Proceedings chapter

Graph Signal Processing of Human Brain Imaging Data

Presented at Calgary, AB (Canada), 15-20 April 2018
Publication date2018

Modern neuroimaging techniques offer disctinct views on brain structure and function. Data acquired using these techniques can be analyzed in terms of its network structure to identify organizing principles at the systems level. Graph representations are flexible frameworks where nodes are related to brain regions and edges to structural or functional links. Most research to date has focused on analyzing these graphs reflecting structure or function. Graph signal processing (GSP) is an emerging area of research where signals at the nodes are studied atop the underlying graph structure. Here, we review GSP tools for brain imaging data and discuss their potential to integrate brain structure with function. We discuss how brain activity can be meaningfully filtered. We also derive surrogate data as a null model to test significance for graph signals. We review that individuals with less concentration on graph high frequency could switch attention faster.

  • Brain
  • Magnetic resonance imaging
  • Eigenvalues and eigenfunctions
  • Laplace equations
  • Signal processing
  • Neuroimaging
  • Brain
  • Neuroimaging
  • Network models
  • Graph signal processing
  • Functional MRI
  • Structural MRI
Citation (ISO format)
HUANG, Weiyu et al. Graph Signal Processing of Human Brain Imaging Data. In: Proceedings of the IEEE 42th International Conference on Acoustics, Speech, and Signal Processing. Calgary, AB (Canada). [s.l.] : IEEE, 2018. p. 980–984. doi: 10.1109/ICASSP.2018.8461314
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Proceedings chapter (Published version)

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