Early functional magnetic resonance imaging activations predict language outcome after stroke

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2010
Autor:innen
Saur, Dorothee
Ronneberger, Olaf
Kümmerer, Dorothee
Mader, Irina
Weiller, Cornelius
Klöppel, Stefan
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
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
Brain. 2010, 133(4), pp. 1252-1264. ISSN 0006-8950. eISSN 1460-2156. Available under: doi: 10.1093/brain/awq021
Zusammenfassung

An accurate prediction of system-specific recovery after stroke is essential to provide rehabilitation therapy based on the individual needs. We explored the usefulness of functional magnetic resonance imaging scans from an auditory language comprehension experiment to predict individual language recovery in 21 aphasic stroke patients. Subjects with an at least moderate language impairment received extensive language testing 2 weeks and 6 months after left-hemispheric stroke. A multivariate machine learning technique was used to predict language outcome 6 months after stroke. In addition, we aimed to predict the degree of language improvement over 6 months. 76% of patients were correctly separated into those with good and bad language performance 6 months after stroke when based on functional magnetic resonance imaging data from language relevant areas. Accuracy further improved (86% correct assignments) when age and language score were entered alongside functional magnetic resonance imaging data into the fully automatic classifier. A similar accuracy was reached when predicting the degree of language improvement based on imaging, age and language performance. No prediction better than chance level was achieved when exploring the usefulness of diffusion weighted imaging as well as functional magnetic resonance imaging acquired two days after stroke. This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke. Best prediction of language recovery is achieved when the brain activation potential after system-specific stimulation is assessed in the second week post stroke. More intensive early rehabilitation could be provided for those with a predicted poor recovery and the extension to other systems, for example, motor and attention seems feasible.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
150 Psychologie
Schlagwörter
Aphasia, stroke, outcome prediction, language impairment, functional magnetic resonance imaging, support vector machine
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690SAUR, Dorothee, Olaf RONNEBERGER, Dorothee KÜMMERER, Irina MADER, Cornelius WEILLER, Stefan KLÖPPEL, 2010. Early functional magnetic resonance imaging activations predict language outcome after stroke. In: Brain. 2010, 133(4), pp. 1252-1264. ISSN 0006-8950. eISSN 1460-2156. Available under: doi: 10.1093/brain/awq021
BibTex
@article{Saur2010-04Early-25802,
  year={2010},
  doi={10.1093/brain/awq021},
  title={Early functional magnetic resonance imaging activations predict language outcome after stroke},
  number={4},
  volume={133},
  issn={0006-8950},
  journal={Brain},
  pages={1252--1264},
  author={Saur, Dorothee and Ronneberger, Olaf and Kümmerer, Dorothee and Mader, Irina and Weiller, Cornelius and Klöppel, Stefan}
}
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/123456789/25802">
    <dcterms:title>Early functional magnetic resonance imaging activations predict language outcome after stroke</dcterms:title>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:bibliographicCitation>Brain : a journal of neurology ; 133 (2010), 4. - S. 1252-1264</dcterms:bibliographicCitation>
    <dc:creator>Saur, Dorothee</dc:creator>
    <dc:creator>Ronneberger, Olaf</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-10T12:45:49Z</dcterms:available>
    <dc:contributor>Weiller, Cornelius</dc:contributor>
    <dc:contributor>Saur, Dorothee</dc:contributor>
    <dcterms:abstract xml:lang="eng">An accurate prediction of system-specific recovery after stroke is essential to provide rehabilitation therapy based on the individual needs. We explored the usefulness of functional magnetic resonance imaging scans from an auditory language comprehension experiment to predict individual language recovery in 21 aphasic stroke patients. Subjects with an at least moderate language impairment received extensive language testing 2 weeks and 6 months after left-hemispheric stroke. A multivariate machine learning technique was used to predict language outcome 6 months after stroke. In addition, we aimed to predict the degree of language improvement over 6 months. 76% of patients were correctly separated into those with good and bad language performance 6 months after stroke when based on functional magnetic resonance imaging data from language relevant areas. Accuracy further improved (86% correct assignments) when age and language score were entered alongside functional magnetic resonance imaging data into the fully automatic classifier. A similar accuracy was reached when predicting the degree of language improvement based on imaging, age and language performance. No prediction better than chance level was achieved when exploring the usefulness of diffusion weighted imaging as well as functional magnetic resonance imaging acquired two days after stroke. This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke. Best prediction of language recovery is achieved when the brain activation potential after system-specific stimulation is assessed in the second week post stroke. More intensive early rehabilitation could be provided for those with a predicted poor recovery and the extension to other systems, for example, motor and attention seems feasible.</dcterms:abstract>
    <dc:creator>Weiller, Cornelius</dc:creator>
    <dc:creator>Mader, Irina</dc:creator>
    <dc:contributor>Ronneberger, Olaf</dc:contributor>
    <dc:creator>Kümmerer, Dorothee</dc:creator>
    <dc:contributor>Mader, Irina</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Klöppel, Stefan</dc:creator>
    <dc:language>eng</dc:language>
    <dc:contributor>Klöppel, Stefan</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/25802"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-10T12:45:49Z</dc:date>
    <dc:contributor>Kümmerer, Dorothee</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:issued>2010-04</dcterms:issued>
  </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
Begutachtet
Diese Publikation teilen