Author
Listed:
- Yuxiang Yan
(Massachusetts General Hospital, Harvard Medical School
Tsinghua University)
- Louisa Dahmani
(Massachusetts General Hospital, Harvard Medical School
Zhengzhou University People Hospital & Henan Provincial People’s Hospital)
- Jianxun Ren
(Massachusetts General Hospital, Harvard Medical School
Tsinghua University)
- Lunhao Shen
(Massachusetts General Hospital, Harvard Medical School
Tsinghua University)
- Xiaolong Peng
(Massachusetts General Hospital, Harvard Medical School)
- Ruiqi Wang
(Massachusetts General Hospital, Harvard Medical School)
- Changgeng He
(Massachusetts General Hospital, Harvard Medical School
Tsinghua University)
- Changqing Jiang
(Tsinghua University)
- Chen Gong
(Tsinghua University)
- Ye Tian
(Tsinghua University)
- Jianguo Zhang
(Capital Medical University)
- Yi Guo
(Peking Union Medical College Hospital)
- Yuanxiang Lin
(First Affiliated Hospital of Fujian Medical University)
- Shijun Li
(Massachusetts General Hospital, Harvard Medical School)
- Meiyun Wang
(Zhengzhou University People Hospital & Henan Provincial People’s Hospital)
- Luming Li
(Tsinghua University
Capital Medical University)
- Bo Hong
(Tsinghua University)
- Hesheng Liu
(Massachusetts General Hospital, Harvard Medical School
Capital Medical University
Medical University of South Carolina)
Abstract
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual’s own functional brain organization.
Suggested Citation
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 & Meiy, 2020.
"Reconstructing lost BOLD signal in individual participants using deep machine learning,"
Nature Communications, Nature, vol. 11(1), pages 1-13, December.
Handle:
RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18823-9
DOI: 10.1038/s41467-020-18823-9
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