Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

Hofmann, S., Beyer, F., Lapuschkin, S., Goltermann, O., Loeffler, M., Müller, K.-R., et al. (2022). Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NeuroImage, 261: 119504. doi:10.1016/j.neuroimage.2022.119504.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000A-C9F6-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000D-DB72-C
Genre: Zeitschriftenartikel

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Hofmann_2022.pdf (Verlagsversion), 5MB
Öffnen   Speichern
Datei-Permalink:
https://hdl.handle.net/21.11116/0000-000A-F1DE-C
Name:
Hofmann_2022.pdf
Beschreibung:
-
OA-Status:
Gold
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Öffnen
Copyright Datum:
-
Copyright Info:
-
Lizenz:
https://creativecommons.org/licenses/by-nc-nd/4.0/

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Hofmann, Simon1, 2, 3, Autor                 
Beyer, Frauke1, 3, Autor           
Lapuschkin, Sebastian2, Autor
Goltermann, Ole1, 4, 5, Autor
Loeffler, Markus6, Autor
Müller, Klaus-Robert7, 8, 9, 10, 11, Autor
Villringer, Arno1, 3, 12, 13, Autor           
Samek, Wojciech2, 11, Autor
Witte, A. Veronica1, 3, Autor           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany, ou_persistent22              
3Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
4Max Planck School of Cognition, Leipzig, Germany, ou_persistent22              
5Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
6Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany, ou_persistent22              
7Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
8Center for Artificial Intelligence, Korea University, Seoul, Republic of Korea, ou_persistent22              
9Brain Team, Google Research, Berlin, Germany, ou_persistent22              
10Max Planck Institute for Informatics, Saarbrücken, Germany, ou_persistent22              
11BIFOLD Berlin Institute for the Foundations of Learning and Data, Germany, ou_persistent22              
12MindBrainBody Institute, Berlin School of Mind and Brain, Germany, ou_persistent22              
13Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: Aging; Brain-age; Cardiovascular risk factors; Deep learning; Explainable A.I.; Structural MRI
 Zusammenfassung: Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2022-07-152022-07-212022-07-232022-11-01
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1016/j.neuroimage.2022.119504
Anderer: epub 2022
PMID: 35882272
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden: ausblenden:
Projektname : -
Grant ID : 713-241202; 14505/2470
Förderprogramm : -
Förderorganisation : LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig
Projektname : -
Grant ID : 209933838
Förderprogramm : -
Förderorganisation : German Research Foundation (DFG)
Projektname : -
Grant ID : 01IS18025A; 01IS18037A; 01IS17058
Förderprogramm : -
Förderorganisation : Federal Ministry of Education & Research (BMBF)
Projektname : -
Grant ID : 965221; 01GQ1115; 01GQ0850
Förderprogramm : -
Förderorganisation : European Union (EU)
Projektname : -
Grant ID : EXC 2046/1; 390685689
Förderprogramm : -
Förderorganisation : German Research Foundation (DFG)
Projektname : -
Grant ID : 2019-0-00079
Förderprogramm : -
Förderorganisation : Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea

Quelle 1

einblenden:
ausblenden:
Titel: NeuroImage
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Orlando, FL : Academic Press
Seiten: - Band / Heft: 261 Artikelnummer: 119504 Start- / Endseite: - Identifikator: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166