Abstract
Brain functional connectivity analysis is important for understanding brain development, aging, sexual distinction and brain disorders. Existing methods typically adopt the resting-state functional connectivity (rs-FC) measured by functional MRI as an effective tool, while they either neglect the importance of information exchange between different brain regions or the heterogeneity of brain activities. To address these issues, we propose a Path-based Heterogeneous Brain Transformer Network (PH-BTN) for analyzing rs-FC. Specifically, to integrate the path importance and heterogeneity of rs-FC for a comprehensive description of the brain, we first construct the brain functional network as a path-based heterogeneous graph using prior knowledge and gain initial edge features from rs-FC. Then, considering the constraints of graph convolution in aggregating long-distance and global information, we design a Heterogeneous Path Graph Transformer Convolution (HP-GTC) module to extract edge features by aggregating different paths’ information. Furthermore, we adopt Squeeze-and-Excitation (SE) with HP-GTC modules, which can alleviate the over-smoothing problem and enhance influential features. Finally, we apply a readout layer to generate the final graph embedding to estimate brain age and gender, and thoroughly evaluate the PH-BTN on the Baby Connectome Project (BCP) dataset. Experimental results demonstrate the superiority of PH-BTN over other state-of-the-art methods. The proposed PH-BTN offers a powerful tool to investigate and explore brain functional connectivity.
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References
Axer, M., Amunts, K.: Scale matters: the nested human connectome. Science 378(6619), 500–504 (2022)
Bao, A.M., Swaab, D.F.: Sex differences in the brain, behavior, and neuropsychiatric disorders. Neuroscientist 16(5), 550–565 (2010)
Cai, H., Gao, Y., Liu, M.: Graph transformer geometric learning of brain networks using multimodal MR images for brain age estimation. IEEE Trans. Med. Imaging 42(2), 456–466 (2023)
Cole, J.H., Franke, K.: Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40(12), 681–690 (2017)
Friston, K.J.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2(1–2), 56–78 (1994)
Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E.V., Adeli, E., Pohl, K.M.: Spatio-temporal graph convolution for resting-state fMRI analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 528–538. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_52
Gao, W., Alcauter, S., Smith, J.K., Gilmore, J.H., Lin, W.: Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220, 1173–1186 (2015)
He, S., Grant, P.E., Ou, Y.: Global-local transformer for brain age estimation. IEEE Trans. Med. Imaging 41(1), 213–224 (2021)
Hou, Y., et al.: Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15(10), 565–581 (2019)
Howell, B.R., et al.: The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. Neuroimage 185, 891–905 (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Jung, J., Cloutman, L.L., Binney, R.J., Ralph, M.A.L.: The structural connectivity of higher order association cortices reflects human functional brain networks. Cortex 97, 221–239 (2017)
Kan, X., Dai, W., Cui, H., Zhang, Z., Guo, Y., Yang, C.: Brain network transformer. arXiv preprint arXiv:2210.06681 (2022)
Kawahara, J., et al.: Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038–1049 (2017)
Kim, B.H., Ye, J.C.: Understanding graph isomorphism network for rs-fMRI functional connectivity analysis. Front. Neurosci. 14, 630 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)
Li, X., et al.: Pooling regularized graph neural network for fMRI biomarker analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 625–635. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_61
Li, Y., et al.: Brain connectivity based graph convolutional networks and its application to infant age prediction. IEEE Trans. Med. Imaging 41(10), 2764–2776 (2022)
Nikolentzos, G., Dasoulas, G., Vazirgiannis, M.: K-hop graph neural networks. Neural Netw. 130, 195–205 (2020)
Thiebaut de Schotten, M., Forkel, S.J.: The emergent properties of the connected brain. Science 378(6619), 505–510 (2022)
Shi, G., Zhu, Y., Liu, W., Yao, Q., Li, X.: Heterogeneous graph-based multimodal brain network learning. arXiv e-prints pp. arXiv-2110 (2021)
Smith, S.M., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219 (2004)
Van Den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10–48550 (2017)
Wang, L., Li, K., Hu, X.P.: Graph convolutional network for fMRI analysis based on connectivity neighborhood. Netw. Neurosci. 5(1), 83–95 (2021)
Weis, S., Patil, K.R., Hoffstaedter, F., Nostro, A., Yeo, B.T., Eickhoff, S.B.: Sex classification by resting state brain connectivity. Cereb. Cortex 30(2), 824–835 (2020)
Wen, X., Wang, R., Yin, W., Lin, W., Zhang, H., Shen, D.: Development of dynamic functional architecture during early infancy. Cereb. Cortex 30(11), 5626–5638 (2020)
Wu, K., Taki, Y., Sato, K., Hashizume, H., Sassa, Y., et al.: Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence. PLoS ONE 8(2), e55347 (2013)
Yao, D., Yang, E., Sun, L., Sui, J., Liu, M.: Integrating multimodal MRIs for adult ADHD identification with heterogeneous graph attention convolutional network. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds.) PRIME 2021. LNCS, vol. 12928, pp. 157–167. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87602-9_15
Yin, W., Li, L., Wu, F.X.: A graph attention neural network for diagnosing ASD with fMRI data. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1131–1136. IEEE (2021)
Zhang, H., et al.: Classification of brain disorders in rs-fMRI via local-to-global graph neural networks. IEEE Trans. Med. Imaging 42(2), 444–455 (2023)
Acknowledgements
This work was supported by Guangdong Key Laboratory of Human Digital Twin Technology (2022B1212010004) and Fundamental Research Funds for the Central Universities (2022ZYGXZR104). This work also utilized approaches developed by the efforts of the UNC/UMN Baby Connectome Project Consortium.
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Fang, R. et al. (2023). Path-Based Heterogeneous Brain Transformer Network for Resting-State Functional Connectivity Analysis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_32
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