Abstract
It has been of great interest in the neuroimaging community to discover brain functional networks (FBNs) based on task functional magnetic resonance imaging (tfMRI). A variety of methods have been used to model tfMRI sequences so far, such as recurrent neural network (RNN) and Autoencoder. However, these models are not designed to incorporate the characteristics of tfMRI sequences, and the same signal values at different time points in a fMRI time series may rep-resent different states and meanings. Inspired by cloze learning methods and the human ability to judge polysemous words based on context, we proposed a self-supervised a Multi-head Attention-based Masked Sequence Model (MAMSM), as BERT model uses (Masked Language Modeling) MLM and multi-head attention to learn the different meanings of the same word in different sentences. MAMSM masks and encodes tfMRI time series, uses multi-head attention to calculate different meanings corresponding to the same signal value in fMRI sequence, and obtains context information through MSM pre-training. Furthermore this work redefined a new loss function to extract FBNs according to the task de-sign information of tfMRI data. The model has been applied to the Human Connectome Project (HCP) task fMRI dataset and achieves state-of-the-art performance in brain temporal dynamics, the Pearson correlation coefficient between learning features and task design curves was more than 0.95, and the model can extract more meaningful network besides the known task related brain networks.
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References
Cabral, J., Kringelbach, M.L., Deco, G.: Exploring the network dynamics underlying brain activity during rest. Prog. Neurobiol. 114, 102–131 (2014)
Kanwisher, N.: Functional specificity in the human brain: a window into the functional architecture of the mind. Proc. Natl. Acad. Sci. 107(25), 11163–11170 (2010)
Beckmann, C.F., et al.: General multilevel linear modeling for group analysis in FMRI. Neuroimage 20(2), 1052–1063 (2003)
Jiang, X., et al.: Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex. Hum. Brain Mapp. 36(12), 5301–5319 (2015)
Lv, J., et al.: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Trans. Biomed. Eng. 62(4), 1120–1131 (2015)
Li, X., et al.: Multple-demand system identification and characterization via sparse representations of fMRI data. In: 2016 IEEE 13th International Symposium on Biomedical Imaging(ISBI). IEEE (2016)
Smith, S.M., et al.: Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. 106(31), 13040–13045 (2009)
Huang, H., et al.: Modeling task fMRI data via mixture of deep expert networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)
Huang, H., et al.: Modeling task fMRI data via deep convolutional autoencoder. IEEE Trans. Med. Imaging 37(7), 1551–1561 (2018)
Zhao, Y., et al.: Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks. IEEE Trans. Biomed. Eng. 65(9), 1975–1984 (2018)
Li, Q., et al.: Simultaneous spatial-temporal decomposition of connectome-scale brain networks by deep sparse recurrent auto-encoders. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 579–591. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_45
Sak, H., et al.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)
Wang, H., et al.: Recognizing brain states using deep sparse recurrent neural network. IEEE Trans. Med. Imaging 38, 1058–1068 (2018)
Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)
Glasser, M.F., et al.: The minimal preprocessing pipelines for the human Connectome project. Neuroimage 80, 105–124 (2013)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)
Devlin, J., et al.: Bert: pre-training of deep bidirectional transformers for language under-standing (2018)
Xie, Z., et al.: Simmim: A simple framework for masked image modeling. arXiv preprint arXiv:2111.09886 (2021)
Dong, Q., et al.: Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, (2020) https://doi.org/10.1007/978-3-030-59728-3_50
He, K., et al.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021)
Tang, G., et al.: Why self-attention? a targeted evaluation of neural machine translation architectures. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp. 4263–4272 (2018)
Abraham, A., et al.: Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014)
Acknowledgement
The work was supported by the National Natural Science Foundation of China (NSFC61976131 and NSFC61936007).
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He, M. et al. (2022). Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks. 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 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_28
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