Abstract
An important goal of cognitive brain imaging studies is to model the functional organization of the brain; yet there exists currently no functional brain atlas built from existing data. One of the main roadblocks to the creation of such an atlas is the functional variability that is observed in subjects performing the same task; this variability goes far beyond anatomical variability in brain shape and size. Function-based alignment procedures have recently been proposed in order to improve the correspondence of activation patterns across individuals. However, the corresponding computational solutions are costly and not well-principled. Here, we propose a new framework based on optimal transport theory to create such a template. We leverage entropic smoothing as an efficient means to create brain templates without losing fine-grain structural information; it is implemented in a computationally efficient way. We evaluate our approach on rich multi-subject, multi-contrasts datasets. These experiments demonstrate that the template-based inference procedure improves the transfer of information across individuals with respect to state of the art methods.
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References
Klein, A., et al.: Evaluation of 14 non linear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786–802 (2009)
Thirion, B.: Functional neuroimaging group studies. In: Thompson, W., Ombao, H., Lindquist, M., Aston, J. (eds.) Handbook of Neuroimaging Data Analysis, pp. 335–354. Chapman and Hall and CRC, Boca Raton (2016). Chap 12
Fedorenko, E., Behr, M.K., Kanwisher, N.: Functional specificity for high-level linguistic processing in the human brain. Proc. Nat. Acad. Sci. 108(39), 16428–16433 (2011)
Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)
Pinho, A.L., et al.: Individual brain charting, a high-resolution fMRI dataset for cognitive mapping. Sci. Data 5, 180105 (2018)
Sabuncu, M.R., Singer, B.D., Conroy, B., Bryan, R.E., Ramadge, P.J., Haxby, J.V.: Function-based intersubject alignment of human cortical anatomy. Cereb. Cortex 20, 130 (2010)
Nenning, K.H., Liu, H., Ghosh, S.S., Sabuncu, M.R., Schwartz, E., Langs, G.: Diffeomorphic functional brain surface alignment: functional demons. NeuroImage 156, 456–465 (2017)
Haxby, J.V., et al.: A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2), 404–416 (2011)
Guntupalli, J.S., Hanke, M., Halchenko, Y.O., Connolly, A.C., Ramadge, P.J., Haxby, J.V.: A model of representational spaces in human cortex. Cereb. Cortex 26(6), 2919–2934 (2016)
Langs, G., et al.: Learning an Atlas of a cognitive process in its functional geometry. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 135–146. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22092-0_12
Langs, G., et al.: Identifying shared brain networks in individuals by decoupling functional and anatomical variability. Cereb. Cortex 26(10), 4004–4014 (2016)
Güçlü, U., van Gerven, M.A.J.: Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35(27), 10005–10014 (2015)
Peyré, G., Cuturi, M.: Computational optimal transport. arXiv e-prints, page arXiv:1803.00567, March 2018
Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)
Kantorovitch, L.: On the translocation of masses. Manag. Sci. 5(1), 1–4 (1958)
Gramfort, A., Peyré, G., Cuturi, M.: Fast optimal transport averaging of neuroimaging data. CoRR, abs/1503.08596 (2015)
Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, pp. 2292–2300 (2013)
Van Essen, D.C., et al.: The WU-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Hoyos-Idrobo, A., Varoquaux, G., Thirion, B.: Towards a faster randomized parcellation based inference. In: PRNI 2017–7th International Workshop on Pattern Recognition in NeuroImaging, Toronto, Canada, June 2017
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2).
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Bazeille, T., Richard, H., Janati, H., Thirion, B. (2019). Local Optimal Transport for Functional Brain Template Estimation. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_18
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DOI: https://doi.org/10.1007/978-3-030-20351-1_18
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