A Unified Multi-Modality Fusion Framework for Deep Spatio-Spectral-Temporal Feature Learning in Resting-State fMRI Denoising-Reference-Cited by-同舟云学术

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A Unified Multi-Modality Fusion Framework for Deep Spatio-Spectral-Temporal Feature Learning in Resting-State fMRI Denoising

Published:2024-04 Issue:4 Volume:28 Page:2067-2078
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Lim Minjoo¹^ORCID,Heo Keun-Soo¹^ORCID,Kim Jun-Mo¹^ORCID,Kang Bogyeong¹^ORCID,Lin Weili²^ORCID,Zhang Han³^ORCID,Shen Dinggang³^ORCID,Kam Tae-Eui¹^ORCID

Affiliation:

1. Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea

2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

3. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

Funder

Institute of Information and communications Technology Planning and Evaluation

Korea University

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10491283/10409518.pdf?arnumber=10409518

Reference50 articles.

1. Improved Estimation of the Number of Independent Components for Functional Magnetic Resonance Data by a Whitening Filter

2. Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis

3. Resting-state fMRI in the Human Connectome Project

4. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study

5. fMRI hemodynamic response function estimation in autoregressive noise by avoiding the drift

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