EAPSI: Longitudinal Modeling of Neurocognitive and Psychosocial Trajectories in Children with Autism Spectrum Disorder

Han Gloria T, Nashville TN

Investigators

Abstract

In collaboration with Dr. Susan Gau, at National Taiwan University in Taipei, Taiwan, this project will develop and apply salient statistical methods in longitudinal data analysis to an ongoing longitudinal follow-up study investigating the development of neurocognitive functioning and psychosocial outcomes in children and adolescents with autism spectrum disorder (ASD). Dr. Gau is a distinguished expert and researcher in developmental psychology, cognitive neuroscience, and epidemiology. Importantly, this study is the first of its kind in facilitating a deeper cross-cultural understanding of ASD for Han Chinese using many levels of analysis, including neuroimaging, genetic, and behavioral measures. This project complements the researcher?s impending dissertation work regarding the development of statistical methods to best identify unique subgroups and trajectories of change in widely varied populations. Through the EAPSI award, the researcher will be able to test and compare different longitudinal modeling methods and identify informative subgroups in the study population based on distinct neurocognitive and psychosocial developmental trajectories. The project will provide insights in both quantitative methods for developmental scientists, and substantive identification of subgroups and distinct longitudinal trajectories for Han Chinese with ASD. The proposed study is situated at the intersection of research in developmental science and statistical methods, specifically the analysis and identification of distinct patterns of change within a population. Traditionally, studies may implement whole sample analyses that lack the specificity and means to identify subpopulations that are more vulnerable to psychosocial and neurocognitive disturbance. By focusing on the identification of subgroups and distinct trajectories of change, the researcher is able to identify protective predictors and risk factors that lead to various outcomes of interest. This study will compare methods such as growth curve modeling, growth mixture modeling, group-based trajectory modeling, and alternative non-parametric methods to identify the degree of convergence between models and assess model selection via substantive and statistical reasons. This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and isjointly funded by NSF and the Ministry of Science and Technology of Taiwan.