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Proceedings Paper

Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI
Author(s): Junqi Wang; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-ping Wang
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Paper Abstract

In recent decades, the graph signal processing techniques have demonstrated their effectiveness in tackling neuroimaging problems. However, most of these tools rely on predefined graphs to conduct spectral analysis, which can not be always satisfied due to the complexity of the brain structure. We, therefore, propose a data-driven signal processing framework (or namely, graph Laplacian learning based Fourier transform) that can effectively estimate the graph structure from the data and conduct Fourier transform afterward to analyze their spectral properties. We validate the proposed method on a large real dataset and the experimental results demonstrate its superiority over traditional methods.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171G (28 February 2020); doi: 10.1117/12.2549378
Show Author Affiliations
Junqi Wang, Tulane Univ. (United States)
Julia M. Stephen, The Mind Research Network (United States)
Tony W. Wilson, Univ. of Nebraska Medical Ctr. (United States)
Vince D. Calhoun, The Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Yu-ping Wang, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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