Journal article
A Graph Signal Processing Perspective on Functional Brain Imaging
Proceedings of the IEEE, v 106(5), pp 868-885
01 May 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- A Graph Signal Processing Perspective on Functional Brain Imaging
- Creators
- Weiyu Huang - University of Pennsylvania Health SystemThomas A. W. Bolton - École Polytechnique Fédérale de LausanneJohn D. Medaglia - Drexel UniversityDanielle S. Bassett - Hospital of the University of PennsylvaniaAlejandro Ribeiro - University of Pennsylvania Health SystemDimitri Van De Ville - École Polytechnique Fédérale de Lausanne
- Publication Details
- Proceedings of the IEEE, v 106(5), pp 868-885
- Publisher
- IEEE
- Number of pages
- 18
- Grant note
- John D. and Catherine T. Mac Arthur Foundation R01-DC014960 / National Institute of Dental and Craniofacial Research (NIDCR); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Dental & Craniofacial Research (NIDCR) ISI Foundation W911NF1710438 / Army Research Office (ARO) Bertarelli Foundation Center for Biomedical Imaging (CIBM) Perelman School of Medicine Alfred P. Sloan Foundation R01EB01W5853; DP5-0D021352 / National Institutes of Health (NIH); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 1543656 / National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000433349100007
- Scopus ID
- 2-s2.0-85043376998
- Other Identifier
- 991019168752304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic