Abstract
Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant efforts made for graph neural network (GNN) techniques, their focus on graph nodes makes the state-of-the-art GNN methods not suitable for classifying brain connectivity as graphs where the objective is to characterize disease-relevant network dysfunction patterns on graph links. To address this issue, we propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks with high discrimination power across diagnostic categories. The core of MENET is a novel graph edge-wise transform that we propose, which allows us to capture multi-resolution “connectomic” features. Using a rich set of the connectomic features, we devise a graph learning framework to jointly select discriminative edges and assign diagnostic labels for graphs. Experiments on two real datasets show that MENET accurately predicts diagnostic labels and identify brain connectivities highly associated with neurological disorders such as Alzheimer’s Disease and Attention-Deficit/Hyperactivity Disorder.
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References
Borgwardt, K.M., Kriegel, H.P.: Shortest-path kernels on graphs. In: ICDM (2005)
Brown, M.R., Sidhu, G.S., Greiner, R., et al.: ADHD-200 global competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Front. Syst. Neurosci. 6, 69 (2012). https://doi.org/10.3389/fnsys.2012.0006
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186 (2009)
Chincarini, A., Bosco, P., Calvini, P., et al.: Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage 58(2), 469–480 (2011). https://doi.org/10.1016/j.neuroimage.2011.05.083
Choo, I.H., Lee, D.Y., Oh, J.S., et al.: Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer’s disease. Neurobiol. Aging 31(5), 772–779 (2010). https://doi.org/10.1016/j.neurobiolaging.2008.06.015
Dennis, E.L., Thompson, P.M.: Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol. Rev. 24(1), 49–62 (2014). https://doi.org/10.1007/s11065-014-9249-6
Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1–15 (2010). https://doi.org/10.1016/j.neuroimage.2010.06.010
Friston, K.J.: Statistical Parametric Mapping. In: Kötter, R. (eds.) Neuroscience Databases. Springer, Boston (2003). https://doi.org/10.1007/978-1-4615-1079-6_16
Galton, C.J., Patterson, K., Graham, K., et al.: Differing patterns of temporal atrophy in Alzheimer’s disease and semantic dementia. Neurology 57(2), 216–225 (2001). https://doi.org/10.1212/wnl.57.2.216
Gärtner, T., Flach, P., Wrobel, S.: On graph kernels: hardness results and efficient alternatives. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 129–143. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45167-9_11
Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100(1), 253–258 (2003). https://doi.org/10.1073/pnas.0135058100
Hammond, D., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129–150 (2011). https://doi.org/10.1016/j.acha.2010.04.005
Jie, B., Zhang, D., Wee, C.Y., Shen, D.: Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum. Brain Mapp. 35(7), 2876–2897 (2014). https://doi.org/10.1002/hbm.22353
Kang, U., Tong, H., Sun, J.: Fast random walk graph kernel. In: ICDM (2012)
Karas, G., Scheltens, P., Rombouts, S., et al.: Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study. Neuroradiology 49(12), 967–976 (2007). https://doi.org/10.1007/s00234-007-0269-2
Kim, W.H., Pachauri, D., Hatt, C., et al.: Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination. Adv. Neural Inf. Process. Syst. 2012, 1241–1249 (2012)
Kim, W.H., Kim, H.J., Adluru, N., Singh, V.: Latent variable graphical model selection using harmonic analysis: applications to the human connectome project (hcp). In: CVPR, pp. 2443–2451 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: CVPR, pp. 4558–4567 (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)
Ma, X., Wu, G., Kim, W.H.: Enriching statistical inferences on brain connectivity for Alzheimer’s disease analysis via latent space graph embedding. In: ISBI, pp. 1685–1689. IEEE (2020)
Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press (1999)
Marsh, A.A., Finger, E.C., Mitchell, D.G., et al.: Reduced amygdala response to fearful expressions in children and adolescents with callous-unemotional traits and disruptive behavior disorders. Am. J. Psychiatry 165(6), 712–20 (2008). https://doi.org/10.1176/appi.ajp.2007.07071145
Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Initiative, A.D.N.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015). https://doi.org/10.1016/j.neuroimage.2014.10.002
Murias, M., Swanson, J.M., Srinivasan, R.: Functional connectivity of frontal cortex in healthy and ADHD children reflected in EEG coherence. Cereb. Cortex 17(8), 1788–1799 (2006)
Narayanan, A., Chandramohan, M., Venkatesan, R., et al.: graph2vec: learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)
Ng, B., Varoquaux, G., Poline, J.B., Thirion, B., Greicius, M.D., Poston, K.L.: Distinct alterations in Parkinson’s medication-state and disease-state connectivity. NeuroImage Clin. 16, 575–585 (2017)
Riesen, K., Bunke, H.: Graph classification based on vector space embedding. Int. J. Pattern Recognit. Artif. Intell. 23(06), 1053–1081 (2009). https://doi.org/10.1142/7731
Rubia, K., Smith, A.B., Brammer, M.J., Taylor, E.: Temporal lobe dysfunction in medication-Naive boys with attention-deficit/hyperactivity disorder during attention allocation and its relation to response variability. Biol. Psychiat. 62(9), 999–1006 (2007). https://doi.org/10.1016/j.biopsych.2007.02.024
Shervashidze, N., Schweitzer, P., Van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-Lehman graph kernels. JMLR 12(9), 2539–2561 (2011)
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. In: AISTATS (2009)
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: CVPR, pp. 3693–3702 (2017)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., 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). https://doi.org/10.1006/nimg.2001.0978
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38, 146 (2019)
Xu, N., Wang, P., Chen, L., Tao, J., Zhao, J.: MR-GNN: multi-resolution and dual graph neural network for predicting structured entity interactions. arXiv preprint arXiv:1905.09558 (2019)
Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: AAAI (2018)
Acknowledgments
This research was supported by NSF IIS CRII 1948510, NSF IIS 2008602, NIH R01 AG059312, IITP-2020-2015-0-00742, and IITP-2019-0-01906 funded by MSIT (AI Graduate School Program at POSTECH).
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Ma, X., Wu, G., Hwang, S.J., Kim, W.H. (2021). Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds) Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science(), vol 12729. Springer, Cham. https://doi.org/10.1007/978-3-030-78191-0_20
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