Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts.

Algorithms Brain / diagnostic imaging Brain Mapping Computer Simulation Magnetic Resonance Imaging
Complex-valued fMRI data Phase denoising Real and imaginary parts Selection of components Sparse representation Spatiotemporal concatenation

Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
01 03 2021
Historique:
received: 10 07 2020
revised: 04 12 2020
accepted: 18 12 2020
pubmed: 2 1 2021
medline: 1 7 2021
entrez: 1 1 2021
Statut: ppublish

Résumé

Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data. We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model. This model not only enables flexible usage of existing real-valued sparse representation algorithms but also allows for the reconstruction of complex-valued spatial and temporal components from their real and imaginary estimates. We propose to select components from both real and imaginary estimates to reconstruct the complex-valued component, using phase denoising to recover weak brain activity from high-amplitude noise. The K-SVD algorithm was used to obtain a sparse representation within the spatiotemporal concatenation model. The results from simulated and experimental complex-valued fMRI datasets validated the efficacy of our method. Compared to a magnitude-only approach, the proposed method detected additional voxels manifest within several specific regions expected to be involved but likely missing from the magnitude-only data, e.g., in the anterior cingulate cortex region. Simulation results showed that the additional voxels were accurate and unique information from the phase data. Compared to a complex-valued dictionary learning algorithm, our method exhibited lower noise for both magnitude and phase maps. The proposed method is robust to noise and effective for identifying a sparse representation of the natively complex-valued fMRI data.

Sections du résumé

BACKGROUND
Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data.
NEW METHODS
We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model. This model not only enables flexible usage of existing real-valued sparse representation algorithms but also allows for the reconstruction of complex-valued spatial and temporal components from their real and imaginary estimates. We propose to select components from both real and imaginary estimates to reconstruct the complex-valued component, using phase denoising to recover weak brain activity from high-amplitude noise.
RESULTS
The K-SVD algorithm was used to obtain a sparse representation within the spatiotemporal concatenation model. The results from simulated and experimental complex-valued fMRI datasets validated the efficacy of our method.
COMPARISON WITH EXISTING METHODS
Compared to a magnitude-only approach, the proposed method detected additional voxels manifest within several specific regions expected to be involved but likely missing from the magnitude-only data, e.g., in the anterior cingulate cortex region. Simulation results showed that the additional voxels were accurate and unique information from the phase data. Compared to a complex-valued dictionary learning algorithm, our method exhibited lower noise for both magnitude and phase maps.
CONCLUSIONS
The proposed method is robust to noise and effective for identifying a sparse representation of the natively complex-valued fMRI data.

Identifiants

DOI: 10.1016/j.jneumeth.2020.109047 PMID: 33385421
pubmed: 33385421
pii: S0165-0270(20)30470-2
doi: 10.1016/j.jneumeth.2020.109047
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

109047

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Chao-Ying Zhang (CY)

School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

Qiu-Hua Lin (QH)

School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. Electronic address: qhlin@dlut.edu.cn.

Li-Dan Kuang (LD)

School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.

Wei-Xing Li (WX)

School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

Xiao-Feng Gong (XF)

School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

Vince D Calhoun (VD)

Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Sparse representation of complex-valued fMRI...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Sparse representation of complex-valued fMRI...
questionsmedicales.fr › Techniques d'investigation › Neuroimagerie › Neuroimagerie fonctionnelle › Cartographie cérébrale › Sparse representation of complex-valued fMRI...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Simulation numérique › Sparse representation of complex-valued fMRI...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Sparse representation of complex-valued fMRI...