Diagnosing Autism Spectrum Disorder through Brain Functional Magnetic Resonance Imaging

Autism spectrum disorder (ASD) is a neurodevelopmental condition that can be debilitating to social functioning. Previous functional Magnetic Resonance Imaging (fMRI) classification studies have included only small subject sample sizes (n 50) and have seen high classification accuracy. The recent release of the Autism Brain Imaging Data Exchange (ABIDE) provides fMRI data for over 1,100 subjects. In our research, we derive a subject\u27s functional network connectivity (FNC) from their fMRI data and develop a regularized logistic classifier to determine whether a subject has autism. We obtained up to 65% classification accuracy, similar to other studies using the ABIDE dataset, suggesting that generalizing a classifier over a large number of subjects is much more difficult than smaller studies. The connectivity among several brain regions of ASD subjects were highlighted in the model as abnormal compared to the control subjects which potentially warrants future investigations about how these regions affect ASD. Although the classification accuracy was lower than what could be considered as clinically applicable, this research contributes to the continuing development of an automated classifier for diagnosing autism

A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions to provide computational support for early identification of ADHD patients that enable early treatments