Common brain disorders are associated with heritable patterns of apparent aging of the brain (original) (raw)

Data availability

The raw data incorporated in this work were gathered from various resources. Material requests will need to be placed with individual principal investigators. A detailed overview of the included cohorts is provided in Supplementary Table 1. GWAS summary statistics for the brain age gaps as well as the models needed to predict brain age in independent cohorts are available at github.com/tobias-kaufmann.

Code availability

Code needed to run brain age prediction models is available at github.com/tobias-kaufmann (see Data availability). Additional R statistics53 code is available from the authors upon request.

Change history

• 17 December 2019

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

1. WHO. World Health Statistics 2016: monitoring health for the SDGs. https://www.who.int/gho/publications/world_health_statistics/2016/en/ (2016).
2. Insel, T. R. & Cuthbert, B. N. Brain disorders? Precisely. Science 348, 499–500 (2015).
   Article CAS PubMed Google Scholar
3. Prince, M. et al. No health without mental health. Lancet 370, 859–877 (2007).
   Article PubMed Google Scholar
4. Parikshak, N. N., Gandal, M. J. & Geschwind, D. H. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet 16, 441–458 (2015).
   Article CAS PubMed PubMed Central Google Scholar
5. Marin, O. Developmental timing and critical windows for the treatment of psychiatric disorders. Nat. Med 22, 1229–1238 (2016).
   Article CAS PubMed Google Scholar
6. Insel, T. R. Rethinking schizophrenia. Nature 468, 187–193 (2010).
   Article CAS PubMed Google Scholar
7. Aubert-Broche, B. et al. Onset of multiple sclerosis before adulthood leads to failure of age-expected brain growth. Neurology 83, 2140–2146 (2014).
   Article PubMed PubMed Central Google Scholar
8. Masters, C. L. et al. Alzheimer’s disease. Nat. Rev. Dis. Primers 1, 15056 (2015).
   Article PubMed Google Scholar
9. Dosenbach, N. U. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).
   Article CAS PubMed PubMed Central Google Scholar
10. Franke, K., Ziegler, G., Klöppel, S. & Gaser, C. & Alzheimera’s Disease Neuroimaging Initiative. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50, 883–892 (2010).
    Article PubMed Google Scholar
11. Cole, J. H. & Franke, K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690 (2017).
    Article CAS PubMed Google Scholar
12. Ritchie, S. J. et al. Sex differences in the adult human brain: evidence from 5216 UK biobank participants. Cereb. Cortex 28, 2959–2975 (2018).
    Article PubMed PubMed Central Google Scholar
13. Cole, J. H. et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163, 115–124 (2017).
    Article PubMed Google Scholar
14. Bansal, V. et al. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat. Commun. 9, 3078 (2018).
    Article CAS PubMed PubMed Central Google Scholar
15. Ellison-Wright, I. & Bullmore, E. Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr. Res. 117, 1–12 (2010).
    Article PubMed Google Scholar
16. Jernigan, T. L., Salmon, D. P., Butters, N. & Hesselink, J. R. Cerebral structure on MRI, part II: specific changes in Alzheimer’s and Huntington’s diseases. Biol. psychiatry 29, 68–81 (1991).
    Article CAS PubMed Google Scholar
17. Wolfers, T. et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. Jama Psychiat 75, 1146–1155 (2018).
    Article Google Scholar
18. Ecker, C., Bookheimer, S. Y. & Murphy, D. G. Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol. 14, 1121–1134 (2015).
    Article PubMed Google Scholar
19. Faraone, S. V. et al. Attention-deficit/hyperactivity disorder. Nat. Rev. Dis. Primers 1, 15020 (2015).
    Article PubMed Google Scholar
20. Andreassen, O. A. et al. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci. Mol. psychiatry 20, 207 (2015).
    Article CAS PubMed Google Scholar
21. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
    Article CAS PubMed Google Scholar
22. Rosen, A. F. G. et al. Quantitative assessment of structural image quality. Neuroimage 169, 407–418 (2018).
    Article PubMed Google Scholar
23. Smith, S. M. & Nichols, T. E. Statistical challenges in “Big Data” human neuroimaging. Neuron 97, 263–268 (2018).
    Article CAS PubMed Google Scholar
24. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
    Article CAS PubMed PubMed Central Google Scholar
25. Ho, D., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).
    Article Google Scholar
26. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).
27. Chen, T., et al. Xgboost: extreme gradient boosting. R package v0.4-2 https://cran.r-project.org/web/packages/xgboost/ (2015).
28. Le, T. T. et al. A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE. Front. Aging Neurosci. 10, 317 (2018).
    Article PubMed PubMed Central Google Scholar
29. Zuber, V. & Strimmer, K. Care. R package v 1.1.10. https://cran.r-project.org/web/packages/care/care.pdf (2017).
30. Cole, J. H. et al. Brain age predicts mortality. Mol. psychiatry 23, 1385–1392 (2018).
    Article CAS PubMed Google Scholar
31. Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 82, 591–605 (2007).
    Article PubMed Google Scholar
32. Viechtbauer, W. Conducting meta-analysis in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
    Article Google Scholar
33. Warnes, G. R. et al. R Package gplots: various R programming tools for plotting data. https://cran.r-project.org/web/packages/gplots/gplots.pdf (2016).
34. Gu, Z. R. Package circlize: circular visualization. https://cran.r-project.org/web/packages/circlize/circlize.pdf (2017).
35. Bycroft, C. et al. TheUK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
    Article CAS PubMed PubMed Central Google Scholar
36. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
    CAS PubMed PubMed Central Google Scholar
37. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
    Article CAS PubMed PubMed Central Google Scholar
38. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
    Article CAS PubMed PubMed Central Google Scholar
39. Nichols, T., Brett, M., Andersson, J., Wager, T. & Poline, J.-B. Valid conjunction inference with the minimum statistic. Neuroimage 25, 653–660 (2005).
    Article PubMed Google Scholar
40. Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197–209 (2013).
    Article CAS PubMed PubMed Central Google Scholar
41. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
    Article CAS PubMed PubMed Central Google Scholar
42. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genet. 51, 63–75 (2018).
    Article CAS PubMed Google Scholar
43. Schizophrenia Working Group of the PGC. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421 (2014).
    Article CAS Google Scholar
44. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
    Article CAS PubMed PubMed Central Google Scholar
45. Patsopoulos, N. et al. The multiple sclerosis genomic map: role of peripheral immune cells and resident microglia in susceptibility. Preprint at biorXiv https://www.biorxiv.org/content/10.1101/143933v1 (2017).
46. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
    Article CAS PubMed PubMed Central Google Scholar
47. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452 (2013).
    Article CAS PubMed PubMed Central Google Scholar
48. McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
    Article CAS PubMed PubMed Central Google Scholar
49. Pedersen, G. & Karterud, S. The symptom and function dimensions of the Global Assessment of Functioning (GAF) scale. Compr. Psychiatry 53, 292–298 (2012).
    Article PubMed Google Scholar
50. Kay, S. R., Fiszbein, A. & Opfer, L. A. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261 (1987).
    Article CAS PubMed Google Scholar
51. Kurtzke, J. F. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1444 (1983).
    Article CAS PubMed Google Scholar
52. Folstein, M. F., Folstein, S. E. & McHugh, P. R. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975).
    Article CAS PubMed Google Scholar
53. R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2013).

