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Interpretation of Brain Morphology in Association to Alzheimer’s Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12474)


We propose a mesh-based technique to aid in the classification of Alzheimer’s disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer’s type.


  • Graph convolutional networks
  • Alzheimer’s disease classification
  • Triangulated meshes
  • Neural network interpretability

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

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  1. Arfken, G.B., Weber, H.J., Harris, F.E.: Mathematical Methods for Physicists. 3 edn. Academic Press, New York (2013).

  2. Beheshti, I., et al.: Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput. Biol. Med. 83, 109–119 (2017).

    CrossRef  Google Scholar 

  3. Besson, P., et al.: Intra-subject reliability of the high-resolution whole-brain structural connectome. NeuroImage 102, 283–293 (2014).

    CrossRef  Google Scholar 

  4. Brookmeyer, R., et al.: Forecasting the global burden of Alzheimer’s disease. Alzheimer Dement. 3, 186–191 (2007).

    CrossRef  Google Scholar 

  5. Bruyn, G.: Atlas of the Cerebral Sulci, vol. 93. G. Thieme Verlag, New York (1991)

    Google Scholar 

  6. De Jong, L.W., et al.: Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain 131(12), 3277–3285 (2008).

    CrossRef  Google Scholar 

  7. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016).

  8. Derflinger, S., et al.: Grey-matter atrophy in Alzheimer’s disease is asymmetric but not lateralized. J. Alzheimer Dis. 25(2), 347–357 (2011).

    CrossRef  Google Scholar 

  9. Dickerson, B.C., et al.: The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb. Cortex 19(3), 497–510 (2009).

    CrossRef  Google Scholar 

  10. Fischl, B.: FreeSurfer. NeuroImage 62(2), 774–781 (2012).

  11. Fischl, B., et al.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272–284 (1999).<272::AID-HBM10>3.0.CO;2-4

    CrossRef  Google Scholar 

  12. Fung, Y.R., et al.: Alzheimer’s disease brain MRI classification: challenges and insights. arXiv preprint arXiv:1906.04231 (2019).

  13. Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 3, pp. 2672–2680 (2014).

  14. Gupta, A., Ayhan, M., Maida, A.: Natural image bases to represent neuroimaging data. In: International Conference on Machine Learning, pp. 987–994 (2013).

  15. Gutiérrez-Becker, B., Wachinger, C.: Learning a conditional generative model for anatomical shape analysis. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 505–516. Springer, Cham (2019).

    CrossRef  Google Scholar 

  16. He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016 December, pp. 770–778 (2016).

  17. Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3d convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 126–130. IEEE (2016).

  18. Hu, K., et al.: Multi-scale features extraction from baseline structure MRI for MCI patient classification and ad early diagnosis. Neurocomputing 175, 132–145 (2016).

  19. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015. Proceedings of Machine Learning Research, vol. 1, pp. 448–456 (2015).

  20. Jack, C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods (2008).

  21. Kälin, A.M., et al.: Subcortical shape changes, hippocampal atrophy and cortical thinning in future Alzheimer’s disease patients. Front. Aging Neurosci. 9, 38 (2017).

  22. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015).

  23. Li, R., et al.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 305–312. Springer, Cham (2014).

    CrossRef  Google Scholar 

  24. Liu, F., Wee, C.Y., Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84, 466–475 (2014).

    CrossRef  Google Scholar 

  25. Liu, T., et al.: Cortical gyrification and sulcal spans in early stage Alzheimer’s disease. PLoS ONE (2012).

    CrossRef  Google Scholar 

  26. Masci, J., Boscaini, D., Bronstein, M.M., Vandergheynst, P.: Geodesic convolutional neural networks on riemannian manifolds. In: Proceedings of the IEEE International Conference on Computer Vision, 2015 February, pp. 832–840 (2015).

  27. McKhann, G.M., et al.: The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer Dement. 7(3), 263–269 (2011).

    CrossRef  Google Scholar 

  28. Pacheco, J., et al.: Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiol. Aging 36(2), 903–908 (2015).

    MathSciNet  CrossRef  Google Scholar 

  29. Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018).

    CrossRef  Google Scholar 

  30. Punjabi, A., et al.: Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks. PLoS ONE 14(12), 1–14 (2019).

    CrossRef  Google Scholar 

  31. Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3D faces using convolutional mesh autoencoders. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 725–741. Springer, Cham (2018).

    CrossRef  Google Scholar 

  32. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence, pp. 399–421. MIT Press, Cambridge, MA (2013)

    Google Scholar 

  33. Selvaraju, R.R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: International Journal of Computer Vision (2019).

  34. Shuman, D.I., et al.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013).

    CrossRef  Google Scholar 

  35. Thompson, P.M., et al.: Cortical variability and asymmetry in normal aging and Alzheimer’s disease. Cereb. Cortex 8(6), 492–509 (1998).

    CrossRef  Google Scholar 

  36. Westman, E., Muehlboeck, J.S., Simmons, A.: Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. NeuroImage 62(1), 229–238 (2012).

    CrossRef  Google Scholar 

  37. Wu, Z., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 1–21 (2020).

  38. Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59(2), 895–907 (2012).

    MathSciNet  CrossRef  Google Scholar 

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This work was funded in part by the Biomedical Data Driven Discovery Training Grant from the National Library of Medicine (5T32LM012203) through Northwestern University, and the National Institute on Aging. The authors would also like to thank the QUEST High Performance Computing Cluster at Northwestern University for computational resources.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Correspondence to Emanuel A. Azcona .

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Azcona, E.A. et al. (2020). Interpretation of Brain Morphology in Association to Alzheimer’s Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Goksel, O., Rekik, I. (eds) Shape in Medical Imaging. ShapeMI 2020. Lecture Notes in Computer Science(), vol 12474. Springer, Cham.

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