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タイトル: Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging
著者: Fuchigami, Takuya
Shikauchi, Yumi
Nakae, Ken  KAKEN_id
Shikauchi, Manabu
Ogawa, Takeshi
Ishii, Shin  KAKEN_id
著者名の別形: 渕上, 卓也
鹿内, 友美
中江, 健
鹿内, 学
小川, 剛史
石井, 信
キーワード: Network models
Neural decoding
発行日: 17-Aug-2018
出版者: Springer Nature
誌名: Scientific Reports
巻: 8
論文番号: 12342
抄録: 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.
著作権等: © 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(出版社版): 10.1038/s41598-018-30676-3
PubMed ID: 30120378
出現コレクション:学術雑誌掲載論文等

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