Abstract
Predicting the evolution trajectories of brain data from a baseline timepoint is a challenging task in the fields of neuroscience and neuro-disorders. While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Recently, a seminal brain network evolution prediction framework was introduced capitalizing on learning how to select the most similar training network samples at baseline to a given testing baseline network for the target prediction task. However, this rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks. Such sample connectomic representation might include irrelevant and redundant features that could mislead the training sample selection step. Undoubtedly, this fails to exploit and preserve the topology of the brain connectome. To overcome this major drawback, we propose Residual Embedding Similarity-Based Network selection (RESNets) for predicting brain network evolution trajectory from a single timepoint. RESNets first learns a compact geometric embedding of each training and testing sample using adversarial connectome embedding network. This nicely reduces the high-dimensionality of brain networks while preserving their topological properties via graph convolutional networks. Next, to compute the similarity between subjects, we introduce the concept of a connectional brain template (CBT), a fixed network reference, where we further represent each training and testing network as a deviation from the reference CBT in the embedding space. As such, we select the most similar training subjects to the testing subject at baseline by comparing their learned residual embeddings with respect to the pre-defined CBT. Once the best training samples are selected at baseline, we simply average their corresponding brain networks at follow-up timepoints to predict the evolution trajectory of the testing network. Our experiments on both healthy and disordered brain networks demonstrate the success of our proposed method in comparison to RESNets ablated versions and traditional approaches. Our RESNets code is available at http://github.com/basiralab/RESNets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Yang, Q., Thomopoulos, S.I., Ding, L., Surento, W., Thompson, P.M., Jahanshad, N.: Support vector based autoregressive mixed models of longitudinal brain changes and corresponding genetics in Alzheimer’s disease. In: Rekik, I., Adeli, E., Park, S.H. (eds.) PRIME 2019. LNCS, vol. 11843, pp. 160–167. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32281-6_17
Zhou, Y., Tagare, H.D.: Bayesian longitudinal modeling of early stage Parkinson’s disease using DaTscan images. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 405–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_31
Rekik, I., Li, G., Lin, W., Shen, D.: Estimation of brain network atlases using diffusive-shrinking graphs: application to developing brains. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 385–397. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_31
Gafuroğlu, C., Rekik, I., Alzheimer’s Disease Neuroimaging Initiative: Joint prediction and classification of brain image evolution trajectories from baseline brain image with application to early Dementia. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 437–445. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_50
Xia, T., Chartsias, A., Tsaftaris, S.A.: Consistent brain ageing synthesis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 750–758. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_82
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
Fornito, A., Zalesky, A., Breakspear, M.: The connectomics of brain disorders. Nature Rev. Neurosci. 16, 159–172 (2015)
Wang, J., et al.: Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation. IEEE Trans. Med. Imaging (2020)
Richiardi, J., Van De Ville, D., Riesen, K., Bunke, H.: Vector space embedding of undirected graphs with fixed-cardinality vertex sequences for classification. In: 20th International Conference on Pattern Recognition, pp. 902–905 (2010)
Bassett, D.S., Sporns, O.: Network neuroscience. Nature Neurosci. 20, 353 (2017)
Dhifallah, S., Rekik, I.: Estimation of connectional brain templates using selective multi-view network normalization. Med. Image Anal. 59, 101567 (2019)
Liu, M., Zhang, D., Shen, D., Initiative, A.D.N.: View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum. Brain Map. 36, 1847–1865 (2015)
Banka, A., Rekik, I.: Adversarial connectome embedding for mild cognitive impairment identification using cortical morphological networks. In: Schirmer, M.D., Venkataraman, A., Rekik, I., Kim, M., Chung, A.W. (eds.) CNI 2019. LNCS, vol. 11848, pp. 74–82. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32391-2_8
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34, 18–42 (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Wang, B., Mezlini, A., Demir, F., Fiume, M., et al.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014)
Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 1–14 (2018)
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/).
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
Göktaş, A.S., Bessadok, A., Rekik, I. (2020). Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-59354-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59353-7
Online ISBN: 978-3-030-59354-4
eBook Packages: Computer ScienceComputer Science (R0)