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Identification of neurobehavioural symptom groups based on shared brain mechanisms

Nature Human Behaviour volume 3, pages 1306–1318 (2019)Cite this article

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Abstract

Most psychopathological disorders develop in adolescence. The biological basis for this development is poorly understood. To enhance diagnostic characterization and develop improved targeted interventions, it is critical to identify behavioural symptom groups that share neural substrates. We ran analyses to find relationships between behavioural symptoms and neuroimaging measures of brain structure and function in adolescence. We found two symptom groups, consisting of anxiety/depression and executive dysfunction symptoms, respectively, that correlated with distinct sets of brain regions and inter-regional connections, measured by structural and functional neuroimaging modalities. We found that the neural correlates of these symptom groups were present before behavioural symptoms had developed. These neural correlates showed case–control differences in corresponding psychiatric disorders, depression and attention deficit hyperactivity disorder in independent clinical samples. By characterizing behavioural symptom groups based on shared neural mechanisms, our results provide a framework for developing a classification system for psychiatric illness that is based on quantitative neurobehavioural measures.

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Fig. 1: Results of the first msCCA-regression analysis showing relationships between anxiety/depression psychiatric symptoms and neuroimaging measures in the IMAGEN sample.
Fig. 2: Results of the second msCCA-regression analyses showing relationships between executive dysfunction symptoms and neuroimaging measures in the IMAGEN sample, following the removal of the first canonical relation.
Fig. 3: Longitudinal analysis of canonical correlates.
Fig. 4: Differences in the grey matter correlates of anxiety/depression and executive dysfunction psychiatric symptoms between cases and controls for a range of psychiatric illnesses.

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Data availability

The IMAGEN data used in this investigation will be made available on reasonable request to the corresponding author. All other data are available on reasonable request to the appropriate study leader.

Code availability

The core code used to run the analyses reported in this study are available as Supplementary Software. The supporting code can be found at: https://github.com/alexjamesing/mscca-regression-code.

References

  1. Kessler, R. C. et al. Age of onset of mental disorders: a review of recent literature. Curr. Opin. Psychiat. 20, 359–364 (2007).

    Article  Google Scholar 

  2. Giedd, J. N. et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 2, 861–863 (1999).

    Article  CAS  PubMed  Google Scholar 

  3. Steinberg, L. Risk taking in adolescence: new perspectives from brain and behavioral science. Curr. Dir. Psychol. Sci. 16, 55–59 (2007).

    Article  Google Scholar 

  4. Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. USA 101, 8174–8179 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 23, 28–38 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Insel, T. et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    Article  PubMed  Google Scholar 

  7. Lahey, B. B. et al. Is there a general factor of prevalent psychopathology during adulthood? J. Abnorm. Psychol. 121, 971–977 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang, X. et al. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 113, E6544 (2016).

    Article  CAS  Google Scholar 

  9. Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Witten, D. M., Tibshirani, R. & Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9, 3003 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Kettenring, J. R. Canonical analysis of several sets of variables. Biometrika 58, 433–451 (1971).

    Article  Google Scholar 

  14. Goodman, R., Ford, T., Richards, H., Gatward, R. & Meltzer, H. The development and well-being assessment: description and initial validation of an integrated assessment of child and adolescent psychopathology. J. Child Psychol. Psychiat. 41, 645–655 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007).

    Article  PubMed  Google Scholar 

  16. Smith, S. M. et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505 (2006).

    Article  PubMed  Google Scholar 

  17. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ashburner, J. & Friston, K. J. Voxel-based morphometry—the methods. Neuroimage 11, 805–821 (2000).

    Article  CAS  PubMed  Google Scholar 

  19. Meinshausen, N. & Bühlmann, P. Stability selection. J. R. Stat. Soc. Ser. B 72, 417–473 (2010).

    Article  Google Scholar 

  20. Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA major depressive disorder working group. Mol. Psychiat. 22, 900–909 (2016).

