Conditional entropy approach to analyze cognitive dynamics in autism spectrum disorder

Preliminary evidence has documented functional connectivity during the cognitive task in Autism Spectrum Disorder (ASD). However, evidence of effective neural connectivity with respect to information flow between different brain regions during complex tasks is missing. The present paper aims to provide insights into the cognition-based neural dynamics reflecting information exchange in brain network under cognitive load in ASD.

Twenty-two individuals with ASD (8-18 years) and 18 Typically Developing (TD; 6-17 years) individuals participated in the cognitive task of differentiating risky from neutral stimuli. The Conditional Entropy (CE) technique is applied upon task-activated Electroencephalogram (EEG) to measure the causal influence of the activity of brain's one Region of interest (ROI) over another.

A higher CE in frontal ROI and left hemisphere reflected atypical brain complexity in ASD. The absence of causal effect, poor Coupling Strength (CS; measured using CE) and hemisphere lateralization is responsible for lower cognition in ASD. However, the persistent information exchange during the task reflects the existence of certain alternative paths when other direct paths remained disconnected due to cognitive impairment. The Support Vector Machine (SVM) classifier showed that CE can identify the atypical information exchange with an accuracy of 96.89% and area under curve = 0.987.

The statistical results reflect a significant change in the information flow between different ROIs in ASD. A correlation of CS and behavioral domain suggests that the cognitive decline could be predicted from the connectivity patterns. Thus, CS could be a potential biomarker to identify cognitive status at a higher discrimination rate in ASD.