Why the study?
Feature extraction and recognition in brain signal processing is significant for understanding the neurological mechanism of ASD, and resting-state fNIRS provides a way to investigate alterations in complexity and its relationship with functional connectivity.
Does multiscale entropy of resting-state fNIRS signals differ between children with ASD and typical development children?
Does multiscale entropy of resting-state fNIRS signals differ between children with ASD and typical development children?
Multiscale entropy of resting-state fNIRS signals shows increased brain complexity in children with ASD, suggesting its potential as a novel biomarker for characterizing the disorder.
Increased fNIRS complexity may support ASD biomarker exploration; hypothesis-generating and requires prospective validation before clinical adoption.
Objective. Feature extraction and recognition in brain signal processing is of great significance for understanding the neurological mechanism of autism spectrum disorder (ASD). Resting-state (RS) functional near-infrared spectroscopy measurement provides a way to investigate the possible alteration in ASD-related complexity of resting-state (RS) functional near-infrared spectroscopy (fNIRS) signals and to explore the relationship between brain functional connectivity and complexity. Approach. Using the multiscale entropy (MSE) of fNIRS signals recorded from the bilateral temporal lobes (TLs) on 25 children with ASD and 22 typical development (TD) children, the pattern of brain complexity was assessed for both the ASD and TD groups. Main results. The quantitative analysis of MSE revealed the increased complexity in RS-fNIRS in children with ASD, particularly in the left temporal lobe. The complexity in the RS signal and resting state functional connectivity (RSFC) were also observed to exhibit negative correlation in the medium magnitude. Significance. These results indicated that the MSE might serve as a novel measure for RS-fNIRS signals in characterizing and understanding ASD.
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Zhang et al. (2021) studied this question.
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