Download references

Acknowledgements

The author list between I.A. and M.Z. is in alphabetic order. The authors were funded by the Research Council of Norway (276082 LifespanHealth (T.K.), 213837 (O.A.A), 223273 NORMENT (O.A.A.), 204966 (L.T.W.), 229129 (O.A.A.), 249795 (L.T.W.), 273345 (L.T.W.) and 283798 SYNSCHIZ (O.A.A.)), the South-Eastern Norway Regional Health Authority (2013-123 (O.A.A.), 2014-097 (L.T.W.), 2015-073 (L.T.W.) and 2016083 (L.T.W.)), Stiftelsen Kristian Gerhard Jebsen (SKGJ-MED-008), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting Grant, Grant agreement No. 802998 BRAINMINT (L.T.W.)), NVIDIA Corporation GPU Grant (T.K.), and the European Commission 7th Framework Programme (602450, IMAGEMEND (A.M.-L.)). The data used in this study were gathered from various sources. A detailed overview of the included cohorts and acknowledgement of their respective funding sources and cohort-specific details is provided in Supplementary Table 1. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), the AddNeuroMed consortium and the Pediatric Imaging, Neurocognition and Genetics (PING) study database (www.chd.ucsd.edu/research/ping-study.html, now shared through the NIMH Data Archive (NDA)). The investigators within the ADNI and PING studies contributed to the design and implementation of ADNI and PING or provided data but did not participate in the analysis or writing of this report. This publication is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health or PING investigators. Complete listings of participating sites and study investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf and http://pingstudy.ucsd.edu/investigators.html. The AddNeuroMed consortium was led by S.L., B.V., P.M., M.T., I.K. and H.S.

