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Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder : A Multisample Diffusion Tensor Imaging Study

Tønnesen, Siren (author)
Kaufmann, Tobias (author)
de Lange, Ann-Marie G (author)
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Richard, Geneviève (author)
Doan, Nhat Trung (author)
Alnæs, Dag (author)
van der Meer, Dennis (author)
Rokicki, Jaroslav (author)
Moberget, Torgeir (author)
Maximov, Ivan I (author)
Agartz, Ingrid (author)
Karolinska Institutet
Aminoff, Sofie R (author)
Beck, Dani (author)
Barch, Deanna M (author)
Beresniewicz, Justyna (author)
Cervenka, Simon (author)
Karolinska Institutet
Fatouros-Bergman, Helena (author)
Karolinska Institutet
Craven, Alexander R (author)
Flyckt, Lena (author)
Karolinska Institutet
Gurholt, Tiril P (author)
Haukvik, Unn K (author)
Hugdahl, Kenneth (author)
Johnsen, Erik (author)
Jönsson, Erik G (author)
Karolinska Institutet
Kolskår, Knut K (author)
Kroken, Rune Andreas (author)
Lagerberg, Trine V (author)
Løberg, Else-Marie (author)
Nordvik, Jan Egil (author)
Sanders, Anne-Marthe (author)
Ulrichsen, Kristine (author)
Andreassen, Ole A (author)
Westlye, Lars T (author)
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 (creator_code:org_t)
Elsevier BV, 2020
2020
English.
In: Biological Psychiatry. - : Elsevier BV. - 2451-9022 .- 2451-9030. ; 5:12, s. 1095-1103
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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 = -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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Psykiatri (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Psychiatry (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)

Keyword

Bipolar
Brain age
DTI
Machine learning
Psychosis
Schizophrenia

Publication and Content Type

ref (subject category)
art (subject category)

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