    Article  CAS  Google Scholar 

  21. Chen, G. et al. Disorganization of white matter architecture in major depressive disorder: a meta-analysis of diffusion tensor imaging with tract-based spatial statistics. Sci. Rep. 6, 21825 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Guo, W. et al. Increased cerebellar-default-mode-network connectivity in drug-naive major depressive disorder at rest. Medicine 94, e560 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Carmona, S. et al. Global and regional gray matter reductions in ADHD: a voxel-based morphometric study. Neurosci. Lett. 389, 88–93 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Power, J. D., Fair, D. A., Schlaggar, B. L. & Petersen, S. E. The development of human functional brain networks. Neuron 67, 735–748 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Krueger, R. F., Caspi, A., Moffitt, T. E. & Silva, P. A. The structure and stability of common mental disorders (DSM-III-R): a longitudinal-epidemiological study. J. Abnorm. Psychol. 107, 216 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Diedenhofen, B. & Musch, J. Cocor: a comprehensive solution for the statistical comparison of correlations. PloS One 10, e0121945 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Dunn, O. J. & Clark, V. Correlation coefficients measured on the same individuals. J. Am. Stat. Assoc. 64, 366–377 (1969).

    Article  Google Scholar 

  28. Whelan, R. et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512, 185–189 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lahey, B. B., Van Hulle, C. A., Singh, A. L., Waldman, I. D. & Rathouz, P. J. Higher-order genetic and environmental structure of prevalent forms of child and adolescent psychopathology. Arch. Gen. Psychiat. 68, 181–189 (2011).

    Article  PubMed  Google Scholar 

  30. Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s world mental health survey initiative. World Psychiat. 6, 168–176 (2007).

    Google Scholar 

  31. Mayberg, H. S. Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. Br. Med Bull. 65, 193–207 (2003).

    Article  PubMed  Google Scholar 

  32. Witelson, S. F. Hand and sex differences in the isthmus and genu of the human corpus callosum: a postmortem morphological study. Brain 112, 799–835 (1989).

    Article  PubMed  Google Scholar 

  33. Tham, M. W., San Woon, P., Sum, M. Y., Lee, T. & Sim, K. White matter abnormalities in major depression: evidence from post-mortem, neuroimaging and genetic studies. J. Affect Disord. 132, 26–36 (2011).

    Article  PubMed  Google Scholar 

  34. Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. 98, 676–682 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Buckner, R. L., Andrews‐Hanna, J. R. & Schacter, D. L. The brain’s default network. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).

    Article  PubMed  Google Scholar 

  36. Ray, R. D. et al. Individual differences in trait rumination and the neural systems supporting cognitive reappraisal. Cogn. Affect. Behav. Neurosci. 5, 156–168 (2005).

    Article  PubMed  Google Scholar 

  37. Stoodley, C. J. The cerebellum and cognition: evidence from functional imaging studies. Cerebellum 11, 352–365 (2012).

    Article  PubMed  Google Scholar 

  38. Guggenmos, M. et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl. Psychiat. 7, 1279–1286 (2017).

    Article  Google Scholar 

  39. Hibar, D. P. et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA bipolar disorder working group. Mol. Psychiat. 23, 932–942 (2017).

    Article  CAS  Google Scholar 

  40. McGorry, P. D., Hickie, I. B., Yung, A. R., Pantelis, C. & Jackson, H. J. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust. N. Z. J. Psychiatry 40, 616–622 (2006).

    Article  PubMed  Google Scholar 

  41. Biswal, B., Zerrin Yetkin, F., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    Article  CAS  PubMed  Google Scholar 

  42. Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiat. 15, 1128–1139 (2010).

    Article  CAS  Google Scholar 

  43. Goodman, R. The strengths and difficulties questionnaire: a research note. J. Child Psychol. Psychiat. 38, 581–586 (1997).

    Article  CAS  PubMed  Google Scholar 

  44. Vulser, H. et al. Subthreshold depression and regional brain volumes in young community adolescents. J. Am. Acad. Child Adolesc. Psychiat. 54, 832–840 (2015).

    Article  Google Scholar 

  45. Kurth, F. & Lüders, E. VBM8. http://www.neuro.uni-jena.de/vbm/download/ (2010).

  46. The FIL Methods Group. SPM8. https://www.fil.ion.ucl.ac.uk/spm/software/spm8/ (2009).

  47. Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851 (2005).

    Article  PubMed  Google Scholar 

  48. Grellmann, C. et al. Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data. Neuroimage 107, 289–310 (2015).

    Article  PubMed  Google Scholar 

  49. Jones, D. K. et al. Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time. Hum. Brain Mapp. 15, 216–230 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S219 (2004).

    Article  Google Scholar 

  51. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).

    Article  PubMed  Google Scholar 

  52. Pruim, R. H. et al. ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 112, 267–277 (2015).

    Article  PubMed  Google Scholar 

  53. Pruim, R. H., Mennes, M., Buitelaar, J. K. & Beckmann, C. F. Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. Neuroimage 112, 278–287 (2015).

    Article  PubMed  Google Scholar 

  54. Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).

    Article  PubMed  Google Scholar 

  55. Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).

    Article  PubMed  Google Scholar 

  56. Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hotelling, H. Relations between two sets of variates. Biometrika 28, 321–377 (1936).

    Article  Google Scholar 

  58. Witten, D. M. & Tibshirani, R. J. Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8, 1–27 (2009).

    Article  CAS  Google Scholar 

  59. Parkhomenko, E., Tritchler, D. & Beyene, J. Sparse canonical correlation analysis with application to genomic data integration. Stat. Appl. Genet. Mol. Biol. 8, 1–34 (2009).

    Article  Google Scholar 

  60. Gifi, A. Nonlinear Multivariate Analysis (Wiley, 1990).

  61. Jenkins, L. M. et al. Shared white matter alterations across emotional disorders: a voxel-based meta-analysis of fractional anisotropy. NeuroImage Clin. 12, 1022–1034 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiat. 72, 305–315 (2015).

    Article  Google Scholar 

  63. Everitt, B. S. & Dunn, G. Applied Multivariate Data Analysis Vol 2 (Arnold, 2001).

  64. Timm, N. H. & Carlson, J. E. Part and bipartial canonical correlation analysis. Psychometrika 41, 159–176 (1976).

    Article  Google Scholar 

  65. O’Brien, L. M. et al. Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods. Psychiatry Res. Neuroimag. 193, 113–122 (2011).

    Article  Google Scholar 

  66. Pell, G. S. et al. Selection of the control group for VBM analysis: influence of covariates, matching and sample size. Neuroimage 41, 1324–1335 (2008).

    Article  PubMed  Google Scholar 

  67. Voevodskaya, O. et al. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Front. Aging Neurosci. 6, 264 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Van Den, Wollenberg & Arnold, L. Redundancy analysis: an alternative for canonical correlation analysis. Psychometrika 42, 207–219 (1977).

    Article  Google Scholar 

  69. Stewart, D. & Love, W. A general canonical correlation index. Psychol. Bull. 70, 160–163 (1968).

    Article  CAS  PubMed  Google Scholar 

  70. Monteiro, J. M., Rao, A., Shawe-Taylor, J. & Mourão-Miranda, J. Alzheimer’s Disease Initiative. A multiple hold-out framework for sparse partial least squares. J. Neurosci. Methods 271, 182–194 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Holmes, A. P., Blair, R. C., Watson, G. & Ford, I. Nonparametric analysis of statistic images from functional mapping experiments. J. Cereb. Blood Flow. Metab. 16, 7–22 (1996).

    Article  CAS  PubMed  Google Scholar 

  72. Westfall, P. H. & Troendle, J. F. Multiple testing with minimal assumptions. Biometrical J. 50, 745–755 (2008).

    Article  Google Scholar 

  73. Westfall, P. H. & Young, S. S. Resampling-based Multiple Testing: Examples and Methods for P-value Adjustment Vol. 279 (Wiley, 1993).

  74. Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning. Springer Series in Statistics, Vol. 1 (Springer, 2001).

  75. Aebi, M. et al. The use of the development and well-being assessment (DAWBA) in clinical practice: a randomized trial. Eur. Child Adolesc. Psychiat. 21, 559–567 (2012).

    Article  Google Scholar 

  76. Steinberg, L. Cognitive and affective development in adolescence. Trends Cogn. Sci. Regul. Ed. 9, 69–74 (2005).

    Article  Google Scholar 

  77. Schmaal, L. et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA major depressive disorder working group. Mol. Psychiat. 21, 806 (2016).

