Abstract
Graph Fourier Transform (GFT) could be a key tool for analyzing brain signals. In this sense, we evaluate the application of Graph signal processing (GSP) for the analysis of neuroimaging data. Thus, a GSP-based approach is proposed and validated for the classification of autism spectrum disorder (ASD). More specifically, the resting state functional magnetic resonance imaging (rs-fMRI) time series of each brain subject are characterized by several statistical metrics. Then, these measures are projected on a structural graph, which is computed from a healthy brain structural connectivity of the human connectome project. Further analysis proves that the combination of the structural connectivity with the standard deviation of fMRI temporal signals can lead to more accurate supervised classification for 172 subjects from the biggest site of the Autism Brain Imaging Data Exchange (ABIDE) datasets. Moreover, the proposed approach outperforms several approaches, based on using functional connectome or complex functional network measures.
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Notes
- 1.
See http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html for specific information.
References
Menoret, M., Farrugia, N., Pasdeloup, B., Gripon, V.: Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2017)
Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20(3), 353–364 (2017)
Nielsen, J.A., et al.: Multisite functional connectivity MRI classification of autism: abide results. Front. Hum. Neurosci. 7, 599 (2013)
Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736–745 (2017)
Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)
Chung, F.R.: Spectral graph theory, vol. 92 (1997)
Singh, A., Dutta, M.K., Jennane, R., Lespessailles, E.: Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput. Biol. Med. 91, 148–158 (2017)
Ktena, S.I., et al.: Distance metric learning using graph convolutional networks: application to functional brain networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 469–477. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_54
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)
Kazeminejad, A., Sotero, R.C.: Topological properties of resting-state fmri functional networks improve machine learning-based autism classification. Front. Neurosci. 12, 1018 (2019)
Craddock, C., et al.: Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (C-PAC). Front. Neuroinformatics 42 (2013)
Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)
Huang, W., Bolton, T.A.W., Medaglia, J.D., Bassett, D.S., Ribeiro, A., Van De Ville, D.: A graph signal processing perspective on functional brain imaging. Proc. IEEE 106(5), 868–885 (2018)
Huang, W., Goldsberry, L., Wymbs, N.F., Grafton, S.T., Bassett, D.S., Ribeiro, A.: Graph frequency analysis of brain signals. IEEE J. Sel. Top. Signal Process. 10(7), 1189–1203 (2016)
Varoquaux, G., Baronnet, F., Kleinschmidt, A., Fillard, P., Thirion, B.: Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 200–208. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15705-9_25
Sato, J.R., Calebe Vidal, M., de Siqueira Santos, S., Brauer Massirer, K., Fujita, A.: Complex network measures in autism spectrum disorders. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(2), 581–587 (2018)
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Brahim, A., Hajjam El Hassani, M., Farrugia, N. (2019). Classification of Autism Spectrum Disorder Through the Graph Fourier Transform of fMRI Temporal Signals Projected on Structural Connectome. In: Vento, M., et al. Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-030-29930-9_5
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