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dc.contributor.authorKorbmacher, Max
dc.contributor.authorWang, Mengyun
dc.contributor.authorEikeland, Rune
dc.contributor.authorBuchert, Ralph
dc.contributor.authorAndreassen, Ole
dc.contributor.authorEspeseth, Thomas
dc.contributor.authorLeonardsen, Esten Høyland
dc.contributor.authorWestlye, Lars Tjelta
dc.contributor.authorMaximov, Ivan
dc.contributor.authorSpecht, Karsten
dc.date.accessioned2023-09-11T08:16:14Z
dc.date.available2023-09-11T08:16:14Z
dc.date.issued2023-08-16
dc.description.abstractIntroduction - Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls.<p> <p>Methods - We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model.<p> <p>Results - We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality.<p> <p>Conclusion - The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.en_US
dc.identifier.citationKorbmacher, Wang, Eikeland, Buchert, Andreassen, Espeseth, Leonardsen, Westlye, Maximov, Specht. Considerations on brain age predictions from repeatedly sampled data across time. Brain and Behavior. 2023en_US
dc.identifier.cristinIDFRIDAID 2172820
dc.identifier.doi10.1002/brb3.3219
dc.identifier.issn2162-3279
dc.identifier.urihttps://hdl.handle.net/10037/30870
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalBrain and Behavior
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/847776//Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms/CoMorMent/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleConsiderations on brain age predictions from repeatedly sampled data across timeen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)