Diagnosis of Brain Disorders Employing Brain Biomedical Data

Abstract

Brain disorders have been widely detected in recent years; however, their diagnosis is based on symptom reports performed by patients or profession-als without clinical or quanti ed judgments. Hence, it is prone to human mistakes. There is an urge for an objective computer assisted diagnosing system. One of the challenging brain disorders is the autism spectrum dis-order (ASD) which is a neuro-developmental disorder associated with im-pairments in social and lingual abilities. Failure in language development is variable in the ASD population and follows a wide spectrum. The autism diagnostic observation schedule (ADOS) is the current gold standard for di-agnosing, supported by expert clinical judgment. Early diagnosis allows for early intervention to reduce the severity of autism. Brain scanning technolo-gies have been widely developing and acquired extensively to understand brain functionality and structure. Magnetic resonance imaging (MRI) is a medical scanning technique that uses strong magnetic elds to form images of the anatomy and the physiological functionality of the brain. Main types of MRI include structural, resting-state functional MRI and task-based func-tional MRI (TfMRI). TfMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in re-sponse to certain tasks. The aim of this thesis is to develop a personalized computer-aided diagnosis (CAD) and grading system to classify autistic sub-jects against typically developed peers. A novel computer-aided ASD grad-ing framework, dependent on the analysis of brain activation in a response to speech experiment, is proposed. Increased hypoactivation of the superior