Abstract
The resting-state functional magnetic resonance imaging (rs-fMRI) as a non-invasive technique with the high spatial and temporal resolution can help characterize the pathogenesis of autism spectrum disorder (ASD). Some results have been achieved with machine learning techniques to diagnose ASD with rs-fMRI data. However, most of machine learning methods have neglected the temporal dependency of the time-series fMRI data. In this study, we propose a method for diagnosing ASD based on brain dynamic networks (BDNs) which are constructed with time series rs-fMRI brain image data to describe the dynamic relationship among multiple brain regions. The least squares method with the forward model selection method was used to establish BDNs, and the Bayesian information criterion (BIC) was adopted as the model selection criteria to avoid overfitting. The resulted DBNs are weighted directed networks. Then a feature extraction method was proposed to extract representative and discriminated features from BDNs. Lastly, machine learning classifiers were trained with the whole ABIDE I cohort to diagnose ASD. The accuracy of 88.8% was achieved, which is higher than any previously reported methods.
Supported by Natural Science and Engineering Research Council of Canada (NSERC).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736–745 (2017)
Anzellotti, S., Kliemann, D., Jacoby, N., Saxe, R.: Directed network discovery with dynamic network modelling. Neuropsychologia 99, 1–11 (2017). https://doi.org/10.1016/j.neuropsychologia.2017.02.006. http://www.sciencedirect.com/science/article/pii/S0028393217300520
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)
Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.: Identifying autism from resting-state fMRI using long short-term memory networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 362–370. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_42
Eslami, T., Mirjalili, V., Fong, A.C.M., Laird, A., Saeed, F.: ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front. Neuroinform. (2019)
Friston, K., Moran, R., Seth, A.K.: Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23(2), 172–178 (2013). https://doi.org/10.1016/j.conb.2012.11.010. http://www.sciencedirect.com/science/article/pii/S0959438812001845
Greicius, M.: Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 21(4), 424–430 (2008)
Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp. 332–339, March 2009. https://doi.org/10.1109/CIDM.2009.4938668
Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003). https://doi.org/10.1109/TKDE.2003.1245283
Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A., Meneguzzi, F.: Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clin. 17, 16–23 (2018)
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
Liu, Y., et al.: Regional homogeneity, functional connectivity and imaging markers of Alzheimer’s disease: a review of resting-state fMRI studies. Neuropsychologia 46(6), 1648–1656 (2008). https://doi.org/10.1016/j.neuropsychologia.2008.01.027
Mostafa, S., Tang, L., Wu, F.: Diagnosis of autism spectrum disorder based on eigenvalues of brain networks. IEEE Access 7, 128474–128486 (2019)
Murphy, K., Birn, R.M., Bandettini, P.A.: Resting-state fMRI confounds and cleanup. NeuroImage 80, 349–359 (2013)
Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_21
Power, J.D., et al.: Functional network organization of the human brain. Neuron 72(4), 665–678 (2011)
Price, T., Wee, C.-Y., Gao, W., Shen, D.: Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 177–184. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10443-0_23
Quesnel-Vallières, M., Weatheritt, R.J., Cordes, S.P., Blencowe, B.J.: Autism spectrum disorder: insights into convergent mechanisms from transcriptomics. Nat. Rev. Genet. 20(1), 51–63 (2019)
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6, 461 (1978). https://doi.org/10.1214/aos/1176344136
Tang, L., Mostafa, S., Liao, B., Wu, F.X.: A network clustering based feature selection strategy for classifying autism spectrum disorder. BMC Med. Genomics 12(Suppl 7), 153–153 (2019)
Van Essen, D.C., Ugurbil, K.: The future of the human connectome. NeuroImage 62(2), 1299–1310 (2012)
Wong, E., Anderson, J.S., Zielinski, B.A., Fletcher, P.T.: Riemannian regression and classification models of brain networks applied to autism. In: Wu, G., Rekik, I., Schirmer, M.D., Chung, A.W., Munsell, B. (eds.) CNI 2018. LNCS, vol. 11083, pp. 78–87. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00755-3_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, H., Yin, W., Mostafa, S., Wu, FX. (2020). Diagnosis of ASD from rs-fMRI Images Based on Brain Dynamic Networks. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-57821-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57820-6
Online ISBN: 978-3-030-57821-3
eBook Packages: Computer ScienceComputer Science (R0)