Decentralized Brain Age Estimation Using MRI Data.
Brain age
COINSTAC
Decentralized
Federated
Journal
Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
accepted:
27
01
2022
pubmed:
6
4
2022
medline:
26
10
2022
entrez:
5
4
2022
Statut:
ppublish
Résumé
Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age regression models can achieve similar performance compared to the models trained with all the data in one location.
Identifiants
pubmed: 35380365
doi: 10.1007/s12021-022-09570-x
pii: 10.1007/s12021-022-09570-x
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
981-990Subventions
Organisme : NIMH NIH HHS
ID : R01 MH121246
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA049238
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA040487
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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