ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

Abstract

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other existing methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 7 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

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References 52

1. 1.

   Abraham A. Milham M. P. Di Martino A. Craddock R. C. Samaras D. Thirion B.. (2017 ). Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. Neuroimage 147, 736–745.

   Not in our library10.1016/j.neuroimage.2016.10.045

2. 2.

   Baggio H.-C. Sala-Llonch R. Segura B. Marti M.-J. Valldeoriola F. Compta Y.. (2014 ). Functional brain networks and cognitive deficits in parkinson's disease. Human Brain Mapp. 35, 4620–4634.

   Not in our library10.1002/hbm.22499

3. 3.
   Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014 · 2018

   No notice
4. 4.

   Bi X.-A. Liu Y. Jiang Q. Shu Q. Sun Q. Dai J. (2018a ). The diagnosis of autism spectrum disorder based on the random neural network cluster. Front. Human Neurosci. 12:257.

   Not in our library10.3389/fnhum.2018.00257

5. 5.

   Bi X.-A. Wang Y. Shu Q. Sun Q. Xu Q. (2018b ). Classification of autism spectrum disorder using random support vector machine cluster. Front. Genet. 9:18.

   Not in our library10.3389/fgene.2018.00018

6. 6.

   Brown C. J. Kawahara J. Hamarneh G. (2018 ). Connectome priors in deep neural networks to predict autism, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (Washington, DC: IEEE), 110–113.

   Not in our library10.1109/isbi.2018.8363534

7. 7.

   Chawla N. V. Bowyer K. W. Hall L. O. Kegelmeyer W. P. (2002 ). SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Research 16, 321–357.

   Not in our library10.1613/jair.953

8. 8.

   Chen H. Duan X. Liu F. Lu F. Ma X. Zhang Y.. (2016 ). Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—a multi-center study. Prog. Neuro Psychopharmacol. Biol. Psychiatry 64, 1–9.

   Not in our library10.1016/j.pnpbp.2015.06.014

9. 9.

   Colby J. B. Rudie J. D. Brown J. A. Douglas P. K. Cohen M. S. Shehzad Z. (2012 ). Insights into multimodal imaging classification of ADHD. Front. Syst. Neurosci. 6:59.

   Not in our library10.3389/fnsys.2012.00059

10. 10.

    Craddock C. Benhajali Y. Chu C. Chouinard F. Evans A. Jakab A. (2013 ). The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Neuroinformatics.

    Not in our library10.3389/conf.fninf.2013.09.00041

11. 11.

    Craddock R. C. James G. A. Holtzheimer P. E. III. Hu X. P. Mayberg H. S. (2012 ). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapp. 33, 1914–1928.

    Not in our library10.1002/hbm.21333

12. 12.

    Deshpande G. Wang P. Rangaprakash D. Wilamowski B. (2015 ). Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans. Cybernet. 45, 2668–2679.

    Not in our library10.1109/tcyb.2014.2379621

13. 13.

    Dvornek N. C. Ventola P. Pelphrey K. A. Duncan J. S. (2017 ). Identifying autism from resting-state fMRI using long short-term memory networks, in International Workshop on Machine Learning in Medical Imaging (Quebec, QC: Springer), 362–370.

    Not in our library10.1007/978-3-319-67389-9_42

14. 14.

    Eitel A. Springenberg J. T. Spinello L. Riedmiller M. Burgard W. (2015 ). Multimodal deep learning for robust RGB-D object recognition, in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Hamburg: IEEE), 681–687.

    Not in our library10.1109/iros.2015.7353446

15. 15.
    ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data · 2019

    No notice
16. 16.

    Eslami T. Saeed F. (2018a ). Fast-GPU-PCC: a GPU-based technique to compute pairwise pearson's correlation coefficients for time series data–fMRI study. High Throughput 7:11.

    Not in our library10.3390/ht7020011

17. 17.

    Eslami T. Saeed F. (2018b ). Similarity based classification of ADHD using singular value decomposition, in Proceedings of the ACM International Conference on Computing Frontiers 2018 (ACM), 19–25.

    Not in our library

18. 18.

    Eslami T. Saeed F. (2019 ). Auto-ASD-network: a technique based on deep learning and support vector machines for diagnosing autism spectrum disorder using fMRI data, in Proceedings of ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (Niagara Falls, NY: ACM).

    Not in our library

19. 19.

    Fredo A. J. Jahedi A. Reiter M. Müller R.-A. (2018 ). Diagnostic classification of autism using resting-state fMRI data and conditional random forest. Age 12, 6–41.

    Not in our library

20. 20.

    Greene D. J. Church J. A. Dosenbach N. U. Nielsen A. N. Adeyemo B. Nardos B.. (2016 ). Multivariate pattern classification of pediatric tourette syndrome using functional connectivity MRI. Dev. Sci. 19, 581–598.

    Not in our library10.1111/desc.12407

21. 21.
    Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method · 2017

    Not checked
22. 22.

    Heinsfeld A. S. Franco A. R. Craddock R. C. Buchweitz A. Meneguzzi F. (2018 ). Identification of autism spectrum disorder using deep learning and the abide dataset. Neuroimage Clin. 17, 16–23.

    Not in our library10.1016/j.nicl.2017.08.017

23. 23.

    Hosseini-Asl E. Gimel'farb G. El-Baz A. (2016 ). Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv [preprint]. arXiv:1607.00556.

    Not in our library10.2741/4606

24. 24.

    Iidaka T. (2015 ). Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63, 55–67.

    Not in our library10.1016/j.cortex.2014.08.011

25. 25.

    Itani S. Thanou D. (2019 ). Combining anatomical and functional networks for neuropathology identification: a case study on autism spectrum disorder. arXiv [preprint]. arXiv:1904.11296.