Author information

Author notes

1. A list of authors and affiliations appears at the end of the paper.

Authors and Affiliations

1. NORMENT, Division of Mental Health and Addiction Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
   Tobias Kaufmann, Dennis van der Meer, Nhat Trung Doan, Martina J. Lund, Ingrid Agartz, Dag Alnæs, Francesco Bettella, Christine L. Brandt, Aldo Córdova-Palomera, Erlend S. Dørum, Torbjørn Elvsåshagen, Oleksandr Frei, Beathe Haatveit, Erik G. Jönsson, Ingrid Agartz, Knut K. Kolskår, Luigi A. Maglanoc, Ingrid Melle, Torgeir Moberget, Linn B. Norbom, Geneviève Richard, Jaroslav Rokicki, Anne-Marthe Sanders, Alexey A. Shadrin, Olav B. Smeland, Kristine M. Ulrichsen, Ole A. Andreassen & Lars T. Westlye
2. School of Mental Health and Neuroscience Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
   Dennis van der Meer
3. Department of Psychiatry and Psychotherapy Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
   Emanuel Schwarz, Sarah Eisenacher, Andreas Meyer-Lindenberg & Mathias Zink
4. Department of Psychiatry Diakonhjemmet Hospital, Oslo, Norway
   Ingrid Agartz, Erlend Bøen, Ingrid Agartz & Nils Inge Landrø
5. Centre for Psychiatry Research, Department of Clinical Neuroscience Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
   Ingrid Agartz, Simon Cervenka, Helena Fatouros-Bergman, Lena Flyckt, Erik G. Jönsson, Lars Farde, Lena Flyckt, Helena Fatouros-Bergman, Simon Cervenka, Ingrid Agartz, Karin Collste & Pauliina Victorsson
6. Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, USA
   Deanna M. Barch
7. Department of Psychiatry Washington, University in St. Louis, St. Louis, USA
   Deanna M. Barch
8. Department of Radiology Washington, University in St. Louis, St. Louis, USA
   Deanna M. Barch
9. Department of Psychology I, University of Würzburg, Würzburg, Germany
   Ramona Baur-Streubel & Paul Pauli
10. Institute of Psychiatry Bari University Hospital, Bari, Italy
    Alessandro Bertolino
11. Department of Basic Medical Science, Neuroscience and Sense Organs University of Bari, Bari, Italy
    Alessandro Bertolino, Pasquale Di Carlo, Marco Papalino & Giulio Pergola
12. Institute of Clinical Medicine, University of Oslo, Oslo, Norway
    Mona K. Beyer, Elisabeth G. Celius, Torbjørn Elvsåshagen, Hanne F. Harbo, Einar A. Høgestøl, Ulrik F. Malt & Geir Selbæk
13. Division of Radiology and Nuclear Medicine, Section of Neuroradiology Oslo University Hospital, Oslo, Norway
    Mona K. Beyer & Piotr Sowa
14. Psychosomatic and CL Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
    Erlend Bøen
15. Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
    Stefan Borgwardt
16. Department of Psychiatry, Psychosomatics and Psychotherapy University of Lübeck, Lübeck, Germany
    Stefan Borgwardt & Eric Westman
17. Institute of Psychiatry King’s College, London, UK
    Stefan Borgwardt
18. Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour Radboud University Medical Center, Nijmegen, The Netherlands
    Jan Buitelaar
19. Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
    Jan Buitelaar
20. Department of Neurology, Oslo University Hospital, Oslo, Norway
    Elisabeth G. Celius, Torbjørn Elvsåshagen, Hanne F. Harbo & Einar A. Høgestøl
21. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy University of Tübingen, Tübingen, Germany
    Annette Conzelmann
22. Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA, USA
    Anders M. Dale
23. Department of Radiology, University of California, San Diego, La Jolla, CA, USA
    Anders M. Dale
24. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
    Anders M. Dale
25. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
    Anders M. Dale
26. Division of Cognitive Neuroscience, University of Basel, Basel, Switzerland
    Dominique J. F. de Quervain & Andreas Papassotiropoulos
27. Transfaculty Research Platform Molecular and Cognitive Neurosciences University of Basel, Basel, Switzerland
    Dominique J. F. de Quervain
28. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
    Srdjan Djurovic
29. NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
    Srdjan Djurovic, Stephanie Le Hellard & Vidar M. Steen
30. Department of Psychology, University of Oslo, Oslo, Norway
    Erlend S. Dørum, Thomas Espeseth, Beathe Haatveit, Knut K. Kolskår, Nils Inge Landrø, Luigi A. Maglanoc, Linn B. Norbom, Geneviève Richard, Jaroslav Rokicki, Anne-Marthe Sanders, Kristine M. Ulrichsen & Lars T. Westlye
31. Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
    Erlend S. Dørum, Knut K. Kolskår, Geneviève Richard, Anne-Marthe Sanders & Kristine M. Ulrichsen
32. Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour Radboud University Medical Center, Nijmegen, The Netherlands
    Barbara Franke
33. Department of Neuromedicine and Movement Science Norwegian, University of Science and Technology, Trondheim, Norway
    Asta K. Håberg
34. Department of Radiology and Nuclear Medicine St. Olavs Hospital, Trondheim, Norway
    Asta K. Håberg
35. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
    Catharina A. Hartman
36. Clinical Neuropsychology section Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
    Dirk Heslenfeld & Jaap Oosterlaan
37. Department of Cognitive Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
    Dirk Heslenfeld
38. Department of Child and Adolescent Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
    Pieter J. Hoekstra
39. Center for Human Development, University of California, San Diego, USA
    Terry L. Jernigan
40. Department of Cognitive Science, University of California, San Diego, USA
    Terry L. Jernigan
41. Departments of Psychiatry and Radiology, University of California, San Diego, USA
    Terry L. Jernigan
42. Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
    Rune Jonassen
43. Department of Clinical Psychology Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
    Peter Kirsch
44. Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Mannheim, Germany
    Peter Kirsch
45. Department of Old Age Psychiatry and Psychotic Disorders Medical University of Lodz, Lodz, Poland
    Iwona Kłoszewska
46. Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
    Klaus-Peter Lesch & Georg C. Ziegler
47. Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine Sechenov First Moscow State Medical University, Moscow, Russia
    Klaus-Peter Lesch
48. Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS) Maastricht University, Maastricht, The Netherlands
    Klaus-Peter Lesch
49. Department of Psychiatry, Warneford Hospital University of Oxford, Oxford, UK
    Simon Lovestone
50. Department of Biomedicine, University of Bergen, Bergen, Norway
    Arvid Lundervold
51. Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
    Arvid Lundervold
52. Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
    Astri J. Lundervold
53. Department of Research and Education, Oslo University Hospital, Oslo, Norway
    Ulrik F. Malt
54. Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
    Patrizia Mecocci
55. CatoSenteret Rehabilitation Center Son, Oslo, Norway
    Jan Egil Nordvik
56. Departments of Radiation Sciences and Integrative Medical Biology, Umeå Center for Functional Brain Imaging Umeå University, Umeå, Sweden
    Lars Nyberg
57. Emma Children’s Hospital, Amsterdam UMC University of Amsterdam and Vrije Universiteit Amsterdam, Emma Neuroscience Group, Department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands
    Jaap Oosterlaan
58. Division of Molecular Neuroscience University of Basel, Basel, Switzerland
    Andreas Papassotiropoulos
59. Life Sciences Training Facility, Department Biozentrum University of Basel, Basel, Switzerland
    Andreas Papassotiropoulos
60. Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
    Karin Persson & Geir Selbæk
61. Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
    Karin Persson & Geir Selbæk
62. Department of Neurology, Institute of Clinical Medicine University of Eastern Finland, Kuopio, Finland
    Hilkka Soininen
63. Neurocenter, Neurology Kuopio University Hospital, Kuopio, Finland
    Hilkka Soininen
64. Dr. E. Martens Research Group for Biological Psychiatry, Department of Medical Genetics Haukeland University Hospital, Bergen, Norway
    Vidar M. Steen
65. 1st Department of Neurology Aristotle University of Thessaloniki, Thessaloniki, Greece
    Magda Tsolaki
66. UMR Inserm 1027, CHU Toulouse, UPS, Toulouse, France
    Bruno Vellas
67. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
    Lei Wang
68. Department of Neurobiology Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
    Eric Westman
69. District hospital Ansbach, Ansbach, Germany
    Mathias Zink
70. Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
    Göran Engberg, Sophie Erhardt, Lilly Schwieler, Anna Malmqvist, Mikael Hedberg & Funda Orhan
71. Neuroimmunology Unit, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
    Fredrik Piehl

