Abstract
A confluence of neuroscience and clinical evidence suggests that the disruption of structural connectivity (SC) and functional connectivity (FC) in the brain is an early sign of neurodegenerative diseases years before any clinical signs of the disease progression. Since the changes in SC-FC coupling may provide a potential putative biomarker that detects subtle brain network dysfunction more sensitively than does a single modality, tremendous efforts have been made to understand the relationship between SC and FC from the perspective of connectivity, sub-networks, and network topology. However, the methodology design of current analytic methods lacks the in-depth neuroscience underpinning of to what extent the altered SC-FC coupling mechanisms underline the cognitive decline. To address this challenge, we put the spotlight on a neural oscillation model that characterizes the system behavior of a set of (functional) neural oscillators coupled via (structural) nerve fibers throughout the brain. On top of this, we present a physics-guided graph neural network to understand the synchronization mechanism of system dynamics that is capable of predicting self-organized functional fluctuations. By doing so, we generate a novel SC-FC coupling biomarker that allows us to recognize the early sign of neurodegeneration through the lens of an altered SC-FC relationship. We have evaluated the statistical power and clinical value of new SC-FC biomarker in the early diagnosis of Alzheimer’s disease using the ADNI dataset. Compared to conventional SC-FC coupling methods, our physics-guided deep model not only yields higher prediction accuracy but also reveals the mechanistic role of SC-FC coupling alterations in disease progression.
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
Badhwar, A., Tam, A., Dansereau, C., Orban, P., Hoffstaedter, F., Bellec, P.: Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 8, 73–85 (2017)
Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20(3), 353–364 (2017)
Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)
Breakspear, M., Heitmann, S., Daffertshofer, A.: Generative models of cortical oscillations: neurobiological implications of the Kuramoto model. Front. Hum. Neurosci. 4, 190 (2010)
Chang, C., Glover, G.H.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50(1), 81–98 (2010)
Cummings, J.L., Doody, R., Clark, C.: Disease-modifying therapies for Alzheimer disease: challenges to early intervention. Neurology 69(16), 1622–1634 (2007)
Gohel, S.R., Biswal, B.B.: Functional integration between brain regions at rest occurs in multiple-frequency bands. J. Neurosci. 35(43), 14665–14673 (2015)
Greicius, M.D., Supekar, K., Menon, V., Dougherty, R.F.: Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19(1), 72–78 (2009)
Gu, Z., Jamison, K.W., Sabuncu, M.R., Kuceyeski, A.: Heritability and interindividual variability of regional structure-function coupling. Nat. Commun. 12(1), 4894 (2021)
Hasani, R., Lechner, M., Amini, A., Rus, D., Grosu, R.: Liquid time-constant networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7657–7666 (2021)
Kondo, S., Miura, T.: Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329(5999), 1616–1620 (2010)
Kuramoto, Y., Kuramoto, Y.: Chemical Turbulence. Springer, Heidelberg (1984). https://doi.org/10.1007/978-3-642-69689-3_7
Mitra, A., Snyder, A.Z., Blazey, T., Raichle, M.E.: Lag threads organize the brain’s intrinsic activity. Proc. Natl. Acad. Sci. 112(16), E2235–E2244 (2015)
Palop, J.J., Chin, J., Mucke, L.: A network dysfunction perspective on neurodegenerative diseases. Nature 443(7113), 768–773 (2006)
Park, C.H., Kim, S.Y., Kim, Y.H., Kim, K.: Comparison of the small-world topology between anatomical and functional connectivity in the human brain. Physica A Stat. Mech. Appl. 387(23), 5958–5962 (2008)
Petersen, R.C., et al.: Alzheimer’s disease neuroimaging initiative (ADNI). Neurology 74(3), 201–209 (2010)
Pluchino, A., Rapisarda, A.: Metastability in the Hamiltonian mean field model and Kuramoto model. Physica A 365(1), 184–189 (2006)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)
Tzourio-Mazoyer, N., 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)
Acknowledgment
This work was supported by Foundation of Hope, NIH R01AG068399, NIH R03AG073927. Won Hwa Kim was partially supported by IITP-2019-0-01906 (AI Graduate Program at POSTECH) funded by the Korean government (MSIT).
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
Dan, T., Kim, M., Kim, W.H., Wu, G. (2023). Uncovering Structural-Functional Coupling Alterations for Neurodegenerative Diseases. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-43898-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43897-4
Online ISBN: 978-3-031-43898-1
eBook Packages: Computer ScienceComputer Science (R0)
