Abstract
The identification of autistic individuals using resting state functional connectivity networks can provide an objective diagnostic method for autism spectrum disorder (ASD). The present state-of-the-art machine learning model using deep learning has a classification accuracy of 70.2% on the ABIDE (Autism Brain Imaging Data Exchange) data set. In this paper, we explore the utility of topological features in the classification of ASD versus typically developing control subjects. These topological features have been shown to provide a complementary source of discriminative information in applications such as 2D object classification and social network analysis. We evaluate the performance of three different representations of topological features - persistence diagrams, persistence images, and persistence landscapes - for autism classification using neural networks, support vector machines and random forests. We also propose a hybrid approach of augmenting topological features with functional correlations, which typically outperforms the models that use functional correlations alone. With this approach, even with a simple 3-layer neural network, we are able to achieve a classification accuracy of 69.2% on the ABIDE data set. However, our experiments also show that the improvement due to topological features is not always statistically significant. Therefore, we offer a cautionary tale to the practitioners regarding the limited discriminative power of topological features derived from fMRI data for the classification of autism.
This work was supported in part by NSF IIS 1513616 and NIH R01EB022876.
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)
Adams, H., et al.: Persistence images: a stable vector representation of persistent homology. J. Mach. Learn. Res. 18(1), 218–252 (2017)
Bubenik, P.: Statistical topological data analysis using persistence landscapes. J. Mach. Learn. Res. 16(1), 77–102 (2015)
Cameron, C., et al.: The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinformatics 7 (2013)
Carriére, M.: sklearn-tda: a scikit-learn compatible python package for machine learning and TDA. https://github.com/MathieuCarriere/sklearn-tda
Carrière, M., Cuturi, M., Oudot, S.: Sliced Wasserstein kernel for persistence diagrams. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 664–673 (2017)
Martino, D., 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)
Edelsbrunner, H., Harer, J.: Persistent homology-a survey. Contemp. Math. 453, 257–282 (2008)
Guo, X., Dominick, K.C., Minai, A.A., Li, H., Erickson, C.A., Lu, L.J.: Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front. Neurosci. 11, 460 (2017)
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)
Hofer, C., Kwitt, R., Niethammer, M., Uhl, A.: Deep learning with topological signatures. In: Advances in Neural Information Processing Systems, pp. 1634–1644 (2017)
Kusano, G., Fukumizu, K., Hiraoka, Y.: Kernel method for persistence diagrams via kernel embedding and weight factor. J. Mach. Learn. Res. 18(1), 6947–6987 (2017)
Le, T., Yamada, M.: Persistence fisher kernel: a Riemannian manifold kernel for persistence diagrams. In: Advances in Neural Information Processing Systems, vol. 31, pp. 10028–10039 (2018)
Nielsen, J.A., et al.: Multisite functional connectivity MRI classification of autism: ABIDE results. Front. Hum. Neurosci. 7, 599 (2013)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Reininghaus, J., Huber, S., Bauer, U., Kwitt, R.: A stable multi-scale kernel for topological machine learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4741–4748 (2015)
Wong, E., Palande, S., Wang, B., Zielinski, B., Anderson, J., Fletcher, P.T.: Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior. In: IEEE International Symposium on Biomedical Imaging, pp. 1303–1306 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rathore, A., Palande, S., Anderson, J.S., Zielinski, B.A., Fletcher, P.T., Wang, B. (2019). Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_82
Download citation
DOI: https://doi.org/10.1007/978-3-030-32248-9_82
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32247-2
Online ISBN: 978-3-030-32248-9
eBook Packages: Computer ScienceComputer Science (R0)