Authors

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

Consortia

Karolinska Schizophrenia Project (KaSP)

• Lars Farde
• , Lena Flyckt
• , Göran Engberg
• , Sophie Erhardt
• , Helena Fatouros-Bergman
• , Simon Cervenka
• , Lilly Schwieler
• , Fredrik Piehl
• , Ingrid Agartz
• , Karin Collste
• , Pauliina Victorsson
• , Anna Malmqvist
• , Mikael Hedberg
• & Funda Orhan

Contributions

T.K. and L.T.W. conceived the study, T.K., N.T.D. and L.T.W. pre-processed all data in Freesurfer, N.T.D., M.J.L., C.L.B, L.B.N., L.T.W. and T.K. performed quality control of the data, T.K. performed the analysis with contributions from L.T.W. and D.v.d.M., and T.K., L.T.W., N.T.D., D.v.d.M. and O.A.A. contributed to interpretation of the results. All remaining authors were involved in data collection at various sites as well as cohort-specific tasks. T.K. and L.T.W. wrote the first draft of the paper and all authors contributed to and approved the final manuscript.

Corresponding authors

Correspondence toTobias Kaufmann or Lars T. Westlye.

Ethics declarations

Competing interests

Some authors received educational speaker’s honoraria from Lundbeck (O.A.A., A.B., T.E., M.Z., N.I.L.), Sunovion (O.A.A.), Shire (B.F.), Medice (B.F.), Otsuka (A.B., M.Z.), Janssen (A.B.), Roche (M.Z.), Ferrer (M.Z.), Trommsdorff (M.Z.) and Servier (M.Z.), all unrelated to this work. A.B. is a stockholder of Hoffmann-La Roche and has received consultant fees from Biogen Idec. S.L. is currently an employee of Janssen-Cilag, but contribution to this work was completed prior to this employment. E.A.H., E.G.C., M.K.B., P.S., and H.F.H. have received travel support, honoraria for advice and/or lecturing from Almirall (E.G.C.), Biogen Idec (E.G.C., H.F.H., M.K.B.), Sanofi-Genzyme (E.G.C., H.F.H., E.A.H.), Merck (E.G.C., H.F.H., E.A.H., P. S.), Novartis (E.G.C., H.F.H., M.K.B.), Roche (E.G.C., H.F.H.), Sanofi-Aventis (E.G.C., H.F.H.) and Teva (E.G.C., H.F.H.). E.G.C. anf H.F.H. have received unrestricted research grants from Novartis (E.G.C., H.F.H.), Biogen Idec (E.G.C.) and Sanofi-Genzyme (E.G.C.). G.P. has been the academic supervisor of a Roche collaboration grant (years 2015-2016) that funds his salary. None of the mentioned external parties had any role in the analysis, writing or decision to publish this work. All other authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Janine Bijsterbosch, Gagan Wig, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1

Outline of the main analysis pipeline.

Supplementary Figure 2 Validation of the prediction models confirmed validity of the models.

(a) The Pearson correlation between chronological age and predicted brain age estimated using 5-fold cross-validation within the training set (n=35,474) for each feature set. (b) The Pearson correlation between chronological age and predicted brain age estimated in the ten independent test samples. The test samples with cases and matched controls 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.

Supplementary Figure 3 Comparison of prediction models in data from the UK Biobank.

Our main analysis used xgboost models for estimating brain age. The figure compares predicted brain age (a) and brain age gaps (b) from xgboost to shrinkage linear models (slm) and to a free public brain age estimation approach (brainageR). Predicted brain age and resulting brain age gaps were highly correlated between approaches (Pearson r; analysis performed in n=23,602 healthy individuals from the UK Biobank; two-sided; all P < FDR; Benjamini-Hochberg). All brain age gaps account for age, age², sex, scanning site and Euler number.

