1. Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study
- Author
-
Siren Tønnesen, Tobias Kaufmann, Ann-Marie G. de Lange, Geneviève Richard, Nhat Trung Doan, Dag Alnæs, Dennis van der Meer, Jaroslav Rokicki, Torgeir Moberget, Ivan I. Maximov, Ingrid Agartz, Sofie R. Aminoff, Dani Beck, Deanna M. Barch, Justyna Beresniewicz, Simon Cervenka, Helena Fatouros-Bergman, Alexander R. Craven, Lena Flyckt, Tiril P. Gurholt, Unn K. Haukvik, Kenneth Hugdahl, Erik Johnsen, Erik G. Jönsson, Knut K. Kolskår, Rune Andreas Kroken, Trine V. Lagerberg, Else-Marie Løberg, Jan Egil Nordvik, Anne-Marthe Sanders, Kristine Ulrichsen, Ole A. Andreassen, Lars T. Westlye, L. Farde, L. Flyckt, G. Engberg, S. Erhardt, H. Fatouros-Bergman, S. Cervenka, L. Schwieler, F. Piehl, I. Agartz, K. Collste, P. Victorsson, A. Malmqvist, M. Hedberg, F. Orhan, C. Sellgren, Psychiatrie & Neuropsychologie, and RS: MHeNs - R2 - Mental Health
- Subjects
medicine.medical_specialty ,Psychosis ,Bipolar Disorder ,Cognitive Neuroscience ,Audiology ,050105 experimental psychology ,Psykiatri ,White matter ,MOVEMENT ,03 medical and health sciences ,0302 clinical medicine ,DEFICITS ,Fractional anisotropy ,Machine learning ,Medicine ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Generalizability theory ,Bipolar disorder ,METAANALYSIS ,Biological Psychiatry ,GRAY ,Psychiatry ,ABNORMALITIES ,business.industry ,05 social sciences ,Linear model ,Brain ,Brain age ,medicine.disease ,White Matter ,CORPUS-CALLOSUM ,INTEGRITY ,medicine.anatomical_structure ,Diffusion Tensor Imaging ,Schizophrenia ,DTI ,ONSET ,Bipolar ,PATTERNS ,Neurology (clinical) ,MICROSTRUCTURE ,MYELINATION ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
BACKGROUND: 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 = -20.29) 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.
- Published
- 2020