Abstract
There are strong indications that structural and functional magnetic resonance imaging (MRI) may help identify biologically relevant phenotypes of neurodevelopmental disorders such as Autism spectrum disorder (ASD). Extracting patterns from MRI data is challenging due to the high dimensionality, limited cardinality and data heterogeneity. In this paper, we explore structural and resting state functional MRI (rs-fMRI) for ASD classification using available ABIDE II dataset, using several standard machine learning (ML) models and convolutional neural networks (CNNs). To overcome the high dimensionality problem, we propose a simple data transformation method based on histograms calculation for the standard ML models and a simple 3D-to-2D and 4D-to-3D data transformation method for the CNNs in ASD classification. Numerous research has been done for ASD classification using the online available ABIDE I dataset, and several with the ABIDE II dataset, the latter mostly working with single-site classification studies. Here, we take the whole ABIDE II dataset using all structural and functional raw data. Our results show that the proposed methods achive state-of-the art results of high 71.4% accuracy (functional) and 73.4% AUC (structural) compared to the best performing results in literature of 68% accuracy (functional) for ASD classification on all ABIDE dataset.
Supported by Faculty of Computer Science and Engineering, Skopje, N. Macedonia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: \(\{\)TensorFlow\(\}\): a system for \(\{\)Large-Scale\(\}\) machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283 (2016)
Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. Neuroimage 147, 736–745 (2017)
Almuqhim, F., Saeed, F.: ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front. Comput. Neurosci. 15, 654315 (2021)
Artifact detection tools ART (2019). http://www.nitrc.org/projects/artifact_detect. Accessed 30 12 2019
Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight J 2(365), 1–35 (2009)
Batista, G., Silva, D.F., et al.: How k-nearest neighbor parameters affect its performance. In: Argentine Symposium on Artificial Intelligence, pp. 1–12. Citeseer (2009)
Bengs, M., Gessert, N., Schlaefer, A.: 4D Spatio-temporal deep learning with 4D fMRI data for autism spectrum disorder classification. arXiv preprint arXiv:2004.10165 (2020)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010. Physica-Verlag HD, pp. 177–186. Springer (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Chollet, F., et al.: Keras: The python deep learning library. Astrophy. Source Code Libr., pp. ascl-1806 (2018)
Di Martino, A., et al.: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. data 4(1), 1–15 (2017)
DI Martino, A., et al.: 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)
Dougherty, D.D., Rauch, S.L., Rosenbaum, J.F.: Essentials of Neuroimaging for Clinical Practice. American Psychiatric Pub, Washington (2008)
El Gazzar, A., Cerliani, L., van Wingen, G., Thomas, R.M.: Simple 1-D convolutional networks for resting-state fMRI based classification in autism. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2019)
El-Gazzar, A., Quaak, M., Cerliani, L., Bloem, P., van Wingen, G., Mani Thomas, R.: A hybrid 3DCNN and 3DC-LSTM based model for 4D spatio-temporal fMRI data: an ABIDE autism classification study. In: Zhou, L., et al. (eds.) OR 2.0/MLCN -2019. LNCS, vol. 11796, pp. 95–102. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32695-1_11
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1
Hoerl, A.E., Kannard, R.W., Baldwin, K.F.: Ridge regression: some simulations. Commun. Stat. Theory Methods 4(2), 105–123 (1975)
Hosmer, D.W., Jr., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)
Jiang, H., Cao, P., Xu, M., Yang, J., Zaiane, O.: Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput. Biol. Med. 127, 104096 (2020)
Khodatars, M., et al.: Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139, 104949 (2021)
Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: 3D convolutional neural networks for classification of functional connectomes. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 137–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_16
Kim, B.H., Ye, J.C.: Understanding graph isomorphism network for rs-fMRI functional connectivity analysis. Front. Neurosci., 630 (2020)
Kingma, D.P., Ba, J.: ADAM: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C.: The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47–56 (2016)
Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)
Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Qayyum, A., et al.: An efficient 1DCNN-LSTM deep learning model for assessment and classification of fMRI-based autism spectrum disorder. In: Raj, J.S., Kamel, K., Lafata, P. (eds.) Innovative Data Communication Technologies and Application, vol. 96, pp. 1039–1048. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7167-8_77
Riaz, A., Asad, M., Alonso, E., Slabaugh, G.: DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J. Neurosci. Methods 335, 108506 (2020)
Riedmiller, M.: Advanced supervised learning in multi-layer perceptrons-from backpropagation to adaptive learning algorithms. Comput. Stan. Interfaces 16(3), 265–278 (1994)
Rish, I., et al.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46 (2001)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)
Sarraf, S., Desouza, D.D., Anderson, J.A., Saverino, C.: MCADNNeT: recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access 7, 155584–155600 (2019)
Serai, S.D.: Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging. Pediatr. Radiol. 52(2), 217–227 (2021). https://doi.org/10.1007/s00247-021-05042-7
Smith, S.M.: Bet: brain extraction tool. FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK (2000)
Statistical parametric mapping SPM12 (2018). https://www.fil.ion.ucl.ac.uk/spm/software/spm12/. Accessed 30 12 2019
Sporns, O.: Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15, 247–262 (2022)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tahmassebi, A., Gandomi, A.H., McCann, I., Schulte, M.H., Goudriaan, A.E., Meyer-Baese, A.: Deep learning in medical imaging: fMRI big data analysis via convolutional neural networks. In: Proceedings of the Practice and Experience on Advanced Research Computing, pp. 1–4 (2018)
Thomas, R.M., Gallo, S., Cerliani, L., Zhutovsky, P., El-Gazzar, A., Van Wingen, G.: Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front. Psych. 11, 440 (2020)
Tournier, J.D., et al.: Mrtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019)
Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)
Wang, L.: Support Vector Machines: Theory and Applications, vol. 177. Springer Science & Business Media, Berlin (2005)
Wen, D., Wei, Z., Zhou, Y., Li, G., Zhang, X., Han, W.: Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: a brief overview and our opinion. Front. Neuroinform. 12, 23 (2018)
Wen, J., et al.: Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)
Whitfield-Gabrieli, S., Nieto-Castanon, A.: CONN: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–41 (2012). https://doi.org/10.1089/brain.2012.0073
Woolrich, M.W., et al.: Bayesian analysis of neuroimaging data in FSL. Neuroimage 45(1), S173–S186 (2009)
Yang, X., Zhang, N., Schrader, P.: A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Mach. Learn. Appl. 8, 100290 (2022)
Yin, W., Li, L., Wu, F.X.: Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332–345 (2022)
Acknowledgements
This work was partially financed by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University in Skopje.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Krajevski, J., Ivanoska, I., Trivodaliev, K., Kalajdziski, S., Gievska, S. (2022). An Exploration of Autism Spectrum Disorder Classification from Structural and Functional MRI Images. In: Zdravkova, K., Basnarkov, L. (eds) ICT Innovations 2022. Reshaping the Future Towards a New Normal. ICT Innovations 2022. Communications in Computer and Information Science, vol 1740. Springer, Cham. https://doi.org/10.1007/978-3-031-22792-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-22792-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22791-2
Online ISBN: 978-3-031-22792-9
eBook Packages: Computer ScienceComputer Science (R0)