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Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data

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Abstract

Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3–6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.

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Acknowledgments

This research was supported by the National Research Foundation (NRF) funded by the Korean Government (MSIT) (2019M3E5D1A01069345 to J-IK and 2020M3E5D9080787 to B-NK), by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) and Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (No.HU20C0198 to J-ML), and the Technology Innovation Program (Industrial Strategic Technology Development Program) funded by the Ministry of Trade, Industry, and Energy (20002769 to B-NK, Development of Next Generation Platform for Diagnosis and Therapeutic of Attention Deficit Hyperactivity Disorder and Intellectual Disability based on Big Data).

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Author notes

  1. Johanna Inhyang Kim and Sungkyu Bang have contributed equally and Shared first authorship.

Authors and Affiliations

  1. Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea

    Johanna Inhyang Kim, Sungwon Roh & Seok Hyeon Kim

  2. Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea

    Sungkyu Bang, Jin-Ju Yang & Jong-Min Lee

  3. Department of Psychology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 02722, Republic of Korea

    Heejin Kwon

  4. Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Soomin Jang & Bung-Nyun Kim

  5. Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

    Sungwon Roh & Seok Hyeon Kim

  6. Department of Rehabilitation Medicine, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea

    Mi Jung Kim

  7. Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea

    Hyun Ju Lee

  8. Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea

    Bung-Nyun Kim

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  1. Johanna Inhyang Kim
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  2. Sungkyu Bang
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  8. Mi Jung Kim
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  9. Hyun Ju Lee
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  10. Jong-Min Lee
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  11. Bung-Nyun Kim
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Contributions

All authors contributed to the study conception, design, material preparation and data collection. Analysis were performed by Sungkyu Bang and Jin-Ju Yang. The first draft of the manuscript was written by Johanna Inhyang Kim, Sungkyu Bang and Jin-Ju Yang. All authors read, commented on and approved the final manuscript. Jong-Min Lee and Bung-Nyun Kim supervised the whole process.

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Correspondence to Jong-Min Lee or Bung-Nyun Kim.

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Kim, J.I., Bang, S., Yang, JJ. et al. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. J Autism Dev Disord 53, 25–37 (2023). https://doi.org/10.1007/s10803-021-05368-z

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