Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

  • Maryam Akhavan Aghdam
  • Arash SharifiEmail author
  • Mir Mohsen Pedram


Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of “combining classifiers,” both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and “transfer learning” were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.


Autism spectrum disorder Convolutional neural network Transfer learning Mixture of experts Simple ‌Bayes 



We thank the Autism Brain Imaging Data Exchange (ABIDE) for generously sharing their data with scientific community.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Crosson B, Ford A, McGregor KM, Meinzer M, Cheshkov S, Li X, Walker-Baston D, Briggs RW: Functional imaging and related techniques: An introduction for rehabilitation researchers. J Rehabil Res Dev 47(2):vii–xxxiv, 2010CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Sarraf S, Sun J: Functional Brain Imaging: A Comprehensive Survey. arXiv preprint arXiv:1602.02225, 2005.Google Scholar
  3. 3.
    Fox MD, Snyder AZ, Vincent JL, Corbetta M, Essen DCV, Raichle ME: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102(27):9673–9678, 2005. CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Buckner RL, Andrews-Hanna JR, Schacter DL: The brain’s default network. Ann NY Acad Sci 1124:1–38, 2008. CrossRefGoogle Scholar
  5. 5.
    Wang J, Zuo X, He Y: Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci, 2010.
  6. 6.
    Suk HI, Wee CY, Lee SW, Shen D: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292–307, 2016. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Levy SE, Mandell DS, Schultz RT: Autism. Lancet 374(9701):1627–1638, 2009. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Coleman M, Gillberg C: The Autisms. Oxford: Oxford University Press, 2012Google Scholar
  9. 9.
    Waterhouse L: Rethinking Autism: Variation and Complexity. London: Academic Press, 2013Google Scholar
  10. 10.
    Fernell E, Eriksson MA, Gillberg C: Early diagnosis of autism and impact on prognosis: a narrative review. Clin Epidemiol 5:33–43, 2013CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pennington ML, Cullinan D, Southern LB: Defining Autism: Variability in state education agency definitions of and evaluations for autism spectrum disorders. Autism Res Treat, 2014.
  12. 12.
    Yerys BE, Pennington BF: How do we establish a biological marker for a behaviorally defined disorder? Autism as a test case. Autism Res 4(4):239–241, 2011CrossRefPubMedGoogle Scholar
  13. 13.
    Plitt M, Barnes KA, Martin A: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. Neuroimage Clin 7:359–366, 2014. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, Deen B, Delmonte S, Dinstein I, Ertl-Wagner B, Fair DA, Gallagher L, Kennedy DP, Keown CL, Keysers C, Lainhart JE, Lord C, Luna B, Menon V, Minshew NJ, Monk CS, Mueller S, Müller RA, Nebel MB, Nigg JT, O’Hearn K, Pelphrey KA, Peltier SJ, Rudie JD, Sunaert S, Thioux M, Tyszka JM, Uddin LQ, Verhoeven JS, Wenderoth N, Wiggins JL, Mostofsky SH, Milham MP: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19(6):659–667, 2014. CrossRefPubMedGoogle Scholar
  15. 15.
    Nielsen JA, Zielinski BA, Fletcher PT, Alexander AL, Lange N, Bigler ED, Lainhart JE, Anderson JS: Multisite functional connectivity MRI classification of autism: ABIDE results. Front. Hum. Neurosci 7(599), 2013.
  16. 16.
    Anderson JS, Nielsen JA, Froehlich AL, DuBray MB, Druzgal TJ, Cariello AN, Cooperrider JR, Zielinski BA, Ravichandran C, Fletcher PT, Alexander AL, Bigler ED, Lange N, Lainhart JE: Functional connectivity magnetic resonance imaging classification of autism. Brain 134(12):3742–3754, 2011. CrossRefPubMedGoogle Scholar
  17. 17.
    Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, Ryali S, Menon V: Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70(8):869–879, 2013. CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bell AJ, Sejnowski TJ: An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159, 1995CrossRefPubMedGoogle Scholar
  19. 19.
    McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ: Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6(3):160–188, 1998CrossRefPubMedGoogle Scholar
  20. 20.
