Abstract
In recent studies, we have witnessed the applicability of deep learning methods on resting-state functional magnetic resonance image (rs-fMRI) analysis and its use for brain disease diagnosis, e.g., autism spectrum disorder (ASD). However, it still remains challenging to learn discriminative representations from raw BOLD signals or functional connectivity (FC) with a limited number of samples. In this paper, we propose a simple but efficient representation learning method for FC in a self-supervised learning manner. Specifically, we devise a proxy task of estimating the randomly masked seed-based functional networks from the remaining ones in FC, to discover the complex high-level relations among brain regions, which are not directly observable from an input FC. Thanks to the random masking strategy in our proxy task, it also has the effect of augmenting training samples, thus allowing for robust training. With the pretrained feature representation network in a self-supervised manner, we then construct a decision network for the downstream task of ASD diagnosis. In order to validate the effectiveness of our proposed method, we used the ABIDE dataset that collected subjects from multiple sites and our proposed method showed superiority to the comparative methods in various metrics.
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
Notes
- 1.
http://fcon_1000.projects.nitrc.org/indi/abide/.
- 2.
{UM, NYU, MAX MUN, OHSU, SBL, OLIN, SDSU, CALTECH, TRINITY, YALE, PITT, LEUVEN, UCLA, USM, STANFORD, CMU, and KKI}.
- 3.
- 4.
Note that the input FC \(\mathbf {X}\) represents first-order relation among ROIs, and the \(\ell \)-th hidden layer finds (\(\ell +1\))-th relations among ROIs.
References
Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736–745 (2017)
Bengio, Y.: Learning Deep Architectures for AI. Now Publishers Inc, Hanover (2009)
Biswal, B.B.: Resting state fMRI: a personal history. Neuroimage 62(2), 938–944 (2012)
Brier, M.R., et al.: Loss of intranetwork and internetwork resting state functional connections with alzheimer’s disease progression. J. Neurosci. 32(26), 8890–8899 (2012)
Chao-Gan, Y., Yu-Feng, Z.: DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4 (2010)
Chen, X., et al.: High-order resting-state functional connectivity network for MCI classification. Hum. Brain Map. 37(9), 3282–3296 (2016)
Cribben, I., Haraldsdottir, R., Atlas, L.Y., Wager, T.D., Lindquist, M.A.: Dynamic connectivity regression: determining state-related changes in brain connectivity. Neuroimage 61(4), 907–920 (2012)
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)
Dichter, G.S.: Functional magnetic resonance imaging of autism spectrum disorders. Dial. Clin. Neurosci. 14(3), 319 (2012)
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)
Hjelm, R.D., Calhoun, V.D., Salakhutdinov, R., Allen, E.A., Adali, T., Plis, S.M.: Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks. NeuroImage 96, 245–260 (2014)
Hu, X., et al.: Latent source mining in fMRI via restricted Boltzmann machine. Hum. Brain Map. 39(6), 2368–2380 (2018)
Jeon, E., Kang, E., Lee, J., Lee, J., Kam, T.-E., Suk, H.-I.: Enriched representation learning in resting-state fMRI for early MCI diagnosis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 397–406. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_39
Kang, E., Suk, H.-I.: Probabilistic source separation on resting-state fMRI and its use for early MCI identification. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 275–283. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_32
Kim, J., Calhoun, V.D., Shim, E., Lee, J.H.: Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124, 127–146 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lee, L., Harrison, L.M., Mechelli, A.: A report of the functional connectivity workshop, Dusseldorf 2002. Neuroimage 19(2), 457–465 (2003)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., Müller, K.R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recogn. 65, 211–222 (2017)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Rakić, M., Cabezas, M., Kushibar, K., Oliver, A., Lladó, X.: Improving the detection of autism spectrum disorder by combining structural and functional MRI information. NeuroImage Clin. 25, 102181 (2020)
Suk, H.I., Wee, C.Y., Lee, S.W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129, 292–307 (2016)
Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4(6), e1000100 (2008)
Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103 (2008)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A., Bottou, L.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12) (2010)
Zhao, F., Chen, Z., Rekik, I., Lee, S.W., Shen, D.: Diagnosis of autism spectrum disorder using central-moment features from low-and high-order dynamic resting-state functional connectivity networks. Front. Neurosci. 14 (2020)
Acknowledgement
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1006543) and by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jung, W., Heo, DW., Jeon, E., Lee, J., Suk, HI. (2021). Inter-regional High-Level Relation Learning from Functional Connectivity via Self-supervision. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12902. Springer, Cham. https://doi.org/10.1007/978-3-030-87196-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-87196-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87195-6
Online ISBN: 978-3-030-87196-3
eBook Packages: Computer ScienceComputer Science (R0)
