Establishing the sensitivity of functional near-infrared spectroscopy to cognitive load and state in healthy adults and children with Autism Spectrum Disorder

Abstract

Functional near-infrared spectroscopy (fNIRS) is an emerging low-cost noninvasive neuroimaging technique that measures cortical blood flow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the sensitivity to serve as a substitute for fMRI in resolving current developmental cognitive neuroscience research questions. In order for this to happen, fNIRS must first be validated on a pediatric population that is problematic for fMRI and upon cognitive processes that are known to be affected in the pediatric populations of interest. To this end, I have selected Autism Spectrum Disorder (ASD) and working memory as the validation population and cognitive process, respectively. Current theories of ASD suggest that the symptoms are caused by disruptions in communication between brain regions. This is most commonly operationalized as functional connectivity, the temporal correlation of brain activity. While most studies of have measured functional connectivity at rest, we explore the dynamic modulation of functional connectivity across multiple cognitive states. Such modulations are thought to reflect the adaptive recruitment of brain networks to meet the processing demands of the moment, and prior work suggests this may be key in understanding the neural basis of ASD. The present dissertation sets out to 1) establish the sensitivity of fNIRS to working memory load and cognitive state in healthy adults, 2) develop methods for improved preprocessing and statistical analysis of fNIRS data, and 3) demonstrate that fNIRS can detect activation and functional connectivity differences between ASD children and typically-developing controls as a function of working memory load and cognitive state.