Share Email Print
cover

Proceedings Paper

Automatic identification of resting state networks: an extended version of multiple template-matching
Author(s): Javier Guaje; Juan Molina; Jorge Rudas; Athena Demertzi; Lizette Heine; Luaba Tshibanda; Andrea Soddu; Steven Laureys; Francisco Gómez
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Functional magnetic resonance imaging in resting state (fMRI-RS) constitutes an informative protocol to investigate several pathological and pharmacological conditions. A common approach to study this data source is through the analysis of changes in the so called resting state networks (RSNs). These networks correspond to well-defined functional entities that have been associated to different low and high brain order functions. RSNs may be characterized by using Independent Component Analysis (ICA). ICA provides a decomposition of the fMRI-RS signal into sources of brain activity, but it lacks of information about the nature of the signal, i.e., if the source is artifactual or not. Recently, a multiple template-matching (MTM) approach was proposed to automatically recognize RSNs in a set of Independent Components (ICs). This method provides valuable information to assess subjects at individual level. Nevertheless, it lacks of a mechanism to quantify how much certainty there is about the existence/absence of each network. This information may be important for the assessment of patients with severely damaged brains, in which RSNs may be greatly affected as a result of the pathological condition. In this work we propose a set of changes to the original MTM that improves the RSNs recognition task and also extends the functionality of the method. The key points of this improvement is a standardization strategy and a modification of method's constraints that adds flexibility to the approach. Additionally, we also introduce an analysis to the trustworthiness measurement of each RSN obtained by using template-matching approach. This analysis consists of a thresholding strategy applied over the computed Goodness-of-Fit (GOF) between the set of templates and the ICs. The proposed method was validated on 2 two independent studies (Baltimore, 23 healthy subjects and Liege, 27 healthy subjects) with different configurations of MTM. Results suggest that the method will provide complementary information for characterization of RSNs at individual level.

Paper Details

Date Published: 22 December 2015
PDF: 10 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810V (22 December 2015); doi: 10.1117/12.2211530
Show Author Affiliations
Javier Guaje, Univ. Nacional de Colombia (Colombia)
Juan Molina, Univ. Central de Colombia (Colombia)
Jorge Rudas, Univ. Nacional de Colombia (Colombia)
Athena Demertzi, Univ. de Liège (Belgium)
Lizette Heine, Univ. de Liège (Belgium)
Luaba Tshibanda, Univ. de Liège (Belgium)
Andrea Soddu, Western Univ. (Canada)
Steven Laureys, Univ. de Liège (Belgium)
Francisco Gómez, Univ. Central de Colombia (Colombia)


Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

© SPIE. Terms of Use
Back to Top