Abstract
Identifying topology alterations in white matter connectivity has emerged as a promising avenue for exploring potential markers of Alzheimer’s disease (AD). However, conventional graph learning methods struggle to accurately represent the subtle and heterogeneous topology alterations caused by AD, leading to marginal classification accuracy. In this study, we address this issue through a two-fold approach. Firstly, to more reliably capture AD-induced alterations, we collect multi-shell high-angular resolution diffusion MRI data and construct a topology tensor to incorporate multiple edge-based attributes. Secondly, we propose a novel CNN framework called REST-Net, utilizing lightweight convolutional kernels to integrate the multiple attributes, enhancing its capacity for topology representation. With extensive experiments, REST-Net outperforms seven state-of-the-art graph learning methods for binary and tertiary classification tasks. Of utmost importance, the white matter connections identified by REST-Net guide the selection of target bundles for further analysis, which can potentially provide valuable insights for clinical and pharmacological investigations.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Arenaza-Urquijo, E.M., Vemuri, P.: Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology 90(15), 695–703 (2018)
Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. TPAMI (2022)
Catani, M., Ffytche, D.H.: The rises and falls of disconnection syndromes. Brain 128(10), 2224–2239 (2005)
Chen, H., Koga, H.: GL2vec: graph embedding enriched by line graphs with edge features. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11955, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36718-3_1
De Strooper, B., Karran, E.: The cellular phase of Alzheimer’s disease. Cell 164(4), 603–615 (2016)
Jenkinson, M., et al.: FSL. NeuroImage 62(2), 782–790 (2012)
Jeurissen, B., et al.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103, 411–426 (2014)
Jonkman, L.E., et al.: Relationship between \(\beta \)-amyloid and structural network topology in decedents without dementia. Neurology 95(5), e532–e544 (2020)
Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038–1049 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6(171), 1–12 (2012)
Narayanan, A., et al.: Graph2vec: learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)
Reid, A.T., Evans, A.C.: Structural networks in Alzheimer’s disease. Eur. Neuropsychopharmacol. 23(1), 63–77 (2013)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)
Schult, D.A.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of SciPy (2008)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of ICCV, pp. 618–626 (2017)
Smith, R.E., et al.: Sift2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351 (2015)
Taylor, N.L., Shine, J.M.: A whole new world: embracing the systems-level to understand the indirect impact of pathology in neurodegenerative disorders. J. Neurol. 270(4), 1969–1975 (2023)
Tournier, J.D., et al.: Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 1670 (2010)
Tournier, J.D., et al.: MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019)
Tustison, N.J., et al.: N4ITK: Improved N3 bias correction. TMI 29(6), 1310–1320 (2010)
Xiao, B., et al.: Weakly supervised confidence learning for brain MR image dense parcellation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 409–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_47
Xu, K., et al.: How powerful are graph neural networks? In: Proceedings of ICLR (2018)
Yang, Z., et al.: A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nat. Commun. 12, 7065 (2021)
Yu, M., et al.: The human connectome in Alzheimer disease-relationship to biomarkers and genetics. Nat. Rev. Neurol. 17(9), 545–563 (2021)
Zhang, X., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of CVPR, pp. 6848–6856 (2018)
Acknowledgements
This work was supported in part by National Natural Science Foundation of China (No. 62203355, 62131015, 62073260, 12271434), Science and Technology Commission of Shanghai Municipality (STCSM) (No. 21010502600), and Shanghai Pujiang Program (No. 21PJ1421400).
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Liu, F. et al. (2024). Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convolutional Neural Networks. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_4
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