Brief Report: Classification of Autistic Traits According to Brain Activity Recoded by fNIRS Using ε-Complexity...

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Individuals with autism spectrum disorder (ASD) exhibit distinct patterns of brain functional connectivity.
• Interpersonal synchronization tasks reveal differences in brain region coordination.

Purpose of the Study:

• To investigate differences in brain activity between individuals with high and low autistic traits during an interpersonal synchronization task.
• To introduce and validate a novel signal complexity analysis method for functional near-infrared spectroscopy (fNIRS) data.

Main Methods:

• Utilized functional near-infrared spectroscopy (fNIRS) to record brain activity.
• Employed a novel signal complexity assessment using ε-complexity coefficients on fNIRS data.
• Applied the method to classify brain recordings from participants with varying autistic traits.

Main Results:

• Successfully classified brain recordings based on the number of autistic traits.
• Demonstrated a significant difference in brain activity patterns between groups during the task.
• Indicated the potential utility of ε-complexity for fNIRS data classification.

Conclusions:

• The novel ε-complexity method is effective for classifying fNIRS recordings.
• Significant differences in brain activity exist between individuals with high and low autistic traits during interpersonal synchronization.
• This approach may offer a new tool for understanding neural underpinnings of ASD.