Themodel is constructed using a set of sequential layers specifically designed toextract relevant features from brain scans. Trained and tested on over 300,000distinct features across three imaging types, the model shows promisingresults, achieving an accuracy of 95.4% and outperforming metrics of currentgold standard diagnostics. 32 optimal features from the imaging data wereidentified and classified as candidate biomarkers using an independent samplest-test, in which functional features such as neural activity and connectivityin various brain regions exhibited the highest differences in the mean valuesbetween individuals with autism and typical control subjects. The p-values ofthese biomarkers were < 0.001, proving the statistical significance of theresults and indicating that this research could pave the way towards the usageof neuroimaging in conjunction with behavioral criteria in clinics.Furthermore, the salient features discovered in the brain structure ofindividuals with autism could lead to a more profound understanding of theunderlying neurobiological mechanisms of the disorder, which remains one of themost substantial enigmas in the field even today.

Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism  Spectrum Disorder Through Convolutional Neural Network-Based Analysis of  Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced  Autism Diagnosis