Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals

Autism spectrum disorder (ASD) is not a discrete disorder and that symptoms of ASD (i.e., so-called autistic traits) are found to varying degrees in the general population. Typically developing individuals with sub-clinical yet high-level autistic traits have similar abnormities both in behavioral performances and cortical activation patterns to individuals diagnosed with ASD. Thus it's crucial to develop objective and efficient tools that could be used in the assessment of autistic traits. Here, we proposed a novel machine learning-based assessment of the autistic traits using EEG microstate features derived from a brief resting-state EEG recording. The results showed that: (1) through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and correlation analysis, the mean duration of microstate class D, the occurrence rate of microstate class A, the time coverage of microstate class D and the transition rate from microstate class B to D were selected to be crucial microstate features which could be used in autistic traits prediction; (2) in the support vector regression (SVR) model, which was constructed to predict the participants' autistic trait scores using these four microstate features, the out-of-sample predicted autistic trait scores showed a significant and good match with the self-reported scores. These results suggest that the resting-state EEG microstate analysis technique can be used to predict autistic trait to some extent.

Automated summary

Autism spectrum disorder (ASD) is a disorder that exists to varying degrees in the general population. This study proposes a machine learning-based method using EEG microstate features to assess autistic traits. The results show that these microstate features can be used to predict autistic traits.