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

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Abstract

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.

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Data Availability

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Funding

The work was supported by Post-funded Project of the National Social Science Fund under Grant 20FJKB005, Henan Province Philosophy and Social Sciences Outstanding Scholars Project under Grant 2018-YXXZ-03, the Philosophy and Social Sciences Planning Project of Henan Province under Grant 2020BJY010, Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University under Grant SYLYC2022039, Henan University Philosophy and Social Science Innovation Team under Grant 2019CXTD009, the China Postdoctoral Science Foundation under Grant 2022M721016 and Henan Province Higher Education Teaching Reform Research and Practice Project under Grant 2021SJGLX330.

Conflict of Interest

The authors have no relevant financial or non-financial interests to disclose.

Author information

Authors and Affiliations

  1. Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China

    Huibin Jia, Xiangci Wu, Xiaolin Zhang, Meiling Guo & Enguo Wang

  2. School of Psychology, Henan University, Kaifeng, 475004, China

    Huibin Jia, Xiangci Wu, Xiaolin Zhang, Meiling Guo & Enguo Wang

  3. School of Special Education, Zhengzhou Normal University, Zhengzhou, 450000, China

    Chunying Yang

Authors
  1. Huibin Jia
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  2. Xiangci Wu
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  3. Xiaolin Zhang
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  4. Meiling Guo
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  5. Chunying Yang
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  6. Enguo Wang
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Contributions

Huibin Jia, Xiangci Wu and Xiaolin Zhang analyzed the datasets. Meiling Guo prepared the figures. All authors writed and reviewed the manuscript.

Corresponding authors

Correspondence to Chunying Yang or Enguo Wang.

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Competing Interests

The authors declare no competing interests.

Additional information

Communicated by Christoph Michel.

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Jia, H., Wu, X., Zhang, X. et al. Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals. Brain Topogr 37, 410–419 (2024). https://doi.org/10.1007/s10548-023-01010-6

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  • DOI: https://doi.org/10.1007/s10548-023-01010-6

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