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, 22 (10), 1617-1623

Common Brain Disorders Are Associated With Heritable Patterns of Apparent Aging of the Brain

Tobias Kaufmann  1 Dennis van der Meer  2   3 Nhat Trung Doan  2 Emanuel Schwarz  4 Martina J Lund  2 Ingrid Agartz  2   5   6 Dag Alnæs  2 Deanna M Barch  7   8   9 Ramona Baur-Streubel  10 Alessandro Bertolino  11   12 Francesco Bettella  2 Mona K Beyer  13   14 Erlend Bøen  5   15 Stefan Borgwardt  16   17   18 Christine L Brandt  2 Jan Buitelaar  19   20 Elisabeth G Celius  13   21 Simon Cervenka  6 Annette Conzelmann  22 Aldo Córdova-Palomera  2 Anders M Dale  23   24   25   26 Dominique J F de Quervain  27   28 Pasquale Di Carlo  12 Srdjan Djurovic  29   30 Erlend S Dørum  2   31   32 Sarah Eisenacher  4 Torbjørn Elvsåshagen  2   13   21 Thomas Espeseth  31 Helena Fatouros-Bergman  6 Lena Flyckt  6 Barbara Franke  33 Oleksandr Frei  2 Beathe Haatveit  2   31 Asta K Håberg  34   35 Hanne F Harbo  13   21 Catharina A Hartman  36 Dirk Heslenfeld  37   38 Pieter J Hoekstra  39 Einar A Høgestøl  13   21 Terry L Jernigan  40   41   42 Rune Jonassen  43 Erik G Jönsson  2   6 Karolinska Schizophrenia Project (KaSP)Peter Kirsch  44   45 Iwona Kłoszewska  46 Knut K Kolskår  2   31   32 Nils Inge Landrø  5   31 Stephanie Le Hellard  30 Klaus-Peter Lesch  47   48   49 Simon Lovestone  50 Arvid Lundervold  51   52 Astri J Lundervold  53 Luigi A Maglanoc  2   31 Ulrik F Malt  13   54 Patrizia Mecocci  55 Ingrid Melle  2 Andreas Meyer-Lindenberg  4 Torgeir Moberget  2 Linn B Norbom  2   31 Jan Egil Nordvik  56 Lars Nyberg  57 Jaap Oosterlaan  37   58 Marco Papalino  12 Andreas Papassotiropoulos  27   59   60 Paul Pauli  10 Giulio Pergola  12 Karin Persson  61   62 Geneviève Richard  2   31   32 Jaroslav Rokicki  2   31 Anne-Marthe Sanders  2   31   32 Geir Selbæk  13   61   62 Alexey A Shadrin  2 Olav B Smeland  2 Hilkka Soininen  63   64 Piotr Sowa  14 Vidar M Steen  30   65 Magda Tsolaki  66 Kristine M Ulrichsen  2   31   32 Bruno Vellas  67 Lei Wang  68 Eric Westman  17   69 Georg C Ziegler  47 Mathias Zink  4   70 Ole A Andreassen  2 Lars T Westlye  71   72
Collaborators, Affiliations

Common Brain Disorders Are Associated With Heritable Patterns of Apparent Aging of the Brain

Tobias Kaufmann et al. Nat Neurosci.

Erratum in

  • Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain.
    Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, Alnæs D, Barch DM, Baur-Streubel R, Bertolino A, Bettella F, Beyer MK, Bøen E, Borgwardt S, Brandt CL, Buitelaar J, Celius EG, Cervenka S, Conzelmann A, Córdova-Palomera A, Dale AM, de Quervain DJF, Di Carlo P, Djurovic S, Dørum ES, Eisenacher S, Elvsåshagen T, Espeseth T, Fatouros-Bergman H, Flyckt L, Franke B, Frei O, Haatveit B, Håberg AK, Harbo HF, Hartman CA, Heslenfeld D, Hoekstra PJ, Høgestøl EA, Jernigan TL, Jonassen R, Jönsson EG; Karolinska Schizophrenia Project (KaSP), Kirsch P, Kłoszewska I, Kolskår KK, Landrø NI, Le Hellard S, Lesch KP, Lovestone S, Lundervold A, Lundervold AJ, Maglanoc LA, Malt UF, Mecocci P, Melle I, Meyer-Lindenberg A, Moberget T, Norbom LB, Nordvik JE, Nyberg L, Oosterlaan J, Papalino M, Papassotiropoulos A, Pauli P, Pergola G, Persson K, Richard G, Rokicki J, Sanders AM, Selbæk G, Shadrin AA, Smeland OB, Soininen H, Sowa P, Steen VM, Tsolaki M, Ulrichsen KM, Vellas B, Wang L, Westman E, Ziegler GC, Zink M, Andreassen OA, Westlye LT. Kaufmann T, et al. Nat Neurosci. 2020 Feb;23(2):295. doi: 10.1038/s41593-019-0553-6. Nat Neurosci. 2020. PMID: 31848485


Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.

