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dc.contributor.authorTønnesen, Siren
dc.contributor.authorKaufmann, Tobias
dc.contributor.authorde Lange, Ann-Marie Glasø
dc.contributor.authorRichard, Geneviève
dc.contributor.authorNhat Trung, Doan
dc.contributor.authorAlnæs, Dag
dc.contributor.authorvan der Meer, Dennis
dc.contributor.authorRokicki, Jaroslav
dc.contributor.authorMoberget, Torgeir
dc.contributor.authorMaximov, Ivan
dc.contributor.authorAgartz, Ingrid
dc.contributor.authorAminoff, Sofie Ragnhild
dc.contributor.authorBeck, Dani
dc.contributor.authorBarch, Deanna M.
dc.contributor.authorBeresniewicz, Justyna
dc.contributor.authorCervenka, Simon
dc.contributor.authorFatouros-Bergman, Helena
dc.contributor.authorCraven, Alexander R.
dc.contributor.authorFlyckt, Lena
dc.contributor.authorGurholt, Tiril Pedersen
dc.contributor.authorHaukvik, Unn Kristin H.
dc.contributor.authorHugdahl, Kenneth
dc.contributor.authorJohnsen, Erik
dc.contributor.authorJönsson, Erik Gunnar
dc.contributor.authorSchizophrenia Project (KaSP), Karolinska
dc.contributor.authorKolskår, Knut-Kristian
dc.contributor.authorKroken, Rune Andreas
dc.contributor.authorLagerberg, Trine Vik
dc.contributor.authorLøberg, Else-Marie
dc.contributor.authorNordvik, Jan Egil
dc.contributor.authorSanders, Anne-Marthe
dc.contributor.authorUlrichsen, Kristine Moe
dc.contributor.authorAndreassen, Ole Andreas
dc.contributor.authorWestlye, Lars Tjelta
dc.description.abstractBackground Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleBrain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging studyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2020 Society of Biological Psychiatry.en_US
dc.source.journalBiological Psychiatry: Cognitive Neuroscience and Neuroimagingen_US
dc.relation.projectNorges forskningsråd: 273345en_US
dc.identifier.citationBiological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2020, 5 (12), 1095-1103en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal