UCLA

Autism Spectrum Disorders (ASD) are a collection of neurodevelopmental disorders with features of impairments in two domains: social communication, and restrictive repetitive behaviors/interests. Quantitative electroencephalography (QEEG) holds promise as a translational method for investigating abnormal neural oscillatory activity in ASD. Use of QEEG in ASD research is increasing, however discrepancies exist among the types of methods, artifact handling, and analyses researchers use. To facilitate comparing results across researchers, a solid foundation for use of QEEG in ASD research is needed. This dissertation builds a framework for using QEEG in future studies on children with ASD through a detailed description of data processing, artifact effects, statistical analysis methods, and differences in QEEG measures between children with ASD and typically developing controls. Chapter 2 presents a systematic approach to establishing clean, artifact-free data, and investigates the effect of artifacts on QEEG band power measures in children. We demonstrate that: (1) data quality is similar between a diverse group of children with ASD and a control group of typically developing children (TD), and (2) group differences in QEEG measures are not confounded by group differences in artifacts. Chapter 3 focuses on incorporating individual diversity observed in ASD into frequency band power analyses. It first demonstrates the need for including phenotypic information in QEEG analyses, and then describes frequency band power differences between typically developing children and children with ASD.