Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Mapping gene transcription and neurocognition across human neocortex

Nature Human Behaviour volume 5, pages 1240–1250 (2021)Cite this article

Subjects

Abstract

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here, we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial–dorsolateral gradient of gene assemblies that separate affective and perceptual domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation and intrinsic network affiliation. In addition, this molecular-psychological signature strengthens over neurodevelopment and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Relating gene expression to functional association.
Fig. 2: Gene sets underlying psychological processes.
Fig. 3: The gene expression–functional association gradient is organized around microscale and macroscale hierarchies.
Fig. 4: The molecular signature of psychological function strengthens with development.

Similar content being viewed by others

Data availability

The AHBA is available at https://human.brain-map.org/static/download. The Neurosynth database is available at https://neurosynth.org/. The HCP database is available at https://db.humanconnectome.org/data/projects/HCP_1200. The BrainSpan database is available at https://www.brainspan.org/static/download.html. Processed data as used in this report are available at https://github.com/netneurolab/hansen_genescognition. BrainMap data are available upon reasonable request to B.M.

Code availability

Code used to conduct the reported analyses is available at https://github.com/netneurolab/hansen_genescognition.

References

  1. Betzel, R. F. & Bassett, D. S. Multi-scale brain networks. NeuroImage 160, 73–83 (2017).

    Article  PubMed  Google Scholar 

  2. Tripathy, S. J. et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol. 13, e1005814 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotech. 34, 199–203 (2016).

    Article  CAS  Google Scholar 

  4. Lee, K. F. H., Soares, C., Thivierge, J.-P. & Béïque, J.-C. Correlated synaptic inputs drive dendritic calcium amplification and cooperative plasticity during clustered synapse development. Neuron 89, 784–799 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Frémaux, N. & Gerstner, W. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circuits 9, 85 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Blankenship, A. G. & Feller, M. B. Mechanisms underlying spontaneous patterned activity in developing neural circuits. Nat. Rev. Neurosci. 11, 18–29 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Suárez, L. E., Markello, R. D., Betzel, R. F. & Misic, B.Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24, 302–315 (2020).

    Article  PubMed  Google Scholar 

  9. Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    Article  PubMed  Google Scholar 

  10. Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Richiardi, J. et al. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl Acad. Sci. USA 113, 1435–1440 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fulcher, B. D., Murray, J. D., Zerbi, V. & Wang, X.-J. Multimodal gradients across mouse cortex. Proc. Natl Acad. Sci. USA 116, 4689–4695 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Betzel, R. F. et al. Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nat. Biomed. Eng. 3, 902–916 (2019).

    Article  PubMed  Google Scholar 

  15. Vértes, P. E. et al. Gene transcription profiles associated with intra-modular and inter-modular hubs in human fMRI networks. Phil. Trans. R. Soc. Lond. B Biol. Sci. 371, 735–769 (2016).

    Article  CAS  Google Scholar 

  16. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D.Large-scale automated synthesis of human functional neuroimaging data. Nat. Meth. 8, 665–670 (2011).

    Article  CAS  Google Scholar 

  17. Fox, P. T. & Lancaster, J. L. Mapping context and content: the BrainMap model. Nat. Rev. Neurosci. 3, 319–321 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Gorgolewski, K. J. et al. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform. 9, 8 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dockès, J. et al. NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife 9, e53385 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurodevelopmental disorders. Nat. Commun. 11, 3358 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA 113, 9105–9110 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Váša, F. et al. Conservative and disruptive modes of adolescent change in human brain functional connectivity. Proc. Natl Acad. Sci. USA 117, 3248–3253 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251–1259 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Arnatkevičiūtė, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage 189, 353–367 (2019).

    Article  PubMed  Google Scholar 

  28. Fox, A. S., Chang, L. J., Gorgolewski, K. J. & Yarkoni, T. Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data. Preprint at bioRxiv https://doi.org/10.1101/012310 (2014).

  29. Poldrack, R. A. et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform. 5, 17 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  31. Cammoun, L. et al. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012).

    Article  PubMed  Google Scholar 

  32. McIntosh, A. R., Bookstein, F. L., Haxby, J. V. & Grady, C. L. Spatial pattern analysis of functional brain images using partial least squares. NeuroImage 3, 143–157 (1996).

    Article  CAS  PubMed  Google Scholar 

  33. Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage 56, 455–475 (2011).

    Article  PubMed  Google Scholar 

  34. McIntosh, A. R. & Mišić, B. Multivariate statistical analyses for neuroimaging data. Annu. Rev. Psychol. 64, 499–525 (2013).

    Article  PubMed  Google Scholar 

  35. Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Markello, R. & Misic, B. Comparing spatially-constrained null models for parcellated brain maps. Preprint at bioRxiv https://doi.org/10.1101/2020.08.13.249797 (2020).

  37. Burt, J. B., Helmer, M., Shinn, M., Anticevic, A. & Murray, J. D.Generative modeling of brain maps with spatial autocorrelation. NeuroImage 220, 117038 (2020).

    Article  PubMed  Google Scholar 

  38. Fulcher, B. D, Arnatkevičiūtė, A. & Fornito, A. Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data. Preprint at bioRxiv https://doi.org/10.1101/2020.04.24.058958 (2020).

  39. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  41. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. McKenzie, A. T. et al. Brain cell type specific gene expression and co-expression network architectures. Sci. Rep. 8, 8868 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    Article  PubMed  Google Scholar 

  46. Wang, X.-J.Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat. Rev. Neurosci. 21, 169–178 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Glasser, M. F. & Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wagstyl, K., Ronan, L., Goodyer, I. M. & Fletcher, P. C. Cortical thickness gradients in structural hierarchies. NeuroImage 111, 241–250 (2015).

