Reconstructing lost BOLD signal in individual participants using deep machine learning

Signal loss in blood oxygen level‐dependent (BOLD) fMRI can lead to misinterpretation of findings. The authors trained a deep learning model to reconstruct compromised BOLD signal in datasets from healthy participants and in patients whose scans suffered signal loss due to intracortical electrodes....

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Main Authors:	Yuxiang Yan, Louisa Dahmani, Jianxun Ren, Lunhao Shen, Xiaolong Peng, Ruiqi Wang, Changgeng He, Changqing Jiang, Chen Gong, Ye Tian, Jianguo Zhang, Yi Guo, Yuanxiang Lin, Shijun Li, Meiyun Wang, Luming Li, Bo Hong, Hesheng Liu
Format:	article
Language:	EN
Published:	Nature Portfolio 2020
Subjects:	Science Q
Online Access:	https://doaj.org/article/4329acd981f14858b29114ccd5cf4a4c
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spelling	oai:doaj.org-article:4329acd981f14858b29114ccd5cf4a4c2021-12-02T18:37:29ZReconstructing lost BOLD signal in individual participants using deep machine learning10.1038/s41467-020-18823-92041-1723https://doaj.org/article/4329acd981f14858b29114ccd5cf4a4c2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18823-9https://doaj.org/toc/2041-1723Signal loss in blood oxygen level‐dependent (BOLD) fMRI can lead to misinterpretation of findings. The authors trained a deep learning model to reconstruct compromised BOLD signal in datasets from healthy participants and in patients whose scans suffered signal loss due to intracortical electrodes.Yuxiang YanLouisa DahmaniJianxun RenLunhao ShenXiaolong PengRuiqi WangChanggeng HeChangqing JiangChen GongYe TianJianguo ZhangYi GuoYuanxiang LinShijun LiMeiyun WangLuming LiBo HongHesheng LiuNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yuxiang Yan Louisa Dahmani Jianxun Ren Lunhao Shen Xiaolong Peng Ruiqi Wang Changgeng He Changqing Jiang Chen Gong Ye Tian Jianguo Zhang Yi Guo Yuanxiang Lin Shijun Li Meiyun Wang Luming Li Bo Hong Hesheng Liu Reconstructing lost BOLD signal in individual participants using deep machine learning
description	Signal loss in blood oxygen level‐dependent (BOLD) fMRI can lead to misinterpretation of findings. The authors trained a deep learning model to reconstruct compromised BOLD signal in datasets from healthy participants and in patients whose scans suffered signal loss due to intracortical electrodes.
format	article
author	Yuxiang Yan Louisa Dahmani Jianxun Ren Lunhao Shen Xiaolong Peng Ruiqi Wang Changgeng He Changqing Jiang Chen Gong Ye Tian Jianguo Zhang Yi Guo Yuanxiang Lin Shijun Li Meiyun Wang Luming Li Bo Hong Hesheng Liu
author_facet	Yuxiang Yan Louisa Dahmani Jianxun Ren Lunhao Shen Xiaolong Peng Ruiqi Wang Changgeng He Changqing Jiang Chen Gong Ye Tian Jianguo Zhang Yi Guo Yuanxiang Lin Shijun Li Meiyun Wang Luming Li Bo Hong Hesheng Liu
author_sort	Yuxiang Yan
title	Reconstructing lost BOLD signal in individual participants using deep machine learning
title_short	Reconstructing lost BOLD signal in individual participants using deep machine learning
title_full	Reconstructing lost BOLD signal in individual participants using deep machine learning
title_fullStr	Reconstructing lost BOLD signal in individual participants using deep machine learning
title_full_unstemmed	Reconstructing lost BOLD signal in individual participants using deep machine learning
title_sort	reconstructing lost bold signal in individual participants using deep machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4329acd981f14858b29114ccd5cf4a4c
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Reconstructing lost BOLD signal in individual participants using deep machine learning

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