    Ghiassian S, Greiner R, Jin P, Brown MRG: Using functional or structural magnetic resonance images and personal characteristic data to diagnose ADHD and autism. PLos ONE 11(12):e0166934, 2016CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sen B: Generalized Prediction Model for Detection of Psychiatric Disorders. Master Thesis, University of Alberta, 2016.Google Scholar
  22. 22.
    Plis SM, Hjelm D, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen J, Turner JA, Calhoun VD: Deep learning for neuroimaging: a validation study. Front Neurosci 8, 2014.
  23. 23.
    Olshausen BA: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609, 1996. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Suk HI, Lee SW, Shen D: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582, 2014. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Suk HI, Lee SW, Shen D: Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220(2):841–859, 2015. CrossRefPubMedGoogle Scholar
  26. 26.
    Sarraf S, Tofighi G: Classification of Alzheimer’s Disease Using fMRI Data and Deep Learning Convolutional Neural Networks. arXiv preprint arXiv:1603.08631, 2016.Google Scholar
  27. 27.
  28. 28.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L: Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252, 2015CrossRefGoogle Scholar
  29. 29.
    Available at: Scholar
  30. 30.
    Jenkinson M, Smith SM: Pre-Processing of BOLD FMRI Data. Oxford University Centre for Functional MRI of the Brain (FMRIB), 2006.Google Scholar
  31. 31.
  32. 32.
    Bowman FD, Guo Y, Derado G: Statistical approaches to functional neuroimaging data. Neuroimaging Clin N Am 17(4):441–458, 2007. CrossRefPubMedGoogle Scholar
  33. 33.
    Hermans E: SPM8 Starters Guide, 2011.
  34. 34.
    Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS: Deep learning for visual understanding: a review. Neurocomputing 187:27–48, 2016. CrossRefGoogle Scholar
  35. 35.
    Jacobs RA, Jordan MI, Steven JN, Georey EH: Adaptive mixtures of local experts. Neural Computation 3(1):79–87, 1991. CrossRefGoogle Scholar
  36. 36.
    Nair V, Hinton GE: Rectified linear units improve restricted Boltzmann machines. In Proceedings of the International Conference on Machine Learning (ICML), 2010.Google Scholar
  37. 37.
    Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR: Improving Neural Networks by Preventing Co-adaptation of Feature Detectors. arXiv preprint arXiv: 1207.0580, 2012.Google Scholar
  38. 38.
    Ciresan DC, Meier U, Schmidhuber J: Transfer learning for Latin and Chinese characters with deep neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 2012.Google Scholar
  39. 39.
    Ren JSJ, Xu L: On vectorization of deep convolutional neural networks for vision tasks. In: Proceedings of the Association for the Advancement of artificial intelligence (AAAI), the 29th international conference on artificial intelligence, 2015.Google Scholar
  40. 40.
    Ackey S, Kundegorski ME, Devereux M, Breckon TP: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp 1057–1061, 2016.
  41. 41.
    Singh D, Garzon P: Using Convolutional Neural Networks and Transfer Learning to Perform Yelp Restaurant Photo Classification, 2016.Google Scholar
  42. 42.
    Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C: A Survey on Deep Transfer Learning, arXiv preprint arXiv: 1808.01974, 2018.Google Scholar
  43. 43.
    Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298, 2016. CrossRefPubMedGoogle Scholar
  44. 44.
    Chollet F: Keras: Deep Learning Library for Theano and TensorFlow. 2015.
  45. 45.
    Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow IJ, Bergeron A, Bouchard N, Warde-Farley D, Bengio Y: Theano: New features and speed improvements. In: Proceedings of the workshop on deep learning and unsupervised feature learning Neural Information Processing Systems (NIPS), 2012.Google Scholar
  46. 46.
    Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688.Google Scholar
  47. 47.
    Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X: Tensorflow: A system for large-scale machine learning. arXiv preprint arXiv:1605.08695, 2016.Google Scholar
  48. 48.
    Kingma DP, Ba JL: Adam: A method for stochastic optimization. In: Proceedings of the international conference on learning representations (ICLR), 2015.Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Electrical and Computer EngineeringKharazmi UniversityTehranIran

Personalised recommendations