    Article  PubMed  Google Scholar 

  49. Huntenburg, J. M. et al. A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex. Cereb. Cortex 27, 981–997 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Jones, E. G. & Powell, T. P. S. An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93, 793–820 (1970).

    Article  CAS  PubMed  Google Scholar 

  51. Mesulam, M.-M. From sensation to cognition. Brain 121, 1013–1052 (1998).

    Article  PubMed  Google Scholar 

  52. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Van Der Maaten, L., Postma, E. & Van den Herik, J. Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10, 66–71 (2009).

    Google Scholar 

  55. Von Economo, C. & Koskinas, G. N. Die Cytoarchitektonik der Hirnrinde des Erwachsenen Menschen (Springer, 1925).

  56. Mesulam, M.-M. Principles of Behavioral and Cognitive Neurology 2nd edn (Oxford Univ. Press, 2000).

  57. Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fox, P. T. et al. BrainMap taxonomy of experimental design: description and evaluation. Hum. Brain Mapp. 25, 185–198 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Vanasse, T. J. et al. BrainMap VBM: an environment for structural meta-analysis. Hum. Brain Mapp. 39, 3308–3325 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Laird, A. R., Lancaster, J. J. & Fox, P. T. BrainMap. Neuroinformatics 3, 65–77 (2005).

    Article  PubMed  Google Scholar 

  61. Alexander-Bloch, A. F. et al. Imaging local genetic influences on cortical folding. Proc. Natl Acad. Sci. USA 117, 7430–7436 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Forest, M. et al. Gene networks show associations with seed region connectivity. Hum. Brain Mapp. 38, 3126–3140 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Krienen, F. M., Yeo, B. T. T., Ge, T., Buckner, R. L. & Sherwood, C. C. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Proc. Natl Acad. Sci. USA 113, E469–E478 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648–665 (2013).

    Article  PubMed  Google Scholar 

  65. Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O. & Betzel, R. F. Subject identification using edge-centric functional connectivity. Preprint at bioRxiv https://doi.org/10.1101/2020.09.13.291898 (2020).

  67. Vogel, J. W. et al. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nat. Commun. 11, 960 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mišić, B. & Sporns, O. From regions to connections and networks: new bridges between brain and behavior. Curr. Opin. Neurobiol. 40, 1–7 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Kirschner, M. et al. Latent clinical-anatomical dimensions of schizophrenia. Schizophr. Bull. 46, 1426–1438 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Amor, S., Puentes, F., Baker, D. & Van Der Valk, P. Inflammation in neurodegenerative diseases. Immunology 129, 154–169 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Shafiei, G. et al. Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biol. Psychiatry 87, 727–735 (2020).

    Article  CAS  PubMed  Google Scholar 

  72. Bush, G., Luu, P. & Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn. Sci. 4, 215–222 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  74. Patania, A. et al. Topological gene expression networks recapitulate brain anatomy and function. Network Neurosci. 3, 744–762 (2019).

    Article  Google Scholar 

  75. Miller, J. A. et al. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-seq. BMC Genomics 15, 154 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540–551 (2018).

    Article  PubMed  Google Scholar 

  77. Eckart, C. & Young, G. The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936).

    Article  Google Scholar 

  78. Kovacevic, N., Abdi, H., Beaton, D. & McIntosh, A. R. in New Perspectives in Partial Least Squares and Related Methods 159–170 (Springer, 2013).

  79. Helmer, M. et al. On stability of canonical correlation analysis and partial least squares with application to brain–behavior associations. Preprint at bioRxiv https://doi.org/10.1101/2020.08.25.265546 (2020).

  80. Hotelling, H. The most predictable criterion. J. Educ. Psychol. 26, 139–142 (1935).

    Article  Google Scholar 

  81. McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23, S250–S263 (2004).

    Article  PubMed  Google Scholar 

  82. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Essen, D. C. Van et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  84. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).

    Article  PubMed  Google Scholar 

  85. Werling, D. M. et al. Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex. Cell Rep. 31, 107489 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank V. Bazinet, L. Suarez, G. Shafiei, B. Vazquez-Rodriguez and Z.-Q. Liu for comments and suggestions on the manuscript. This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN 017-04265) and Canada Research Chairs Program. J.W.V. was funded by NIH grant T32MH019112. D.B. was supported by the Healthy Brains for Healthy Lives initiative (Canada First Research Excellence Fund), CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research), Google (Research Award) and NIH grant R01AG068563A. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada

    Justine Y. Hansen, Ross D. Markello, Danilo Bzdok & Bratislav Misic

  2. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Jacob W. Vogel & Jakob Seidlitz

  3. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz

  4. Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada

    Danilo Bzdok

  5. Mila, Quebec Artificial Intelligence Institute, Montréal, Québec, Canada

    Danilo Bzdok

Authors
  1. Justine Y. Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ross D. Markello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacob W. Vogel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jakob Seidlitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Danilo Bzdok
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bratislav Misic
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.Y.H. and B.M. conceived of the study idea. J.Y.H. performed the formal analysis with contributions from R.D.M. R.D.M., J.W.V., J.S. and D.B. contributed data. J.Y.H. and B.M. wrote the manuscript with valuable revision by R.D.M., J.W.V., J.S. and D.B. B.M. was the project administrator.

Corresponding author

Correspondence to Bratislav Misic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Andre Altmann, Håkon Grydeland 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.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Supplementary Tables 1 and 2.

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 3–6.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hansen, J.Y., Markello, R.D., Vogel, J.W. et al. Mapping gene transcription and neurocognition across human neocortex. Nat Hum Behav 5, 1240–1250 (2021). https://doi.org/10.1038/s41562-021-01082-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-021-01082-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing