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Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

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Abstract

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

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Acknowledgements

The authors express gratitude to the Autism Brain Imaging Data Exchange (ABIDE), for generously sharing their data with the scientific community.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Maryam Akhavan Aghdam & Arash Sharifi

  2. Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran

    Mir Mohsen Pedram

Authors
  1. Maryam Akhavan Aghdam
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  2. Arash Sharifi
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Corresponding author

Correspondence to Maryam Akhavan Aghdam.

Appendix. Names of ROIs in the AAL template

Appendix. Names of ROIs in the AAL template

Table 5 Indices and names of ROIs in the AAL template
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Akhavan Aghdam, M., Sharifi, A. & Pedram, M.M. Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network. J Digit Imaging 31, 895–903 (2018). https://doi.org/10.1007/s10278-018-0093-8

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  • DOI: https://doi.org/10.1007/s10278-018-0093-8

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