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Foreseeing Brain Graph Evolution over Time Using Deep Adversarial Network Normalizer

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Predictive Intelligence in Medicine (PRIME 2020)

Abstract

Foreseeing the brain evolution as a complex highly interconnected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature. Here we design an adversarial brain network normalizer for representing each brain network as a transformation of a fixed centered population-driven connectional template. Such graph normalization with respect to a fixed reference paves the way for reliably identifying the most similar training samples (i.e., brain graphs) to the testing sample at baseline timepoint. The testing evolution trajectory will be then spanned by the selected training graphs and their corresponding evolution trajectories. We base our prediction framework on geometric deep learning which naturally operates on graphs and nicely preserves their topological properties. Specifically, we propose the first graph-based Generative Adversarial Network (gGAN) that not only learns how to normalize brain graphs with respect to a fixed connectional brain template (CBT) (i.e., a brain template that selectively captures the most common features across a brain population) but also learns a high-order representation of the brain graphs also called embeddings. We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time. A series of benchmarks against several comparison methods showed that our proposed method achieved the lowest brain disease evolution prediction error using a single baseline timepoint. Our gGAN code is available at http://github.com/basiralab/gGAN.

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Notes

  1. 1.

    https://www.oasis-brains.org/.

References

  1. Querbes, O., et al.: Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132, 2036 (2009)

    Article  Google Scholar 

  2. Leifer, B.P.: Early diagnosis of Alzheimer’s disease: clinical and economic benefits. J. Am. Geriatr. Soc. 51, S281–S288 (2003)

    Article  Google Scholar 

  3. Grober, E., Bang, S.: Sentence comprehension in Alzheimer’s disease. Dev. Neuropsychol. 11, 95–107 (1995)

    Article  Google Scholar 

  4. Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)

  5. Gafuroğlu, C., Rekik, I., et al.: Joint prediction and classification of brain image evolution trajectories from baseline brain image with application to early dementia. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 437–445 (2018)

    Google Scholar 

  6. Rekik, I., Li, G., Yap, P., Chen, G., Lin, W., Shen, D.: Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. Neuroimage 152, 411–424 (2017)

    Article  Google Scholar 

  7. Ezzine, B.E., Rekik, I.: Learning-guided infinite network atlas selection for predicting longitudinal brain network evolution from a single observation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 796–805. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_88

    Chapter  Google Scholar 

  8. Allassonnière, S., Trouvé, A., Younes, L.: Geodesic shooting and diffeomorphic matching via textured meshes. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 365–381. Springer, Heidelberg (2005). https://doi.org/10.1007/11585978_24

    Chapter  Google Scholar 

  9. Trouvé, A.: An approach of pattern recognition through infinite dimensional group action (1995)

    Google Scholar 

  10. Goodfellow, I.J., et al.: Generative adversarial networks (2014)

    Google Scholar 

  11. Dhifallah, S., Rekik, I.: Estimation of connectional brain templates using selective multi-view network normalization. Med. Image Anal. 59, 101567 (2020)

    Article  Google Scholar 

  12. Yang, Q., et al.: MRI cross-modality image-to-image translation. Sci. Rep. 10, 1–18 (2020)

    Article  Google Scholar 

  13. Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. CoRR abs/1704.02901 (2017)

    Google Scholar 

  14. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015)

    Google Scholar 

  15. Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)

    Google Scholar 

  16. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

  17. Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 1249–1258 (2016)

    Google Scholar 

  18. Ding, C.H.: A similarity-based probability model for latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 58–65 (1999)

    Google Scholar 

  19. Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22, 2677–2684 (2010)

    Article  Google Scholar 

  20. Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018)

    Article  Google Scholar 

  21. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. CoRR abs/1903.02428 (2019)

    Google Scholar 

  22. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)

    Google Scholar 

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Acknowledgement

I. Rekik is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Individual Fellowship grant agreement No 101003403 (http://basira-lab.com/normnets/).

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Correspondence to Islem Rekik .

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Gürler, Z., Nebli, A., Rekik, I. (2020). Foreseeing Brain Graph Evolution over Time Using Deep Adversarial Network Normalizer. In: Rekik, I., Adeli, E., Park, S.H., Valdés Hernández, M.d.C. (eds) Predictive Intelligence in Medicine. PRIME 2020. Lecture Notes in Computer Science(), vol 12329. Springer, Cham. https://doi.org/10.1007/978-3-030-59354-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-59354-4_11

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