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The objective of this study is to implement deep neural network (DNN) models to classify autism spectrum disorder (ASD) patients and typically developing (TD) participants. The experimental design utilizes functional connectivity features extracted from resting-state functional magnetic resonance imaging (rs-fMRI) originating in the multisite repository Autism Brain Imaging Data Exchange (ABIDE) over a significant set of training samples. Our methodology and results have two main parts. First, we build DNN models using the TensorFlow framework in python to classify ASD from TD. Here we acquired an accuracy of 75.27%. This is significantly higher than any known accuracy (71.98%) using the same data. We also obtained a recall of 74% and a precision of 78.37%. In summary, and based on our literature review, this study demonstrated that our DNN (128-64) model achieves the highest accuracy, recall, and precision on the ABIDE dataset to date. Second, using the same ABIDE data, we implemented an identical experimental design with four distinct hidden layer configuration DNN models each preprocessed using four different industry accepted strategies. These results aided in identifying the preprocessing technique with the highest accuracy, recall, and precision: the Configurable Pipeline for the Analysis of Connectomes (CPAC).

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