Automatic identification of resting state networks: An extended version of multiple template-matching

ICA; RSN; Automation; Bioinformatics; Brain; Independent component analysis; Information science; Magnetic resonance imaging; Artifact detection; Automatic identification; Functional magnetic resonance imaging; Independent component analysis(ICA); Multiple template matching; Pathological conditions; Resting state; Template matching

Abstract :

[en] 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. © 2015 SPIE.

Disciplines :

Neurosciences & behavior

Author, co-author :

Guaje, Javier; Computer Science Department, Universidad Nacional de Colombia, Bogotia, Colombia

Molina, Juan; Computer Science Department, Universidad Central de Colombia, Bogotia, Colombia

Rudas, Jorge; Computer Science Department, Universidad Nacional de Colombia, Bogotia, Colombia

Demertzi, Athina ; Université de Liège - ULiège > Centre de recherches du cyclotron

Heine, Lizette ; Université de Liège - ULiège > Centre de recherches du cyclotron

TSHIBANDA, Luaba ; Centre Hospitalier Universitaire de Liège - CHU > Service médical de radiodiagnostic

Soddu, Andrea ; Université de Liège - ULiège > Centre de recherches du cyclotron

Laureys, Steven ; Université de Liège - ULiège > GIGA : Coma Group

Gómez, Francisco; Computer Science Department, Universidad Central de Colombia, Bogotia, Colombia

Language :

English

Title :

Automatic identification of resting state networks: An extended version of multiple template-matching

Publication date :

2015

Event name :

11th International Symposium on Medical Information Processing and Analysis, SIPAIM 2015

Event date :

17 November 2015 through 19 November 2015

Audience :

International

Journal title :

Proceedings of SPIE: The International Society for Optical Engineering

ISSN :

0277-786X

eISSN :

1996-756X

Publisher :

International Society for Optical Engineering, Bellingham, United States - Washington

Volume :

9681

Pages :

96810V

Peer reviewed :

Peer reviewed

Name of the research project :

Project “Caracterizacion de interacciones entre redes de descanso en pacientes con desordenes de conciencia”

Funders :

UC - Universidad Central de Colombia [CO]
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
JSMF - James S McDonnell Foundation [US-MO] [US-MO]
ASE - Agence Spatiale Européenne [FR]
MSF - Mind Science Foundation [US-TX] [US-TX]
FWB - Fédération Wallonie-Bruxelles [BE]
UET - Université Européenne du Travail [BE]
FERB - Fondazione Europea Ricerca Biomedica [IT]
CHU Liège - Centre Hospitalier Universitaire de Liège [BE]
CE - Commission Européenne [BE]

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since 19 May 2020

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