Chapter 6 - Deriving resting-state fMRI biomarkers for classification of autism spectrum disorder

Abstract

The purpose of the current study was to classify people with autism spectrum disorder (ASD) using resting state functional magnetic resonance imaging data. Toward this direction, data were retrieved from the Autism Brain Imaging Data Exchange initiative, based on the Harvard–Oxford parcellation scheme, focusing on the default mode network, which has been previously reported to be related with ASD. The extracted time series were used to calculate plethora of functional features quantifying within-network connections, which were set as input to a medical decision support system (DSS). These interactions were evaluated based on a broad variability of methods, such as static functional connectivity (sFC) and dynamic functional connectivity (dFC) analysis, information-based metrics, and adjusted Haralick texture features. Finally, after extensive trials, head motion parameters, age, sex, and information regarding the acquisition protocol were found to improve the overall performance of the DSS. Internal parameters of the DSS were chosen based on a Bayesian optimization framework, which aimed to maximize the area under curve. The DSS comprised of a common support vector machine classifier featuring several kernels such as linear, polynomial, and radial basis function (RBF).