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Title: Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging
Authors: Fuchigami, Takuya
Shikauchi, Yumi
Nakae, Ken  kyouindb  KAKEN_id
Shikauchi, Manabu
Ogawa, Takeshi
Ishii, Shin  kyouindb  KAKEN_id
Author's alias: 渕上, 卓也
鹿内, 友美
中江, 健
鹿内, 学
小川, 剛史
石井, 信
Keywords: Network models
Neural decoding
Issue Date: 17-Aug-2018
Publisher: Springer Nature
Journal title: Scientific Reports
Volume: 8
Thesis number: 12342
Abstract: Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding. Our registration method based on diffusion tensor imaging (DTI) transferred functional maps of each individual to a common anatomical space, where a decoding analysis of multi-voxel patterns was performed. The decoder trained on functional maps from other individuals in the common space showed a transfer decoding accuracy comparable to that of an individual decoder trained on single-subject functional maps. The DTI-based registration allowed more precise transformation of gray matter boundaries than a well-established T1-based method. These results suggest that the DTI-based registration is a promising tool for standardization of the brain functions, and moreover, will allow us to perform ‘zero-shot’ learning of decoders which is profitable in brain machine interface scenes.
Rights: © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
URI: http://hdl.handle.net/2433/242944
DOI(Published Version): 10.1038/s41598-018-30676-3
PubMed ID: 30120378
Appears in Collections:Journal Articles

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