Publikation:

Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2012

Autor:innen

Eskildsen, Simon
Coupé, Pierrick
García-Lorenzo, Daniel
Fonov, Vladimir
Collins, Louis

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

YUSHKEVICH, Paul A., ed., Lei WANG, ed., Sebastien OURSELIN, ed.. NIBAD'12 : MICCAI 2012 Workshop on Novel Biomarkers for Alzheimer's Disease and Related Disorders. CreateSpace Independent Publishing Platform, 2012, pp. 79-90. ISBN 978-1-4792-6199-4

Zusammenfassung

Predicting Alzheimer's disease (AD) in individuals with some symp-toms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensi-tivity to cortical gray matter changes. In this study, we investigated the possibil-ity of using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and fea-tures were selected as regions of interest within these patterns. The selected re-gions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. The accuracy of the pre-diction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. These results show that prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
150 Psychologie

Schlagwörter

Konferenz

MICCAI 2012 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, 5. Okt. 2012 - 5. Okt. 2012, Nice
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690ESKILDSEN, Simon, Pierrick COUPÉ, Daniel GARCÍA-LORENZO, Vladimir FONOV, Jens C. PRUESSNER, Louis COLLINS, 2012. Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage. MICCAI 2012 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders. Nice, 5. Okt. 2012 - 5. Okt. 2012. In: YUSHKEVICH, Paul A., ed., Lei WANG, ed., Sebastien OURSELIN, ed.. NIBAD'12 : MICCAI 2012 Workshop on Novel Biomarkers for Alzheimer's Disease and Related Disorders. CreateSpace Independent Publishing Platform, 2012, pp. 79-90. ISBN 978-1-4792-6199-4
BibTex
@inproceedings{Eskildsen2012Impro-56040,
  year={2012},
  title={Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage},
  isbn={978-1-4792-6199-4},
  publisher={CreateSpace Independent Publishing Platform},
  booktitle={NIBAD'12 : MICCAI 2012 Workshop on Novel Biomarkers for Alzheimer's Disease and Related Disorders},
  pages={79--90},
  editor={Yushkevich, Paul A. and Wang, Lei and Ourselin, Sebastien},
  author={Eskildsen, Simon and Coupé, Pierrick and García-Lorenzo, Daniel and Fonov, Vladimir and Pruessner, Jens C. and Collins, Louis}
}
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/123456789/56040">
    <dc:contributor>Pruessner, Jens C.</dc:contributor>
    <dc:creator>Coupé, Pierrick</dc:creator>
    <dc:contributor>Coupé, Pierrick</dc:contributor>
    <dc:language>eng</dc:language>
    <dc:contributor>García-Lorenzo, Daniel</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:abstract xml:lang="eng">Predicting Alzheimer's disease (AD) in individuals with some symp-toms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensi-tivity to cortical gray matter changes. In this study, we investigated the possibil-ity of using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and fea-tures were selected as regions of interest within these patterns. The selected re-gions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. The accuracy of the pre-diction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. These results show that prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD.</dcterms:abstract>
    <dc:creator>Eskildsen, Simon</dc:creator>
    <dcterms:title>Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage</dcterms:title>
    <dc:creator>Fonov, Vladimir</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>García-Lorenzo, Daniel</dc:creator>
    <dc:contributor>Collins, Louis</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-23T10:52:51Z</dc:date>
    <dc:creator>Pruessner, Jens C.</dc:creator>
    <dc:contributor>Fonov, Vladimir</dc:contributor>
    <dcterms:issued>2012</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/56040"/>
    <dc:creator>Collins, Louis</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Eskildsen, Simon</dc:contributor>
    <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">2021-12-23T10:52:51Z</dcterms:available>
  </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
Diese Publikation teilen