Conflict of interest statement

Competing financial interests

Some authors received educational speaker’s honorarium from Lundbeck (O.A. Andreassen, A. Bertolino, T. Elvsåshagen, M. Zink, N. I. Landrø), Sunovion (O.A. Andreassen), Shire (B. Franke), Medice (B. Franke), Otsuka (A. Bertolino, M. Zink) and Jannsen (A. Bertolino), Roche (M. Zink), Ferrer (M. Zink), Trommsdorff (M. Zink), Servier (M. Zink), all of these unrelated to this work. A. Bertolino is a stockholder of Hoffmann-La Roche Ltd and has received consultant fees from Biogen Idec. E. G. Celius and H. F. Harbo have received travel support, honoraria for advice and lecturing from Almirall (Celius), Biogen Idec (both), Genzyme (both), Merck (both), Novartis(both), Roche (both), Sanofi-Aventis (both) and Teva (both). They have received unrestricted research grants from Novartis (Celius, Harbo), Biogen Idec (Celius) and Genzyme (Celius). G. Pergola has been the academic supervisor of a Roche collaboration grant (years 2015-16) that funds his salary. None of the mentioned external parties had any role in the analysis, writing or decision to publish this work. Other authors declare no competing financial interests.


Figure 1
Figure 1. Sample distributions and imaging features used for brain age prediction.
a, Age distributions of the training (left) and the ten test samples (right) per sex and diagnosis. The grey shades behind each clinical group reflect its age-, sex- and site-matched control group. b, Cortical features from the Human Connectome Project (HCP) atlas as well as cerebellar/subcortical features used for brain age prediction. Colours were assigned randomly to each feature. All features were used in the full brain feature set (left), whereas only those from specific regions (occipital, frontal, temporal, parietal, cingulate, insula, cerebellar/subcortical) were included in the regional feature set (right). For illustration purpose, the left hemisphere is shown.
Figure 2
Figure 2. Apparent brain aging is common in several brain disorders and sensitive to clinical and cognitive measures.
a, The gap between chronological age and brain age was increased in several disorders. The grey shades behind each clinical group reflect its age-, sex- and site-matched controls. The test samples comprised n=925 ASD / n=925 HC, n=725 ADHD / n=725 HC, n=94 SZRISK / n=94 HC, n=1110 SZ / n=1110 HC, n=300 PSYMIX / n=300 HC, n=459 BD / n=459 HC, n=254 MS / n=254 HC, n=208 MDD / n=208 HC, n=974 MCI / n=974 HC, n=739 DEM / n=739 HC; in total n=10,141 independent subjects. Cohen’s d effect sizes (pooled standard deviation units) and two-sided P-values are provided. b, Several disorders showed specific patterns in regional brain age gaps. Colours indicate Cohen’s d effect sizes for group comparisons. Sample size as specified in panel a. Corresponding correlation matrix of the effect sizes is depicted in Suppl. Fig. 9. c, Effect sizes of significant region by group interactions from repeated measures ANOVAs run for each combination of regions and groups (1260 tests in total). Sample size as specified in panel a yet excluding HC; n=5788 independent subjects. Only significant (p<FDR; Benjamini-Hochberg) effects are shown. Suppl. Fig. 10 depicts effect sizes for all 1260 tests. d, Correlation coefficients for linear associations between brain age gaps and cognitive and clinical scores. Sample size comprised n=389 SZ for GAFsymptom, n=269 SZ for GAFfunction, n=646 SZ for PANSSpositive, n=626 SZ for PANSSnegative, n=195 MS for EDSS, n=907 MCI and n=686 DEM for MMSE. Associations were computed using linear models accounting for age, age2, sex, scanning site and Euler number, and the resulting t-statistics were transformed to r. Significant (P<FDR; Benjamini-Hochberg; two-sided) associations are marked with a black box. Corresponding scatter plots are depicted in Suppl. Fig 11.
Figure 3
Figure 3. The brain age gaps are heritable, and the genetic underpinnings overlap with those observed for several disorders.
Genetic analyses were performed using data from n=20,170 healthy adult individuals with European ancestry a, Heritability (h2) estimated using LD Score regression. Error bars reflect standard error. b, Significantly (P<FDR) overlapping loci between brain age gaps and disorders, identified using conjunctional FDR. c, Corresponding to panel b, the overlapping genes across all disorders, coloured by significance and sized by frequency of detection.

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