    Article  CAS  Google Scholar 

  78. Rimol, L. M. et al. Cortical volume, surface area, and thickness in schizophrenia and bipolar disorder. Biol. Psychiat. 71, 552–560 (2012).

    Article  PubMed  Google Scholar 

  79. van Erp, T. G. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiat. 21, 547 (2016).

    Article  Google Scholar 

  80. von Rhein, D. et al. The NeuroIMAGE study: a prospective phenotypic, cognitive, genetic and MRI study in children with attention-deficit/hyperactivity disorder. design and descriptives. Eur. Child Adolesc. Psychiat. 24, 265–281 (2015).

    Article  Google Scholar 

  81. Hoogman, M. et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiat. 4, 310–319 (2017).

    Article  Google Scholar 

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Acknowledgements

This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (reinforcement-related behaviour in normal brain function and psychopathology; grant no. LSHM-CT-2007-037286), the Horizon 2020-funded ERC Advanced Grant STRATIFY (brain-network-based stratification of reinforcement-related disorders; grant no. 695313), ERANID (understanding the interplay between cultural, biological and subjective factors in drug use pathways; grant no. PR-ST-0416-10004), BRIDGET (JPND: brain imaging, cognition, dementia and next generation genomics; grant no. MR/N027558/1), the Human Brain Project (HBP SGA 2, grant no. 785907), the FP7 project MATRICS (grant no. 603016), the Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions; grant no. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (BMBF grant nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B, 01ZX1314G, 01GS08147), the Deutsche Forschungsgemeinschaft (DFG grant nos. SM 80/7-2, SFB 940/2), the Medical Research Foundation and Medical Research Council (grant nos. MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH)-funded ENIGMA (grant nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from: ANR (project AF12-NEUR0008-01-WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (grant no. 16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; grant no. RO1 MH085772-01A1) and NIH Consortium grant no. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. A.M. gratefully acknowledges funding from the Netherlands Organization for Scientific Research via the Vernieuwingsimpuls VIDI programme (grant no. 016.156.415). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. A list of authors and affiliations for the IMAGEN Consortium appears in the Supplementary Note.

Authors and Affiliations

  1. Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK

    Alex Ing, Congying Chu, Nicole Tay, Francesca Biondo, Gabriel Robert, Tianye Jia, Sylvane Desrivières, Erin Burke Quinlan, Edward D. Barker & Gunter Schumann

  2. Neuroimaging, Max Planck Institute of Psychiatry, Munich, Germany

    Philipp G. Sämann & Elisabeth Binder

  3. Behaviour and Basal Ganglia Research Unit, University of Rennes, Rennes, France

    Gabriel Robert

  4. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Njmegen, The Netherlands

    Thomas Wolfers, Jan Buitelaar & Andre Marquand

  5. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Tobias Banaschewski, Yvonne Grimmer, Sabina Millenet & Frauke Nees

  6. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

    Arun L. W. Bokde

  7. Systems Neuroscience, University Medical Centre Hamburg−Eppendorf, Hamburg, Germany

    Uli Bromberg, Christian Büchel & Tahmine Fadai

  8. Department of Psychology, Stanford University, Stanford, CA, USA

    Christian Büchel

  9. Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience King’s College London, London, UK

    Patricia Conrod

  10. Department of Psychiatry, CHU Ste Justine Hospital, Université de Montréal, Montreal, Quebec, Canada

    Patricia Conrod

  11. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Herta Flor & Frauke Nees

  12. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

    Herta Flor

  13. NeuroSpin, CEA, Université Paris−Saclay, Gif-sur-Yvette, France

    Vincent Frouin & Dimitri Papadopoulos Orfanos

  14. Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA

    Hugh Garavan & Philip A. Spechler

  15. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK

    Penny Gowland

  16. Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

    Andreas Heinz, Ilya M. Veer & Henrik Walter

  17. Biomedical Magnetic Resonance, Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany

    Bernd Ittermann

  18. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Charité, Humboldt University, Berlin, Germany

