Abstract
Most analyses on functional brain connectivity across a group of brains are under the assumption that the positions of the voxels are aligned into a common space. However, the alignment errors are inevitable. To address such issue, a distributional representation for resting-state functional brain connectivity is proposed here. Unlike other relevant connectivity analyses that only consider connections with higher correlation values between voxels, the distributional approach takes the whole picture. The spatial structure of connectivity is captured by the distance between voxels so that the relative position information is preserved. The distributional representation can be visualized to find outliers in a large dataset. The centroid of a group of brains is discovered. The experimental results show that resting-state brains are distributed on the ‘orbit’ around their categorical centroid. In contrast to the main-stream representation such as selected network properties for disease classification, the proposed representation is task-free, which provides a promising foundation for further analysis on functional brain connectivity in various ends.
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References
Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of FMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)
Cao, B., et al.: t-bne: Tensor-based brain network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining SIAM, pp. 189–197 (2017)
Wang, S., He, L., Cao, B., Lu, C.T., Yu, P.S., Ragin, A.B.: Structural deep brain network mining. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 475–484. ACM (2017)
Shen, H., Wang, L., Liu, Y., Hu, D.: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of FMRI. Neuroimage 49(4), 3110–3121 (2010)
Hayasaka, S., Laurienti, P.J.: Comparison of characteristics between region-and voxel-based network analyses in resting-state FMRI data. Neuroimage 50(2), 499–508 (2010)
Tang, J., Qu, M., Mei, Q.: Pte: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174. ACM (2015)
Nishimoto, S., Nishida, S.: Lining up brains via a common representational space. Trends Cogn. Sci. 20(8), 565–567 (2016)
Chen, P.H.: Multi-view Representation Learning with Applications to Functional Neuroimaging Data. Ph.D. thesis, Princeton University (2017)
Bellec, P., Chu, C., Chouinard-Decorte, F., Benhajali, Y., Margulies, D.S., Craddock, R.C.: The neuro bureau adhd-200 preprocessed repository. Neuroimage 144, 275–286 (2017)
Zhu, J., Cao, J.: Group analysis by visualized distributional representation for resting-state functional brain connectivity. In: Proceedings of the 14th International Conference on Semantics, Knowledge and Grids. IEEE (2018)
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Zhu, J., Cao, J. (2018). Distributional Representation for Resting-State Functional Brain Connectivity Analysis. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_20
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DOI: https://doi.org/10.1007/978-3-030-05587-5_20
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