Cortical hemodynamic responses and deep learning models of emotional face processing in preschool children with autism spectrum disorder: a fNIRS study

Purpose: The purpose of the present study was to characterize cortical hemodynamic responses during emotional face processing in preschool children with autism spectrum disorder (ASD) using functional near-infrared spectroscopy (fNIRS), and to develop machine learning frameworks for emotion recognition based on these hemodynamic signals. Methods: Fifty-three ASD preschoolers (41 males, 12 females; aged 3-7 years, mean age 5.20 ± 1.23 years) were exposed to dynamic video and static image facial stimuli displaying angry, happy expressions, and neutral flowers, with their brain activity concurrently recorded using whole-brain fNIRS. A convolutional neural network-long short-term memory (CNN-LSTM) model was proposed to decode spatiotemporal neural patterns of angry/happy emotion recognition. Results: fNIRS analysis revealed significantly enhanced activation in bilateral dorsolateral prefrontal cortex (DLPFC) and frontal pole during dynamic versus static stimulus processing. Angry expressions elicited the most pronounced neural responses, engaging a distributed cortical areas involving DLPFC, ventrolateral prefrontal cortex, and primary visual areas. The CNN-LSTM architecture achieved 86.2% accuracy in dynamic angry/happy emotion classification. Conclusion: This study provides evidence of altered cortical hemodynamics during dynamic emotional facial processing and demonstrates the feasibility of CNN-LSTM models for the objective assessment of emotional facial processing potential in preschool children with ASD.