Monti, RP;
Lorenz, R;
Hellyer, P;
Leech, R;
Anagnostopoulos, C;
Montana, G;
(2017)
Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods.
Frontiers in Computational Neuroscience
, 11
, Article 14. 10.3389/fncom.2017.00014.
Preview |
Text
fncom-11-00014.pdf - Published Version Download (3MB) | Preview |
Abstract
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.
| Type: | Article |
|---|---|
| Title: | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fncom.2017.00014 |
| Publisher version: | http://doi.org/10.3389/fncom.2017.00014 |
| Language: | English |
| Additional information: | Copyright © 2017 Monti, Lorenz, Hellyer, Leech, Anagnostopoulos and Montana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | brain decoding, dynamic networks, functional connectivity, graph embedding, visualization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10045386 |
Archive Staff Only
 |
View Item |
