A Graph Signal Processing Perspective on Functional Brain Imaging

Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems le...

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Bibliographic Details
Published in:Proceedings of the IEEE 2018-05, Vol.106 (5), p.868-885
Main Authors: Huang, Weiyu, Bolton, Thomas A. W., Medaglia, John D., Bassett, Danielle S., Ribeiro, Alejandro, Van De Ville, Dimitri
Format: Article
Language:English
Subjects:
Adaptive systems
Brain
Brain modeling
Brain research
Data acquisition
Extraterrestrial measurements
functional MRI
graph signal processing (GSP)
Graph theory
Graphical representations
Graphs
Magnetic resonance imaging
Medical imaging
network models
Neuroimaging
Neurology
Neuroscience
Signal detection
Signal processing
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Summary:Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems level. Graph representations are versatile models where nodes are associated to brain regions and edges to structural or functional connections. Structural graphs model neural pathways in white matter, which are the anatomical backbone between regions. Functional graphs are built based on functional connectivity, which is a pairwise measure of statistical interdependency between pairs of regional activity traces. Therefore, 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 recorded at the nodes of the graph are studied atop the underlying graph structure. An increasing number of fundamental operations have been generalized to the graph setting, allowing to analyze the signals from a new viewpoint. Here, we review GSP for brain imaging data and discuss their potential to integrate brain structure, contained in the graph itself, with brain function, residing in the graph signals. We review how brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure. We also derive other operations such as surrogate data generation or decompositions informed by cognitive systems. In sum, GSP offers a novel framework for the analysis of brain imaging data.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2018.2798928