Abstract
The functional connectivity (FC) between brain regions is usually estimated through a statistical dependency method with functional magnetic resonance imaging (fMRI) data. It inevitably yields redundant and noise connections, limiting the performance of deep supervised models in brain disease diagnosis. Besides, the supervised signals of fMRI data are insufficient due to the shortage of labeled data. To address these issues, we propose an end-to-end unsupervised graph structure learning method for sufficiently capturing the structure or characteristics of the functional brain network itself without relying on manual labels. More specifically, the proposed method incorporates a graph generation module for automatically learning the discriminative graph structures of functional brain networks and a topology-aware encoding module for sufficiently capturing the structure information. Furthermore, we also design view consistency and correlation-guided contrastive regularizations. We evaluated our model on two real medical clinical applications: the diagnosis of Bipolar Disorder (BD) and Major Depressive Disorder (MDD). The results suggest that the proposed method outperforms state-of-the-art methods. In addition, our model is capable of identifying associated biomarkers and providing evidence of disease association. To the best of our knowledge, our work attempts to construct learnable functional brain networks with unsupervised graph structure learning. Our code is available at https://github.com/IntelliDAL/Graph/tree/main/BrainUSL.
P. Zhang and G. Wen—Contribute equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chavas, J., Guillon, L., Pascucci, M., Dufumier, B., Rivière, D., Mangin, J.F.: Unsupervised representation learning of cingulate cortical folding patterns. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part I. LNCS, vol. 13431, pp. 77–87. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_8
Eslami, T., Mirjalili, V., Fong, A., Laird, A.R., Saeed, F.: ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front. Neuroinform. 13, 70 (2019)
Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E.V., Adeli, E., Pohl, K.M.: Spatio-temporal graph convolution for resting-state fMRI analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 528–538. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_52
Imran, A.A.Z., Wang, S., Pal, D., Dutta, S., Zucker, E., Wang, A.: Multimodal contrastive learning for prospective personalized estimation of CT organ dose. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part I. LNCS, vol. 13431, pp. 634–643. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_60
Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038–1049 (2017)
Khosla, M., Jamison, K., Ngo, G.H., Kuceyeski, A., Sabuncu, M.R.: Machine learning in resting-state fMRI analysis. Magn. Reson. Imaging 64, 101–121 (2019)
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Sig. Process. 151, 107398 (2021)
Kumar, V., Garg, R.: Resting state functional connectivity alterations in individuals with autism spectrum disorders: a systematic review. medRxiv (2021)
Lawry Aguila, A., Chapman, J., Janahi, M., Altmann, A.: Conditional VAEs for confound removal and normative modelling of neurodegenerative diseases. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part I. LNCS, vol. 13431, pp. 430–440. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_41
Lee, W.H., Frangou, S.: Linking functional connectivity and dynamic properties of resting-state networks. Sci. Rep. 7(1), 16610 (2017)
Lynch, C.J., Uddin, L.Q., Supekar, K., Khouzam, A., Phillips, J., Menon, V.: Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits. Biol. Psychiatry 74(3), 212–219 (2013)
Nebel, M.B., et al.: Intrinsic visual-motor synchrony correlates with social deficits in autism. Biol. Psychiatry 79(8), 633–641 (2016)
Radonjić, N.V., et al.: Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders. Mol. Psychiatry 26(6), 2101–2110 (2021)
Sauty, B., Durrleman, S.: Progression models for imaging data with longitudinal variational auto encoders. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part I. LNCS, vol. 13431, pp. 3–13. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_1
Seyfioğlu, M.S., et al.: Brain-aware replacements for supervised contrastive learning in detection of Alzheimer’s disease. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part I. LNCS, vol. 13431, pp. 461–470. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_44
Wang, Y., Kang, J., Kemmer, P.B., Guo, Y.: An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Front. Neurosci. 10, 123 (2016)
Wang, Z., et al.: Distribution-guided network thresholding for functional connectivity analysis in fMRI-based brain disorder identification. IEEE J. Biomed. Health Inform. 26(4), 1602–1613 (2021)
Wen, G., Cao, P., Bao, H., Yang, W., Zheng, T., Zaiane, O.: MVS-GCN: a prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput. Biol. Med. 142, 105239 (2022)
Xia, M., Wang, J., He, Y.: BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7), e68910 (2013)
Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F.: DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics 14(3), 339–351 (2016). https://doi.org/10.1007/s12021-016-9299-4
Yan, K., Zhang, D.: Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens. Actuators, B Chem. 212, 353–363 (2015)
Yan, Y., Zhu, J., Duda, M., Solarz, E., Sripada, C., Koutra, D.: GroupINN: grouping-based interpretable neural network for classification of limited, noisy brain data. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 772–782 (2019)
Yin, W., Li, L., Wu, F.X.: Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332–345 (2022)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 5812–5823 (2020)
Zhang, Z., Ding, J., Xu, J., Tang, J., Guo, F.: Multi-scale time-series kernel-based learning method for brain disease diagnosis. IEEE J. Biomed. Health Inform. 25(1), 209–217 (2020)
Zhao, H., Nyholt, D.R.: Gene-based analyses reveal novel genetic overlap and allelic heterogeneity across five major psychiatric disorders. Hum. Genet. 136, 263–274 (2017). https://doi.org/10.1007/s00439-016-1755-6
Zhou, Z.H., Sun, Y.Y., Li, Y.F.: Multi-instance learning by treating instances as non-IID samples. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1249–1256 (2009)
Zhu, Y., Xu, W., Zhang, J., Liu, Q., Wu, S., Wang, L.: Deep graph structure learning for robust representations: a survey. arXiv preprint arXiv:2103.03036 (2021)
Acknowledgment
This paper is supported by the National Natural Science Foundation of China (No. 62076059), the Science Project of Liaoning province (2021-MS-105) and the 111 Project (B16009).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, P. et al. (2023). BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-43993-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43992-6
Online ISBN: 978-3-031-43993-3
eBook Packages: Computer ScienceComputer Science (R0)