Abstract
As one of the common complications, vascular cognitive impairment (VCI) comprises a range of cognitive disorders related to cerebral vessel diseases like moyamoya disease (MMD), and it is reversible by surgical revascularization in its early stage. However, diagnosis of VCI is time-consuming and less accurate if it solely relies on neuropsychological examination. Even if some existing research connected VCI with medical image, most of them were solely statistical methods with single modality. Therefore, we propose a graph-based framework to integrate both dual-modal imaging information (rs-fMRI and DTI) and non-imaging information to identify VCI in adult MMDs. Unlike some previous studies based on node-level classification, the proposed graph-level model can fully utilize imaging information and improve interpretability of results. Specifically, we firstly design two different graphs for each subject based on characteristics of different modalities and feed them to a dual-modal graph convolution network to extract complementary imaging features and select important brain biomarkers for each subject. Node-based normalization and constraint item are further devised to weakening influence of over-smoothing and natural difference caused by non-imaging information. Experiments on a real dataset not only achieve accuracy of \(80.0\%\), but also highlight some salient brain regions related to VCI in adult MMDs, demonstrating the effectiveness and clinical interpretability of our proposed method.
X. Chen and W. Zeng—These authors contributed equally to this work.
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Moorhouse, P., Rockwood, K.: Vascular cognitive impairment: current concepts and clinical developments. Lancet Neurol. 7(3), 246–255 (2008)
Araki, Y., Takagi, Y., Ueda, K., et al.: Cognitive function of patients with adult moyamoya disease. J. Stroke Cerebrovasc. Dis. 23(7), 1789–1794 (2014)
Steffens, D.C., Otey, E., Alexopoulos, G.S., et al.: Perspectives on depression, mild cognitive impairment, and cognitive decline. Arch. Gen. Psychiatry 63(2), 130–138 (2006)
Lei, Y., Li, Y.J., Guo, Q.H., et al.: Postoperative executive function in adult moyamoya disease: a preliminary study of its functional anatomy and behavioral correlates. J. Neurosurg. 126(2), 527–536 (2017)
Kantarci, K., Murray, M.E., Schwarz, C.G., et al.: White-matter integrity on DTI and the pathologic staging of Alzheimer’s disease. Neurobiol. Aging 56, 172–179 (2017)
Lei, Y., Li, Y., Ni, W., et al.: Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study. Brain Res. 1546, 27–33 (2014)
Kazumata, K., Tha, K.K., Narita, H., et al.: Chronic ischemia alters brain microstructural integrity and cognitive performance in adult moyamoya disease. Stroke 46(2), 354–360 (2015)
Liu, Z., He, S., Xu, Z., et al.: Association between white matter impairment and cognitive dysfunction in patients with ischemic Moyamoya disease. BMC Neurol. 20(1), 302 (2020). https://doi.org/10.1186/s12883-020-01876-0
Lei, Y., Chen X., Su, J.B., et al.: Recognition of cognitive impairment in adult moyamoya disease: a classifier based on high-order resting-state functional connectivity network. Front. Neural Circuits 14, 603208 (2020)
Yan, C.-G., Wang, X.-D., Zuo, X.-N., Zang, Y.-F.: DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics 14(3), 339–351 (2016). https://doi.org/10.1007/s12021-016-9299-4
Cui, Z.X., Zhong, S.Y., Xu, P.F., et al.: PANDA: a pipeline toolbox for analyzing brain diffusion images. Front. Hum. Neurosci. 7, 42 (2013)
Ktena, S.I., Parisot, S., Ferrante, E., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)
Parisot, S., Ktena, S.I., Ferrante, E., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. arXiv preprint arXiv:1806.01738 (2018)
Allen, E.A., Damaraju, E., Plis, S.M., et al.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676 (2014)
Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: International Conference on Machine Learning, pp. 3734–3743 (2019)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. arXiv preprint arXiv:1801.07606 (2018)
Zhao, L., Akoglu, L.: PairNorm: tackling oversmoothing in GNNs. In: ICLR (2019)
He, K.M., Zhang, X.Y., Ren, S.Q., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, Y., Chung, A.C.S.: Edge-variational graph convolutional networks for uncertainty-aware disease prediction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 562–572. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_55
Li, X.X., Zhou, Y., Dvornek, N., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)
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Chen, X. et al. (2022). Identification of Vascular Cognitive Impairment in Adult Moyamoya Disease via Integrated Graph Convolutional Network. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_64
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