A deep learning model for brain age prediction using minimally preprocessed T1w images as input
A deep learning model for brain age prediction using minimally preprocessed T1w images as input
About this item
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Author / Creator
Dartora, Caroline , Marseglia, Anna , Mårtensson, Gustav , Rukh, Gull , Dang, Junhua , Muehlboeck, J-Sebastian , Wahlund, Lars-Olof , Moreno, Rodrigo , Barroso, José , Ferreira, Daniel , Schiöth, Helgi B. , Westman, Eric , the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing , the Japanese Alzheimer’s Disease Neuroimaging Initiative , for the Alzheimer’s Disease Neuroimaging Initiative , the AddNeuroMed Consortium , AddNeuroMed Consortium , Alzheimer’s Disease Neuroimaging Initiative , Japanese Alzheimer’s Disease Neuroimaging Initiative and Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Publisher
Switzerland: Frontiers Research Foundation
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: Frontiers Research Foundation
Subjects
More information
Scope and Contents
Contents
In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one...
Alternative Titles
Full title
A deep learning model for brain age prediction using minimally preprocessed T1w images as input
Authors, Artists and Contributors
Author / Creator
Marseglia, Anna
Mårtensson, Gustav
Rukh, Gull
Dang, Junhua
Muehlboeck, J-Sebastian
Wahlund, Lars-Olof
Moreno, Rodrigo
Barroso, José
Ferreira, Daniel
Schiöth, Helgi B.
Westman, Eric
the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
the Japanese Alzheimer’s Disease Neuroimaging Initiative
for the Alzheimer’s Disease Neuroimaging Initiative
the AddNeuroMed Consortium
AddNeuroMed Consortium
Alzheimer’s Disease Neuroimaging Initiative
Japanese Alzheimer’s Disease Neuroimaging Initiative
Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_07418e3d10c44cdc9c0ff683ec1c29b3
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_07418e3d10c44cdc9c0ff683ec1c29b3
Other Identifiers
ISSN
1663-4365
E-ISSN
1663-4365
DOI
10.3389/fnagi.2023.1303036
