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International Workshop on Machine Learning in Clinical Neuroimaging

MLCN 2021: Machine Learning in Clinical Neuroimaging pp 103–112Cite as

Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 13001)

Abstract

Understanding the topographic heterogeneity of cortical organisation is an essential step towards precision modelling of neuropsychiatric disorders. While many cortical parcellation schemes have been proposed, few attempt to model inter-subject variability. For those that do, most have been proposed for high-resolution research quality data, without exploration of how well they generalise to clinical quality scans. In this paper, we benchmark and ensemble four different geometric deep learning models on the task of learning the Human Connectome Project (HCP) multimodal cortical parcellation. We employ Monte Carlo dropout to investigate model uncertainty with a view to propagate these labels to new datasets. Models achieved an overall Dice overlap ratio of \({>}0.85\,\pm \,0.02\). Regions with the highest mean and lowest variance included V1 and areas within the parietal lobe, and regions with the lowest mean and highest variance included areas within the medial frontal lobe, lateral occipital pole and insula. Qualitatively, our results suggest that more work is needed before geometric deep learning methods are capable of fully capturing atypical cortical topographies such as those seen in area 55b. However, information about topographic variability between participants was encoded in vertex-wise uncertainty maps, suggesting a potential avenue for projection of this multimodal parcellation to new datasets with limited functional MRI, such as the UK Biobank.

Keywords

  • Human connectome project
  • Geometric deep learning
  • Cortical parcellation

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Notes

  1. 1.

    https://github.com/zhaofenqiang/Spherical_U-Net.

References

  1. Abdollahi, R.O., et al.: Correspondences between retinotopic areas and myelin maps in human visual cortex. Neuroimage 99, 509–524 (2014)

    Google Scholar 

  2. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)

    Google Scholar 

  3. Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: Voxresnet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446–455 (2018)

    Google Scholar 

  4. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)

  5. Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)

    Google Scholar 

  6. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)

    Google Scholar 

  7. Frost, M.A., Goebel, R.: Measuring structural-functional correspondence: spatial variability of specialised brain regions after macro-anatomical alignment. Neuroimage 59(2), 1369–1381 (2012)

    Google Scholar 

  8. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059. PMLR (2016)

    Google Scholar 

  9. Given, N.A.: Benchmarking geometric deep learning for cortical segmentation and neurodevelopmental phenotype prediction (2021, in preparation)

    Google Scholar 

  10. Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)

    Google Scholar 

  11. Glasser, M.F., et al.: The human connectome project’s neuroimaging approach. Nat. Neurosci. 19(9), 1175–1187 (2016)

    Google Scholar 

  12. Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013)

    Google Scholar 

  13. Glasser, M.F., Van Essen, D.C.: Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J. Neurosci. 31(32), 11597–11616 (2011)

    Google Scholar 

  14. Gordon, E.M., et al.: Individual-specific features of brain systems identified with resting state functional correlations. Neuroimage 146, 918–939 (2017)

    Google Scholar 

  15. Gratton, C., et al.: Defining individual-specific functional neuroanatomy for precision psychiatry. Biol. Psychiatr. 88, 28-39 (2019)

    Google Scholar 

  16. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Google Scholar 

  17. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)

    Google Scholar 

  18. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  19. Kong, R., et al.: Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29(6), 2533–2551 (2019)

    Google Scholar 

  20. Makropoulos, A., et al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88–112 (2018)

    Google Scholar 

  21. Miller, K.L., et al.: Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19(11), 1523–1536 (2016)

    Google Scholar 

  22. Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  23. Öngür, D., Ferry, A.T., Price, J.L.: Architectonic subdivision of the human orbital and medial prefrontal cortex. J. Comp. Neurol. 460(3), 425–449 (2003)

    Google Scholar 

  24. Reinke, A., et al.: Common limitations of image processing metrics: a picture story. arXiv preprint arXiv:2104.05642 (2021)

  25. Robinson, E.C., et al.: Multimodal surface matching with higher-order smoothness constraints. Neuroimage 167, 453–465 (2018)

    Google Scholar 

  26. Robinson, E.C., et al.: MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414–426 (2014)

    Google Scholar 

  27. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

  28. Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imaging 29(10), 1714–1729 (2010)

    Google Scholar 

  29. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M., et al. (eds.) DLMIA 2017, ML-CDS 2017. LNCS, vol. 10553, pp. 240–248. Springer (2017). https://doi.org/10.1007/978-3-319-67558-9_28

  30. Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J., Coalson, T.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22(10), 2241–2262 (2012)

    Google Scholar 

  31. Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    Google Scholar 

  32. Zhao, F., et al.: Spherical u-net on cortical surfaces: methods and applications. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds.) IPMI 2019, LNCS, vol. 11492, pp. 855–866. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_67

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Acknowledgements

Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University [31].

Author information

Authors and Affiliations

  1. Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK

    Logan Z. J. Williams, A. David Edwards & Emma C. Robinson

  2. Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King’s College London, London, SE1 7EH, UK

    Logan Z. J. Williams, Abdulah Fawaz & Emma C. Robinson

  3. Departments of Radiology and Neuroscience, Washington University Medical School, Saint Louis, MO, 63110, USA

    Matthew F. Glasser

  4. Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK

    A. David Edwards

  5. MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK

    A. David Edwards

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  1. Logan Z. J. Williams
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  2. Abdulah Fawaz
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  3. Matthew F. Glasser
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  4. A. David Edwards
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  5. Emma C. Robinson
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Corresponding author

Correspondence to Logan Z. J. Williams .

Editor information

Editors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Ahmed Abdulkadir

  2. Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  3. The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Mohamad Habes

  4. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Vinod Kumar

  5. University College London, London, UK

    Jane Maryam Rondina

  6. University Medical Center Utrecht, Utrecht, The Netherlands

    Chantal Tax

  7. University of Oslo, Oslo, Norway

    Thomas Wolfers

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Williams, L.Z.J., Fawaz, A., Glasser, M.F., Edwards, A.D., Robinson, E.C. (2021). Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-87586-2_11

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  • Online ISBN: 978-3-030-87586-2

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