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Title: Inter-subject neural code converter for visual image representation.
Authors: Yamada, Kentaro
Miyawaki, Yoichi
Kamitani, Yukiyasu  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-9300-8268 (unconfirmed)
Author's alias: 神谷, 之康
Issue Date: 2-Apr-2015
Publisher: Elsevier Inc.
Journal title: NeuroImage
Volume: 113
Start page: 289
End page: 297
Abstract: Brain activity patterns differ from person to person, even for an identical stimulus. In functional brain mapping studies, it is important to align brain activity patterns between subjects for group statistical analyses. While anatomical templates are widely used for inter-subject alignment in functional magnetic resonance imaging (fMRI) studies, they are not sufficient to identify the mapping between voxel-level functional responses representing specific mental contents. Recent work has suggested that statistical learning methods could be used to transform individual brain activity patterns into a common space while preserving representational contents. Here, we propose a flexible method for functional alignment, "neural code converter, " which converts one subject's brain activity pattern into another's representing the same content. The neural code converter was designed to learn statistical relationships between fMRI activity patterns of paired subjects obtained while they saw an identical series of stimuli. It predicts the signal intensity of individual voxels of one subject from a pattern of multiple voxels of the other subject. To test this method, we used fMRI activity patterns measured while subjects observed visual images consisting of random and structured patches. We show that fMRI activity patterns for visual images not used for training the converter could be predicted from those of another subject where brain activity was recorded for the same stimuli. This confirms that visual images can be accurately reconstructed from the predicted activity patterns alone. Furthermore, we show that a classifier trained only on predicted fMRI activity patterns could accurately classify measured fMRI activity patterns. These results demonstrate that the neural code converter can translate neural codes between subjects while preserving contents related to visual images. While this method is useful for functional alignment and decoding, it may also provide a basis for brain-to-brain communication using the converted pattern for designing brain stimulation.
Rights: © 2015 Elsevier Inc. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/. NOTICE: this is the author's version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NeuroImage, Volume 113, June 2015, Pages 289–297, doi:10.1016/j.neuroimage.2015.03.059.
This is not the published version. Please cite only the published version.
この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/198901
DOI(Published Version): 10.1016/j.neuroimage.2015.03.059
PubMed ID: 25842289
Appears in Collections:Journal Articles

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