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:	English
Published:	Nature Publishing Group 2020-10-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-18823-9

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spelling	doaj-4329acd981f14858b29114ccd5cf4a4c2021-10-10T11:47:32ZengNature Publishing GroupNature Communications2041-17232020-10-0111111310.1038/s41467-020-18823-9Reconstructing lost BOLD signal in individual participants using deep machine learningYuxiang Yan0Louisa Dahmani1Jianxun Ren2Lunhao Shen3Xiaolong Peng4Ruiqi Wang5Changgeng He6Changqing Jiang7Chen Gong8Ye Tian9Jianguo Zhang10Yi Guo11Yuanxiang Lin12Shijun Li13Meiyun Wang14Luming Li15Bo Hong16Hesheng Liu17Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolNational Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua UniversityNational Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua UniversityNational Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua UniversityDepartment of Neurosurgery, Tiantan Hospital, Capital Medical UniversityDepartment of Neurosurgery, Peking Union Medical College HospitalDepartment of Neurosurgery, First Affiliated Hospital of Fujian Medical UniversityAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Radiology, Zhengzhou University People Hospital & Henan Provincial People’s HospitalNational Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua UniversityDepartment of Biomedical Engineering, School of Medicine, Tsinghua UniversityAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolSignal 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.https://doi.org/10.1038/s41467-020-18823-9
collection	DOAJ
language	English
format	Article
sources	DOAJ
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
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-10-01
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.
url	https://doi.org/10.1038/s41467-020-18823-9
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Reconstructing lost BOLD signal in individual participants using deep machine learning

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