Abstract
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
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References
Aganj, I., et al.: A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15(4), 414–425 (2011)
Aganj, I., Lenglet, C., Sapiro, G., Yacoub, E., Ugurbil, K., Harel, N.: Reconstruction of the orientation distribution function in single-and multiple-shell q-ball imaging within constant solid angle. Magn. Resonan. Med. 64(2), 554–566 (2010)
Aganj, I., Mora, J., Frau-Pascual, A., Fischl, B., Initiative, A.D.N., et al.: Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer’s disease. Alzheimer’s Dement.: Diagn. Assess. Dis. Monit. (2023)
Aganj, I., Prasad, G., Srinivasan, P., Yendiki, A., Thompson, P.M., Fischl, B.: Structural brain network augmentation via Kirchhoff’s laws. In: Joint Annual Meeting of ISMRM-ESMRMB, vol. 22, p. 2665 (2014). http://nmr.mgh.harvard.edu/~iman/ConductanceModel_ISMRM14_iman.pdf
Arslan, S., Ktena, S.I., Glocker, B., Rueckert, D.: Graph saliency maps through spectral convolutional networks: application to sex classification with brain connectivity. In: Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities: Second International Workshop, GRAIL 2018 and First International Workshop, Beyond MIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 2, pp. 3–13 (2018)
Beacher, F.D., et al.: Autism attenuates sex differences in brain structure: a combined voxel-based morphometry and diffusion tensor imaging study. Am. J. Neuroradiol. 33(1), 83–89 (2012)
Bresson, X., Laurent, T.: Residual gated graph convnets. arXiv preprint arXiv:1711.07553 (2017)
Dennis, E.L., et al.: Development of brain structural connectivity between ages 12 and 30: a 4-tesla diffusion imaging study in 439 adolescents and adults. Neuroimage 64, 671–684 (2013)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Frau-Pascual, A., et al.: Conductance-based structural brain connectivity in aging and dementia. Brain Connect. 11(7), 566–583 (2021)
Gur, R.E., Gur, R.C.: Sex differences in brain and behavior in adolescence: findings from the philadelphia neurodevelopmental cohort. N & B Reviews
He, Y., Zhang, X., Huang, J., Rozemberczki, B., Cucuringu, M., Reinert, G.: Pytorch geometric signed directed: a software package on graph neural networks for signed and directed graphs. arXiv preprint arXiv:2202.10793 (2022)
Hu, W., et al.: Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)
Ingalhalikar, M., et al.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. 111(2), 823–828 (2014)
Jahanshad, N., et al.: Sex differences in the human connectome: 4-tesla high angular resolution diffusion imaging (hardi) tractography in 234 young adult twins. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 939–943. IEEE (2011)
Karlsgodt, K.H., Sun, D., Cannon, T.D.: Structural and functional brain abnormalities in schizophrenia. Curr. Direct. Psychol. Sci. 19(4), 226–231 (2010)
Kazi, A., et al.: DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging. In: Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 277–281. SPIE (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage 169, 431–442 (2018)
LaMontagne, P.J., et al.: Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv, pp. 2019–12 (2019)
Leoutsakos, J.M., Gross, A., Jones, R., Albert, M., Breitner, J.: ‘Alzheimer’s progression score’: development of a biomarker summary outcome for ad prevention trials. The J. Prevent. Alzheimer’s Disease 3(4), 229 (2016)
Morris, C., et al.: Weisfeiler and leman go neural: higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4602–4609
Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)
Tolan, E., Isik, Z.: Graph theory based classification of brain connectivity network for autism spectrum disorder. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2018. LNCS, vol. 10813, pp. 520–530. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78723-7_45
Wang, J., et al.: Alterations in brain network topology and structural-functional connectome coupling relate to cognitive impairment. Front. Aging Neurosci. 10, 404 (2018)
Wang, Y.M., et al.: Altered grey matter volume and white matter integrity in individuals with high Schizo-obsessive traits, high schizotypal traits and obsessive-compulsive symptoms. Asian J. Psychiatry 52, 102096 (2020)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graphics (tog)
Williamson, J., et al.: Sex differences in brain functional connectivity of hippocampus in mild cognitive impairment. Front. Aging Neurosci. (2022)
Xing, X., et al.: Dynamic spectral graph convolution networks with assistant task training for early MCI diagnosis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 639–646. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_70
Zhang, J., et al.: Sex differences of uncinate fasciculus structural connectivity in individuals with conduct disorder. BioMed Res. Int. (2014)
Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Networks 6(1), 1–23 (2019)
Acknowledgments
Support for this research was provided by the National Institutes of Health (NIH), specifically the National Institute on Aging (NIA; RF1AG068261).
Additional support was provided in part by the BRAIN Initiative Cell Census Network grant U01MH117023, the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, R01EB023281, R01EB006758, R21EB018907, R01EB019956, P41EB030006), the NIA (R56AG064027, R01AG064027, R01AG008122, R01AG016495, R01AG070988), the National Institute of Mental Health (R01MH121885, RF1MH123195), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, U01NS086625, U24NS10059103, R01NS105820), the NIH Blueprint for Neuroscience Research (U01MH093765), part of the multi-institutional Human Connectome Project, and the Michael J. Fox Foundation for Parkinson’s Research (MJFF-021226). Computational resources were provided through the Massachusetts Life Sciences Center.
B. Fischl has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. His interests were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham per their conflict-of-interest policies.
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Kazi, A., Mora, J., Fischl, B., Dalca, A.V., Aganj, I. (2024). Multi-head Graph Convolutional Network for Structural Connectome Classification. In: Ahmadi, SA., Pereira, S. (eds) Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology. MICCAI 2023. Lecture Notes in Computer Science, vol 14373. Springer, Cham. https://doi.org/10.1007/978-3-031-55088-1_3
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