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

## TL;DR

This study uses fNIRS to examine brain activity in preschool children with ASD during emotional face processing and develops a deep learning model to classify emotions.

## Contribution

The study introduces a CNN-LSTM model for emotion recognition in preschool children with ASD using fNIRS data.

## Key findings

- Dynamic stimuli increased activation in the bilateral DLPFC and frontal pole compared to static stimuli.
- Angry expressions triggered the strongest neural responses across multiple brain regions.
- The CNN-LSTM model achieved 86.2% accuracy in classifying dynamic angry/happy emotions.

## Abstract

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.

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.

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.

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.