A Deep Learning Framework for Noise Component Detection from Resting-State Functional MRI-Reference-Cited by-同舟云学术

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A Deep Learning Framework for Noise Component Detection from Resting-State Functional MRI

Published:2019 Issue: Volume: Page:754-762
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Kam Tae-Eui, ,Wen Xuyun,Jin Bing,Jiao Zhicheng,Hsu Li-Ming,Zhou Zhen,Liu Yujie,Yamashita Koji,Hung Sheng-Che,Lin Weili,Zhang Han,Shen Dinggang

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-32248-9_84

Reference13 articles.

1. Caballero-Gaudes, C., Reynolds, R.C.: Methods for cleaning the BOLD fMRI signal. NeuroImage 154, 128–149 (2017)

2. Kundu, P., Voon, V., Balchandani, P., Lombardo, M.V., Poser, B.A., Bandettini, P.A.: Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage 154, 59–80 (2017)

3. McKeown, M.J., et al.: Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 60(3), 160–188 (1998)

4. Griffanti, L., et al.: Hand classification of fMRI ICA noise components. NeuroImage 154, 188–205 (2017)

5. Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith, S.M.: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014)

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