Abstract
The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has been proved quite effective in exploiting their multiway nature. The advantages of tensorial methods over matrix-based approaches have also been demonstrated in the context of functional magnetic resonance imaging (fMRI) data analysis. However, such methods can become ineffective in demanding scenarios, involving, e.g., strong noise and/or significant overlapping of activated regions. This paper aims at investigating the possible gains that can be obtained from a better exploitation of the spatial dimension, through a higher (than 3)-order tensor modeling of the fMRI signals. In this context, a higher-order Block Term Decomposition (BTD) is applied, for the first time in fMRI analysis. Its effectiveness in handling strong instances of noise is demonstrated via extensive simulation results.
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Acknowledgments
The authors would like to thank Profs. A. Stegeman and N. Helwig for providing the datasets used in [13] and [14], respectively, and Prof. S. Van Huffel for her critical comments on earlier version of this paper. Constructive comments from the reviewers are also gratefully acknowledged. This research has been funded by the European Union’s Seventh Framework Programme (H2020-MSCA-ITN-2014) under grant agreement No. 642685 MacSeNet.
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Chatzichristos, C., Kofidis, E., Kopsinis, Y., Moreno, M.M., Theodoridis, S. (2017). Higher-Order Block Term Decomposition for Spatially Folded fMRI Data. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_1
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