Abstract
Advancements in neuroimaging technology have made it possible to measure the connectivity evolution between different brain regions over time. Emerging evidence shows that some critical brain regions, known as hub nodes, play a significant role in updating brain network connectivity over time. However, current spatiotemporal hub identification is built on static network-based approaches, where hub regions are identified independently for each temporal brain network without considering their temporal consistency, and fails to align the evolution of hubs with changes in connectivity dynamics. To address this problem, we propose a novel spatiotemporal hub identification method that utilizes dynamic graph embedding to distinguish temporal hubs from peripheral nodes. Specially, to preserve the time consistency information, we put the dynamic graph embedding learning upon a smooth physics model of network-to-network evolution, which mathematically expresses as a total variation of dynamic graph embedding with respect to time. A novel Grassmannian manifold optimization scheme is further introduced to learn the embeddings accurately and capture the time-varying topology of brain network. Experimental results on real data demonstrate the highest temporal consistency in hub identification, surpassing conventional approaches.
D. Yang and H. Shen—These authors contributed equally.
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
Bullmore, E., Sporns, O.: The economy of brain network organization. Nat. Rev. Neurosci. 13, 336 (2012)
Bertolero, M.A., Yeo, B.T.T., Bassett, D.S., D’Esposito, M.: A mechanistic model of connector hubs, modularity and cognition. Nat. Hum. Behav. 2, 765–777 (2018)
Park, H.-J., Friston, K.: Structural and functional brain networks: from connections to cognition. Science 342, 1238411 (2013)
van den Heuvel, M.P., Sporns, O.: Network hubs in the human brain. Trends Cogn. Sci. 17, 683–696 (2013)
Cole, M.W., Reynolds, J.R., Power, J.D., Repovs, G., Anticevic, A., Braver, T.S.: Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013)
Pedersen, M., Omidvarnia, A., Zalesky, A., Jackson, G.D.: On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis. Neuroimage 181, 85–94 (2018)
Lee, W.J., et al.: Regional Aβ-tau interactions promote onset and acceleration of Alzheimer’s disease tau spreading. Neuron 110, 1932–1943, e1935 (2022)
Achard, S., et al.: Hubs of brain functional networks are radically reorganized in comatose patients. Proc. Natl. Acad. Sci. 109, 20608–20613 (2012)
Frontzkowski, L., et al.: Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading. Nat. Commun. 13, 4899 (2022)
Fornito, A., Zalesky, A., Breakspear, M.: The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159 (2015)
Buckner, R.L., et al.: Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29, 1860–1873 (2009)
Gratton, C., Nomura, E.M., Pérez, F., Esposito, M.D.: Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. J. Cogn. Neurosci. 24, 1275–1285 (2012)
Tu, W., Ma, Z., Zhang, N.: Brain network reorganization after targeted attack at a hub region. Neuroimage 237, 118219 (2021)
Jiao, Z., Xia, Z., Cai, M., Zou, L., Xiang, J., Wang, S.: Hub recognition for brain functional networks by using multiple-feature combination. Comput. Electr. Eng. 69, 740–752 (2018)
Sporns, O.: Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. 20, 111 (2018)
Yang, D., et al.: Joint hub identification for brain networks by multivariate graph inference. Med. Image Anal. 73, 102162 (2021)
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. In: Advances in Neural Information Processing Systems, vol. 19 (2006)
Cetingul, H.E., Vidal, R.: Intrinsic mean shift for clustering on Stiefel and Grassmann manifolds. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1896–1902. IEEE (2009)
Tijms, B.M., et al.: Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol. Aging 34, 2023–2036 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, D. et al. (2023). Spatiotemporal Hub Identification in Brain Network by Learning Dynamic Graph Embedding on Grassmannian Manifold. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-43895-0_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43894-3
Online ISBN: 978-3-031-43895-0
eBook Packages: Computer ScienceComputer Science (R0)
