4.6 Article

Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning

Journal

IEEE ACCESS
Volume 8, Issue -, Pages 153341-153352

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3016734

Keywords

Magnetic resonance imaging; Three-dimensional displays; Autism; Brain modeling; Diseases; Machine learning; Biological system modeling; Deep learning; sMRI; austism spectrum disorders; neural networks

Funding

  1. Scientific and Technological Research of Education Department of Hubei Province [Q20181408]
  2. Scientific Research Foundation of Science and Technology Department of Hubei Province [2018CFB276]
  3. Doctor Launching Fund of the Hubei University of Technology [BSQD20160004]
  4. Hubei Chenguang Talented Youth Development Foundation (HBCG) [2017109]
  5. Institute for Information & Communications Technology Planning & Evaluation (IITP) - Korea government [Ministry of Science and Information and Communication Technology (MSIT)] [2019-0-01371]
  6. National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2019M3E5D2A01066267, NRF-2016R1A2B4006737]
  7. Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Korea government [Ministry Of Health and Welfare (MOHW)] [HI12C0021-A120029]

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Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insufficient amount of neurobiological evidence. We investigated the structural and strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize combinations of brain regions, thus representing the most referred-to regions used by the model whilst classifying the images. We also implemented recurrent neural networks to classify the sequence of brain regions efficiently. We found emphatic structural and strategic evidence on which the model heavily relies during the classification process. For instance, we observed that the structural and strategic evidence tends to be associated with subcortical structures, including the basal ganglia (BG). Our work identifies the distinct brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning that clinicians can use to ensure an economical and time-efficient diagnosis process.

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