Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2012
Autor:innen
Braun, Urs
Plichta, Michael M.
Esslinger, Christine
Sauer, Carina
Haddad, Leila
Grimm, Oliver
Mohnke, Sebastian
Heinz, Andreas
Erk, Susanne
et al.
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
NeuroImage. 2012, 59(2), pp. 1404-1412. ISSN 1053-8119. eISSN 1095-9572. Available under: doi: 10.1016/j.neuroimage.2011.08.044
Zusammenfassung

Characterizing the brain connectome using neuroimaging data and measures derived from graph theory emerged as a new approach that has been applied to brain maturation, cognitive function and neuropsychiatric disorders. For a broad application of this method especially for clinical populations and longitudinal studies, the reliability of this approach and its robustness to confounding factors need to be explored. Here we investigated test–retest reliability of graph metrics of functional networks derived from functional magnetic resonance imaging (fMRI) recorded in 33 healthy subjects during rest. We constructed undirected networks based on the Anatomic-Automatic-Labeling (AAL) atlas template and calculated several commonly used measures from the field of graph theory, focusing on the influence of different strategies for confound correction. For each subject, method and session we computed the following graph metrics: clustering coefficient, characteristic path length, local and global efficiency, assortativity, modularity, hierarchy and the small-worldness scalar. Reliability of each graph metric was assessed using the intraclass correlation coefficient (ICC).

Overall ICCs ranged from low to high (0 to 0.763) depending on the method and metric. Methodologically, the use of a broader frequency band (0.008-0.15 Hz) yielded highest reliability indices (mean ICC=0.484), followed by the use of global regression (mean ICC=0.399). In general, the second order metrics (small-worldness, hierarchy, assortativity) studied here, tended to be more robust than first order metrics.

In conclusion, our study provides methodological recommendations which allow the computation of sufficiently robust markers of network organization using graph metrics derived from fMRI data at rest.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
150 Psychologie
Schlagwörter
Graph theory, Network analysis, fMRI, Resting-state functional connectivity, Test–retest reliability
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690BRAUN, Urs, Michael M. PLICHTA, Christine ESSLINGER, Carina SAUER, Leila HADDAD, Oliver GRIMM, Daniela MIER, Sebastian MOHNKE, Andreas HEINZ, Susanne ERK, 2012. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. In: NeuroImage. 2012, 59(2), pp. 1404-1412. ISSN 1053-8119. eISSN 1095-9572. Available under: doi: 10.1016/j.neuroimage.2011.08.044
BibTex
@article{Braun2012-01-16Testr-45676,
  year={2012},
  doi={10.1016/j.neuroimage.2011.08.044},
  title={Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures},
  number={2},
  volume={59},
  issn={1053-8119},
  journal={NeuroImage},
  pages={1404--1412},
  author={Braun, Urs and Plichta, Michael M. and Esslinger, Christine and Sauer, Carina and Haddad, Leila and Grimm, Oliver and Mier, Daniela and Mohnke, Sebastian and Heinz, Andreas and Erk, Susanne}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/45676">
    <dc:contributor>Haddad, Leila</dc:contributor>
    <dc:creator>Sauer, Carina</dc:creator>
    <dc:creator>Grimm, Oliver</dc:creator>
    <dc:contributor>Esslinger, Christine</dc:contributor>
    <dc:creator>Haddad, Leila</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-04-17T13:26:36Z</dc:date>
    <dc:creator>Esslinger, Christine</dc:creator>
    <dc:creator>Erk, Susanne</dc:creator>
    <dc:language>eng</dc:language>
    <dc:creator>Heinz, Andreas</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/45676"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Sauer, Carina</dc:contributor>
    <dc:contributor>Mier, Daniela</dc:contributor>
    <dc:contributor>Braun, Urs</dc:contributor>
    <dc:creator>Mohnke, Sebastian</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-04-17T13:26:36Z</dcterms:available>
    <dc:contributor>Heinz, Andreas</dc:contributor>
    <dc:creator>Mier, Daniela</dc:creator>
    <dc:creator>Braun, Urs</dc:creator>
    <dc:contributor>Grimm, Oliver</dc:contributor>
    <dc:creator>Plichta, Michael M.</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:issued>2012-01-16</dcterms:issued>
    <dcterms:title>Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures</dcterms:title>
    <dc:contributor>Erk, Susanne</dc:contributor>
    <dc:contributor>Plichta, Michael M.</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Mohnke, Sebastian</dc:contributor>
    <dcterms:abstract xml:lang="eng">Characterizing the brain connectome using neuroimaging data and measures derived from graph theory emerged as a new approach that has been applied to brain maturation, cognitive function and neuropsychiatric disorders. For a broad application of this method especially for clinical populations and longitudinal studies, the reliability of this approach and its robustness to confounding factors need to be explored. Here we investigated test–retest reliability of graph metrics of functional networks derived from functional magnetic resonance imaging (fMRI) recorded in 33 healthy subjects during rest. We constructed undirected networks based on the Anatomic-Automatic-Labeling (AAL) atlas template and calculated several commonly used measures from the field of graph theory, focusing on the influence of different strategies for confound correction. For each subject, method and session we computed the following graph metrics: clustering coefficient, characteristic path length, local and global efficiency, assortativity, modularity, hierarchy and the small-worldness scalar. Reliability of each graph metric was assessed using the intraclass correlation coefficient (ICC).&lt;br /&gt;&lt;br /&gt;Overall ICCs ranged from low to high (0 to 0.763) depending on the method and metric. Methodologically, the use of a broader frequency band (0.008-0.15 Hz) yielded highest reliability indices (mean ICC=0.484), followed by the use of global regression (mean ICC=0.399). In general, the second order metrics (small-worldness, hierarchy, assortativity) studied here, tended to be more robust than first order metrics.&lt;br /&gt;&lt;br /&gt;In conclusion, our study provides methodological recommendations which allow the computation of sufficiently robust markers of network organization using graph metrics derived from fMRI data at rest.</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen