Date Awarded

Fall 2016

Document Type


Degree Name

Master of Arts (M.A.)




Paul D Kieffaber

Committee Member

Matthew R Hilimire

Committee Member

Christopher C Conway


Autism spectrum disorder is a pervasive developmental disorder characterized by heterogeneous deficits in social communication and interaction, as well as repetitive behaviors and restricted interests. Due to the dramatic increase in prevalence, a major theme in contemporary research has been the identification of biomarkers for ASD that can shed light on etiological factors, facilitate diagnosis and serve as markers for tracking the efficacy of behavioral and pharmacological treatments. Electroencephalography (EEG) metrics, such as event-related potentials (ERPs), resting state oscillatory activity (OA), and resting state complexity (multiscale entropy), are well-suited for the measurement of such biomarkers. Due to the complexity and heterogeneity of ASD symptoms, it is important that research aiming to use EEG to identify biomarkers of autism and other neurodevelopmental disorders focus on determining the relationships between electrophysiological neurometrics and clinical presentation. The objective of the present research was two-fold; 1) synthesize a profile of ERP and OA metrics, collected during a novel Brief Neurometric Battery, that differentiates between youth with ASD and controls, and 2) determine if a relatively novel analysis of resting state EEG complexity (MSE) can be used to differentiate between ASD and controls. Through a two study approach, this research was able to synthesize a multivariate profile that classified youth with and without ASD at an accuracy rate comparable to that of the gold standard methods (ADI-R/ADOS) and identify an additional neurometric, multiscale entropy, that can accurately differentiate between youth with ASD and controls.




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