    Viola Kappel & Betteke van Noort

  19. Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 Neuroimaging & Psychiatry, University Paris Saclay, University Paris Descartes; DIgiteo-Labs, Gif-sur-Yvette; and Maison de Solenn, Paris, France

    Jean-Luc Martinot

  20. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Andreas Meyer-Lindenberg

  21. Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 Neuroimaging & Psychiatry, University Paris Saclay, University Paris Descartes; DIgiteo-Labs, Gif-sur-Yvette, France

    Marie-Laure Paillère Martinot

  22. AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France

    Marie-Laure Paillère Martinot

  23. Department of Social and Health Care, Psychosocial Services Adolescent Outpatient Clinic, Lahti, Finland

    Jani Penttilä

  24. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany

    Luise Poustka

  25. Department of Psychiatry and Neuroimaging Centre, Technische Universität Dresden, Dresden, Germany

    Michael N. Smolka & Maren Struve

  26. Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Argyris Stringaris

  27. Mood Brain and Development Unit, National Institute of Mental Health/NIH, Bethesda, MD, USA

    Argyris Stringaris

  28. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

    Robert Whelan

  29. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    Ole A. Andreassen

  30. NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Ole A. Andreassen & Ingrid Agartz

  31. Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway

    Ingrid Agartz

  32. Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden

    Ingrid Agartz

  33. Institut National de la Santé et de la Recherche Médicale, UMR 992 INSERM, CEA, Faculté de médecine, Université Paris-Sud, Université Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France

    Hervé Lemaitre

  34. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Edward D. Barker

  35. Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK

    John Ashburner

  36. Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Trevor W. Robbins

  37. PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin and Leibniz Institute for Neurobiology, Magdeburg, Germany

    Gunter Schumann

  38. PONS Research Group, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China

    Gunter Schumann

  39. PONS Research Group, Dept. of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin, Germany

    Gunter Schumann

  40. PONS Research Group, Leibniz Institute for Neurobiology, Magdeburg, Germany

    Gunter Schumann

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  1. Alex Ing
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Consortia

IMAGEN Consortium

Contributions

A.I., C.C., I.M.V., P.G.S., H.L., T.J. and G.R. preprocessed the data. A.I. and P.G.S. analysed the data. A.I., G.S., F.B. and P.G.S. wrote the manuscript. A.I., G.S., T.W.R., A.M., J.A. and E.B. conceptualized the study. N.T., E.B.Q., T.W., S.D., T.B., A.L.W.B., U.B., C.B., P.C., T.F., H.F., V.F., H.G., P.S., P.G., Y.G., A.H., B.I., V.K., J.-L.M., A.M.-L., F.N., B.v.N., D.P.O., M.-L.P.M., S.M., J.P., L.P., M.S., A.S., M.N.S., H.W., R.W., O.A.A., I.A., E.D.B. and J.B. collected data. A.I. and N.T. prepared the figures. All authors revised the manuscript.

Corresponding author

Correspondence to Gunter Schumann.

Ethics declarations

Competing interests

T.B. served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Shire. He received conference support or a speaker’s fee from Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press. The present work is unrelated to the above grants and relationships. E.D.B. received honoraria from General Electric Healthcare for teaching on scanner programming courses and acts as a consultant for IXICO. O.A.A. received a speaker’s honorarium from Lundbeck. G.R. received financial support from scientific meetings (Janssen & Janssen, Otsuka−Lundbeck). A.M.-L. received consultant fees from Boehringer Ingelheim, Brainsway, Elsevier, Lundbeck Int. Neuroscience Foundation and Science Advances. The other authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Mary Elizabeth Sutherland.

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Supplementary Information

Supplementary Information

Supplementary Figs. 1−10, and Tables 1−9, and Supplementary Note (containing the list of authors for the IMAGEN Consortium).

Reporting Summary

Supplementary Software

This msCCA script forms the basis of the msCCA-regression approach used in the present investigation.

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Ing, A., Sämann, P.G., Chu, C. et al. Identification of neurobehavioural symptom groups based on shared brain mechanisms. Nat Hum Behav 3, 1306–1318 (2019). https://doi.org/10.1038/s41562-019-0738-8

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