    Not in our library10.1016/j.media.2021.101986

26. 26.

    Jaiswal S. Valstar M. F. Gillott A. Daley D. (2017 ). Automatic detection of ADHD and ASD from expressive behaviour in RGBD data, in 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (Washington, DC: IEEE), 762–769.

    Not in our library

27. 27.

    Karpathy A. Toderici G. Shetty S. Leung T. Sukthankar R. Fei-Fei L. (2014 ). Large-scale video classification with convolutional neural networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Columbus, OH), 1725–1732.

    Not in our library

28. 28.

    Khazaee A. Ebrahimzadeh A. Babajani-Feremi A. (2016 ). Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and alzheimer's disease. Brain Imaging Behav. 10, 799–817.

    Not in our library10.1007/s11682-015-9448-7

29. 29.

    Khazaee A. Ebrahimzadeh A. Babajani-Feremi A. Initiative A. D. N.. (2017 ). Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI. Behav. Brain Res. 322, 339–350.

    Not in our library10.1016/j.bbr.2016.06.043

30. 30.

    Khosla M. Jamison K. Kuceyeski A. Sabuncu M. (2018 ). 3D convolutional neural networks for classification of functional connectomes, in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Granada: Springer), 137–145.

    Not in our library10.1007/978-3-030-00889-5_16

31. 31.

    Li H. Parikh N. A. He L. (2018 ). A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front. Neurosci. 12:491.

    Not in our library10.3389/fnins.2018.00491

32. 32.
    Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study · 2012

    No notice
33. 33.

    Lindquist M. A. (2008 ). The statistical analysis of fMRI data. Stat. Sci. 23, 439–464.

    Not in our library10.1214/09-sts282

34. 34.

    Liu W. Li M. Yi L. (2016 ). Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Res. 9, 888–898.

    Not in our library10.1002/aur.1615

35. 35.

    National Collaborating Centre for Mental Health (UK) (2018 ). Attention Deficit Hyperactivity Disorder: Diagnosis and Management of ADHD in Children, Young People and Adults. Leicester: British Psychological Society.

    Not in our library

36. 36.

    Nickel R. E. Huang-Storms L. (2017 ). Early identification of young children with autism spectrum disorder. Indian J. Pediatr. 84, 53–60.

    Not in our library10.1007/s12098-015-1894-0

37. 37.
    Multisite functional connectivity MRI classification of autism: ABIDE results · 2013

    No notice
38. 38.

    Parikh M. N. Li H. He L. (2019 ). Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data. Front. Comput. Neurosci. 13:9.

    Not in our library10.3389/fncom.2019.00009

39. 39.

    Parisot S. Ktena S. I. Ferrante E. Lee M. Guerrero R. Glocker B. Rueckert D. (2018 ). Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease. Med. Image Anal. 48, 117–130.

    Not in our library10.1016/j.media.2018.06.001

40. 40.

    Peng X. Lin P. Zhang T. Wang J. (2013 ). Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS ONE 8:e79476.

    Not in our library10.1371/journal.pone.0079476

41. 41.

    Perez L. Wang J. (2017 ). The effectiveness of data augmentation in image classification using deep learning. arXiv [preprint]. arXiv:1712.04621.

    Not in our library

42. 42.

    Plitt M. Barnes K. A. Martin A. (2015 ). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. Neuroimage Clin. 7, 359–366.

    Not in our library10.1016/j.nicl.2014.12.013

43. 43.

    Power J. D. Cohen A. L. Nelson S. M. Wig G. S. Barnes K. A. Church J. A.. (2011 ). Functional network organization of the human brain. Neuron 72, 665–678.

    Not in our library10.1016/j.neuron.2011.09.006

44. 44.

    Raschka S. Mirjalili V. (2017 ). Python Machine Learning. Birmingham: Packt Publishing Ltd.

    Not in our library

45. 45.
    A general prediction model for the detection of ADHD and Autism using structural and functional MRI. · 2018

    Not checked
46. 46.

    Subbaraju V. Suresh M. B. Sundaram S. Narasimhan S. (2017 ). Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging: a spatial filtering approach. Med. Image Anal. 35, 375–389.

    Not in our library10.1016/j.media.2016.08.003

47. 47.

    Wong S. C. Gatt A. Stamatescu V. McDonnell M. D. (2016 ). Understanding data augmentation for classification: when to warp? in 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (Gold Coast, QLD: IEEE), 1–6.

    Not in our library10.1109/dicta.2016.7797091

48. 48.

    Xu Y. Jia R. Mou L. Li G. Chen Y. Lu Y. (2016 ). Improved relation classification by deep recurrent neural networks with data augmentation, in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (Osaka: The COLING 2016 Organizing Committee), 1461–1470.

    Not in our library

49. 49.

    Yang K. Shahabi C. (2004 ). A PCA-based similarity measure for multivariate time series, in Proceedings of the 2nd ACM International Workshop on Multimedia Databases (Washington, DC: ACM), 65–74.

    Not in our library

50. 50.

    Yang Z. Zhong S. Carass A. Ying S. H. Prince J. L. (2014 ). Deep learning for cerebellar ataxia classification and functional score regression, in International Workshop on Machine Learning in Medical Imaging (Springer), 68–76.

    Not in our library10.1007/978-3-319-10581-9_9

51. 51.
    Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis · 2017

    Not checked
52. 52.

    Zunino A. Morerio P. Cavallo A. Ansuini C. Podda J. Battaglia F. (2018 ). Video gesture analysis for autism spectrum disorder detection, in 2018 24th International Conference on Pattern Recognition (ICPR) (Beijing: IEEE), 3421–3426.

    Not in our library

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