Background and Objectives: Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are the two most common neurodevelopmental disorders often with overlapping symptoms. Misdiagnosis of these disorders is the leading cause of a variety of problems including inappropriate interventions and improper treatment outcome. Over the last few years, resting state functional magnetic Resonance imaging (rs-fMRI) has received clinical attention among other beneficial brain scan techniques to extract functional connectivity in the brain. However, extracting useful information by human observation is prone to errors.
Material and Methods: The above unmet need prompted us to design the present investigation to construct a convolutional neural network model with 12 layers architecture in rsFMRI data aiming to differentiate the two conditions. The rs-fMRI data was collected from the ADHD-200 and ABIDE to feed into a convolutional neural network. Over the preprocessing phase, we have removed undesirable data and coordinated the remaining to MSDL atlas to recruit 39 regions of the brain.
Results: Ultimately, out results obtained a 0.92 accuracy, an AUC of 0.97 and loss of 0.17 in classification and discrimination of ADHD and ASD.
onclusion: Though cross-validity with larger datasets is deemed required, the results obtained from the present investigation suggest that convolutional neural network may serve as a beneficial tool to differentiate ADHD and ASD from relatively small fMRI datasets. This further highlights the potential application of deep neural networks for serving the above purpose.