Abstract
A connectional brain template (CBT) is a normalized graph-based representation of a population of brain networks —also regarded as an ‘average’ connectome. CBTs are powerful tools for creating representative maps of brain connectivity in typical and atypical populations. Particularly, estimating a well-centered and representative CBT for populations of multi-view brain networks (MVBN) is more challenging since these networks sit on complex manifolds and there is no easy way to fuse different heterogeneous network views. This problem remains unexplored with the exception of a few recent works rooted in the assumption that the relationship between connectomes are mostly linear. However, such an assumption fails to capture complex patterns and non-linear variation across individuals. Besides, existing methods are simply composed of sequential MVBN processing blocks without any feedback mechanism, leading to error accumulation. To address these issues, we propose Deep Graph Normalizer (DGN), the first geometric deep learning (GDL) architecture for normalizing a population of MVBNs by integrating them into a single connectional brain template. Our end-to-end DGN learns how to fuse multi-view brain networks while capturing non-linear patterns across subjects and preserving brain graph topological properties by capitalizing on graph convolutional neural networks. We also introduce a randomized weighted loss function which also acts as a regularizer to minimize the distance between the population of MVBNs and the estimated CBT, thereby enforcing its centeredness. We demonstrate that DGN significantly outperforms existing state-of-the-art methods on estimating CBTs on both small-scale and large-scale connectomic datasets in terms of both representativeness and discriminability (i.e., identifying distinctive connectivities fingerprinting each brain network population). Our DGN code is available at https://github.com/basiralab/DGN.
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
Essen, D., et al.: The human connectome project: a data acquisition perspective. NeuroImage 62, 2222–2231 (2012)
Van Essen, D.C., Glasser, M.F.: The human connectome project: progress and prospects. In: Cerebrum: the Dana Forum on Brain Science, vol. 2016. Dana Foundation (2016)
Dhifallah, S., Rekik, I., Initiative, A.D.N., et al.: Clustering-based multi-view network fusion for estimating brain network atlases of healthy and disordered populations. J. Neurosci. Methods 311, 426–435 (2019)
Wang, B., et al.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333 (2014)
Dhifallah, S., Rekik, I.: Estimation of connectional brain templates using selective multi-view network normalization. Med. Image Anal. 59, 101567 (2019)
Rhee, S., Seo, S., Kim, S.: Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification, pp. 3527–3534 (2018)
Fout, A., Byrd, J., Shariat, B., Ben-Hur, A.: Protein interface prediction using graph convolutional networks. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 6530–6539. Curran Associates, Inc. (2017)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.: A comprehensive survey on graph neural networks (2019)
Zhou, J., Cui, G., Zhang, Z., Liu, Z., Sun, M.: Graph neural networks: a review of methods and applications (2018)
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs, pp. 29–38 (2017)
Mueller, S., et al.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin. North Am. 15, 869–77, xi (2005)
Raeper, R., Lisowska, A., Rekik, I.: Cooperative correlational and discriminative ensemble classifier learning for early dementia diagnosis using morphological brain multiplexes. IEEE Access 6, 43830–43839 (2018)
Lisowska, A., Rekik, I., for the ADNI: Joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis. Brain Connect. 9, 22–36 (2019)
Nebli, A., Rekik, I.: Gender differences in cortical morphological networks. Brain Imaging Behav., 1–9 (2019). https://doi.org/10.1007/s11682-019-00123-6
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, 659–667 (2014)
Fischl, B.: Freesurfer. NeuroImage 62, 774–781 (2012)
Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cerebral Cortex 14, 11–22 (2004). (New York, N.Y.: 1991)
Yamada, T., et al.: Altered functional organization within the insular cortex in adult males with high - functioning autism spectrum disorder: evidence from connectivity-based parcellation. Mol. Autism 7, 1–15 (2016)
Pelphrey, K., Carter, E.: Brain mechanisms for social perception lessons from autism and typical development. Ann. New York Acad. Sci. 1145, 283–299 (2009)
Hammond, P., et al.: Face-brain asymmetry in autism spectrum disorders. Mol. Psychiatry 13, 614–623 (2008)
Arnold, S.E., Hyman, B.T., Van Hoesen, G.W.: Neuropathologic changes of the temporal pole in Alzheimer’s disease and pick’s disease. Arch. Neurol. 51, 145–150 (1994)
Zhou, M., Zhang, F., Zhao, L., Qian, J., Dong, C.: Entorhinal cortex: a good biomarker of mild cognitive impairment and mild Alzheimer’s disease. Rev. Neurosci. 27(2), 185–195 (2016)
Acknowledgments
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/).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gurbuz, M.B., Rekik, I. (2020). Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-59728-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59727-6
Online ISBN: 978-3-030-59728-3
eBook Packages: Computer ScienceComputer Science (R0)
