Graph Signal Processing of Human Brain Imaging Data
Weiyu Huang, Thomas William Arthur Bolton, John D. Medaglia, Danielle S. Bassett, Alejandro Ribeiro, Dimitri Van De Ville
Abstract
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.
BibTeX
@inproceedings{icassp2018_graphsignalproce,
title = {Graph Signal Processing of Human Brain Imaging Data},
author = {Weiyu Huang and Thomas William Arthur Bolton and John D. Medaglia and Danielle S. Bassett and Alejandro Ribeiro and Dimitri Van De Ville},
booktitle = {ICASSP 2018},
year = {2018}
}