Supplementary Figure 4 Comparison of models trained within site to models trained across site.

In the test sample (n=10,141 independent subjects), for each diagnosis and scanner, we trained a machine learning model on data from all available healthy controls acquired at a given scanner and predicted brain age on data from all cases collected at the respective scanner. Predicted brain age in cases from the within-site models correlated significantly with predicted brain age from the main models (mid column). Likewise, the resulting brain age gaps (right column) were significantly correlated between within-site models (accounted for age, age², sex and Euler number) and main models (accounted for age, age², sex, Euler number and scanning site), indicating that scanning-site independent models provide similar estimates of apparent aging patterns as the models built on cross-site imaging data. The figure reports Pearson r with two-sided p values, all P<FDR (Benjamini-Hochberg). Model performance (left column) is higher in the main models that use more data (see also Supplementary Fig. 5), and we have therefore used the across-site predictions for the main analysis.

Supplementary Figure 5 Impact of sample size on brain age prediction model performance.

Forty random subsets of data from 100, 500, 1000, 2000, 5000, 10000, and 20000 individuals were drawn and corresponding models cross-validated. In addition, results from the full sample (n=35,474) is shown. With increasing sample size, performance of the models increased, with less variation across runs. The whiskers in the boxplot extend from the hinge to the largest/smallest value no further than 1.5 * IQR from the hinge.

Supplementary Figure 6 Case-control differences on principal components in the test sets.

This analysis quantifies variance in the features in relation to case-control differences. We conducted a PCA in each test sample, yielding components that capture the variance across the 1118 brain imaging features, and assessed case-control differences in models accounting for age, age², sex, scanning site and Euler number. Except for MDD and SZRISK, all test samples showed significant case-control differences (black boxes indicate p < FDR; Benjamini-Hochberg; two-sided). The test samples with cases and matched controls 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.

Supplementary Figure 7 Meta-analysis confirmed mega-analysis results.

Individual study sample sizes are depicted in Supplementary Table 2. All Cohen’s d effect sizes for the effect of group accounted for age, age², sex and Euler number. Further, Cohen’s d for all cohorts that were collected at multiple sites also accounted for scanning site. For each sample, the center of the square reflects the estimated effect size and the error bars depict the 95% confidence interval.

Supplementary Figure 8 Replication of results in a subset of 40,301 individuals following more stringent quality control.

(a) Replication of group effects (for comparison, see Fig. 2a). The test samples in this sanity check analysis comprised n=770 ASD / n=770 HC, n=654 ADHD / n=654 HC, n=80 SZRISK / n=80 HC, n=931 SZ / n=931 HC, n=251 PSYMIX / n=251 HC, n=398 BD / n=398 HC, n=219 MS / n=219 HC, n=177 MDD / n=177 HC, n=803 MCI / n=803 HC, n=579 DEM / n=579 HC. Cohen’s d effect sizes (pooled standard deviation units) and two-sided P-values are provided. (b) Replication of spatial brain age gap patterns (for comparison, see Fig. 2b). Colours indicate Cohen’s d effect sizes for group comparisons. Sample size as specified in panel a. (c) Replication of interaction effect pattern (for comparison, see Fig. 2c). Sample size as specified in panel a yet excluding HC; n=4862 independent subjects. Only significant (p<FDR; Benjamini-Hochberg) effects are shown. (d) Replication of associations with clinical and cognitive scores (for comparison, see Fig. 2d). 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. Black box indicates significance after correction for multiple comparison (p<FDR; Benjamini-Hochberg; two-sided). Sample size comprised n=340 SZ for GAFsymptom, n=234 SZ for GAFfunction, n=564 SZ for PANSSpositive, n=549 SZ for PANSSnegative, n=157 MS for EDSS, n=752 MCI and n=535 DEM for MMSE.

Supplementary Figure 9 Clustering of regional brain age gap patterns reveals disorder specific patterns.

The figure indicates hierarchical clustering of the Spearman correlation matrix of effect sizes from Fig. 2b (eight estimates of Cohen d per diagnosis (full brain, occipital, frontal, temporal, parietal, cingulate, insula, subcortical)).

Supplementary Figure 10 Results from 1260 repeated measures ANOVAs confirm group x region interaction effects in brain age patterns.

