Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging
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.
Journal
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- Scientific Reports
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Scientific Reports 8 2018-08-17
Springer Nature
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Keywords
Details 詳細情報について
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- CRID
- 1050001338388074624
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- NII Article ID
- 120006652973
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- ISSN
- 20452322
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- HANDLE
- 2433/242944
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- IRDB
- Crossref
- CiNii Articles
- KAKEN