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Cognitive and behavioral flexibility necessitate mental and behavioral changes in response to changing environmental demands. Cognitive flexibility, or the mental ability to change from one thought to another, supports positive life outcomes. Individuals diagnosed with autism spectrum disorder (ASD) often face challenges with cognitive flexibility as exhibited by a core symptom of restricted and repetitive interests and behaviors (RRBs) and having poor adaptive behavior skills. ASD is rising each year, with one in 36 children diagnosed annually. Early diagnosis and treatment are critical for improving life outcomes. This makes it even more important to investigate the neural underpinnings of cognitive flexibility during early stages of development and across the lifespan to improve treatments and interventions. This dissertation seeks to understand the neural mechanisms underlying healthy cognitive flexibility development and the cognitive inflexibility observed in ASD. Chapter 1 includes an introduction to the research involved in the following chapters, and provides a review of cognitive flexibility and its neural correlates in typical development and in ASD. Chapter 2 includes a study exploring brain dynamics and cognitive flexibility across the lifespan in a neurotypical population. Using a co-activation pattern analysis method to investigate brain dynamics, brain networks including the salience (SN), default mode (DMN), and central executive networks (CEN) underlie changes in cognitive flexibility across the lifespan. Chapter 3 then explores the neural correlates of shifting in children with and without autism during a task and resting state fMRI. Results of this study further revealed brain dynamics among the SN, DMN and CEN were involved in shifting abilities and abnormalities in these dynamics may underlie differences observed in children with ASD. Chapter 4 then examines early brain dynamics in toddlers with and without ASD and their relationship with flexi

BackgroundSocial and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown.MethodsWe collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child's social and language abilities were examined.ResultsWhile there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal-visual region connectivity and communication ability (r(49) = 0.55, p < 0.001) and between temporal-precuneus connectivity and expressive language ability (r(49) = 0.58, p < 0.001).LimitationsThe distinct connectivity-behavior correlation patterns may be related to different developmental stages in ASD and non-ASD subjects. The use of a prior 2-year-old template for spatial normalization may not be optimal for a few subjects beyond this age range.ConclusionsSuperior temporal cortex is known to have reduced activation to social affective speech in ASD at early ages, and here we find in ASD toddlers that it also has atypical connectivity with visual and precuneus cortices that is correlated with communication and lang

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.