Strongest effects were observed between MS and SZ, MS and PSYMIX, MS and BD, MDD and MS, as well as DEM and SZ, suggesting divergent aging patterns in these disorders. The samples comprised n=925 ASD, n=725 ADHD, n=94 SZRISK, n=1110 SZ, n=300 PSYMIX, n=459 BD, n=254 MS, n=208 MDD, n=974 MCI, n=739 DEM; in total n=5788 independent subjects. Significant (p<FDR; Benjamini-Hochberg) effects are shown in white text colour.

Supplementary Figure 11 Scatter plots for associations with clinical and cognitive data in patient groups.

Subplots correspond to the associations reported in Fig. 2d. The analysis was performed in patient data only (excluding controls). Clinical data was available for SZ (GAFsymptom n=389, GAFfunction n=269, PANSSpositive n=646, PANSSnegative n=626), MS (EDSS n=195) and MCI/DEM (MMSE n=907 MCI and n=686 DEM). Shaded areas reflect confidence intervals. 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. For assessment of significance, see Fig. 2d.

Supplementary Figure 12 Cross trait LD-score regression (Rg) between brain age gaps and psychiatric disorders.

GWAS on brain age gaps was performed in data from n=20,170 healthy adult individuals with European ancestry. One association (ADHD with cingulate brain age gap) survived multiple comparison testing (P<FDR; Benjamini-Hochberg, two-sided).

Supplementary Figure 13 Validation of the quality assurance pipeline.

A sample of 1521 individuals was manually quality controlled and data from individuals marked for in- or exclusion. This method was compared against Euler numbers (a-b) and against automated decisions based on data deceeding 3 (c) or 1 (d) standard deviation in Euler numbers. Together, these results confirm that Euler number is sensitive to data quality and can be used for automated quality control. We used 3 SD as the criterion for the main analysis, allowing us to exploit the full sample size at the risk of missing a proportion of outliers. To mitigate this risk, we also provide a re-analysis of the data in the supplement using 1 SD as the exclusion criterion. Panels a-b report t-statistics and two-sided P-values from one t-test per hemisphere. The whiskers in the boxplot extend from the hinge to the largest/smallest value no further than 1.5 * IQR from the hinge. The horizontal mid line depicts the median.

Supplementary information

Supplementary Figures 1–13 and Supplementary Tables 1 and 3

Supplementary Figures 1–13 and Supplementary Tables 1 and 3.

Reporting Summary

Supplementary Table 2

Summary of group size, age and sex for each cohort.

Supplementary Table 4

Sex specific brain age prediction models transferred well across sexes. In the training sample, 1008 out of 1118 features showed a significant sex difference (two-sided; Bonferroni level p<4e-05) in models accounting for age, age², scanning site and Euler number. Due to this, and also due to the possibility of subtle sex differences in age trajectories and mechanisms of brain aging (for example, related to hormones, etc.), we modeled male and female brains separately for predicting brain age in the main analysis. To investigate if lifespan changes in the brain are similar between sexes, we compared the prediction models. Age of all test participants predicted using the male model correlated strongly with their age predicted using the female model (Pearson r=0.99). In addition, the table illustrates the correlation (Pearson r) of predicted brain age with chronological age for different combinations of training models and test data. Together, these results suggest that the incorporated imaging features follow similar age trajectories across sexes.

Supplementary Table 5

Assessment of age by diagnosis interaction in the brain age gaps of ASD (n=925 ASD, n=925 HC) and ADHD (n=725 ADHD, n=725 HC). The linear models accounted for age, age², sex, scanning site and Euler number. None of the interaction effects survived correction for multiple comparison (FDR; Benjamini-Hochberg; two-sided).

Supplementary Table 6

Significant loci from conjunctional FDR analysis reflecting overlap between brain age gaps and the respective disorders. The gene column reflects the gene closest to the significant SNP as identified via the Ensembl Variable Effect Predictor48, unless the closest gene is more than 5000 bp away in which case no annotation is provided.

Rights and permissions

About this article

Cite this article

Kaufmann, T., van der Meer, D., Doan, N.T. et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain.Nat Neurosci 22, 1617–1623 (2019). https://doi.org/10.1038/s41593-019-0471-7

Download citation

• Received: 28 August 2018
• Accepted: 22 July 2019
• Published: 24 September 2019
• Issue Date: October 2019
• DOI: https://doi.org/10.1038/s41593-019-0471-7