Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI

Junqi Wang; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-ping Wang

doi:10.1117/12.2549378

28 February 2020 Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI

Junqi Wang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-ping Wang

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171G (2020) https://doi.org/10.1117/12.2549378
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Junqi Wang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-ping Wang "Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171G (28 February 2020); https://doi.org/10.1117/12.2549378

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