Abstract
Human brain networks convey important insights in understanding the mechanism of many mental disorders. However, it is difficult to determine a universal optimal among various tractography methods for general diagnosis tasks. To address this issue, tentative studies, aiming at the identification of some mental disorders, make an effective concession by exploiting multi-modal brain networks. In this paper, we propose to predict the clinical measures as a more comprehensive and stable assessment of brain abnormalities. We develop a graph convolutional network (GCN) framework to integrate heterogeneous brain networks. Particularly, an adaptive pooling scheme is designed, catering to the modal structural diversity and sharing the advantages of locality, loyalty and likely as in standard convolutional networks. The experimental results demonstrate that our method achieves state-of-the-art prediction results, and validates the advantages of the utilization of multi-modal brain networks in that, more modals are always at least as good as the best modal, if not better.
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Acknowledgements
This study was partially funded by NSF IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956, and NIH R21 AG056782, R01 AG049371, U54 EB020403, P41 EB015922, R56 AG058854.
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Zhang, Y., Zhan, L., Cai, W., Thompson, P., Huang, H. (2019). Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_24
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DOI: https://doi.org/10.1007/978-3-030-32251-9_24
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