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:

Connectivity precedes function in the development of the visual word form area

Nature Neuroscience volume 19, pages 1250–1255 (2016)Cite this article

Subjects

Abstract

What determines the cortical location at which a given functionally specific region will arise in development? We tested the hypothesis that functionally specific regions develop in their characteristic locations because of pre-existing differences in the extrinsic connectivity of that region to the rest of the brain. We exploited the visual word form area (VWFA) as a test case, scanning children with diffusion and functional imaging at age 5, before they learned to read, and at age 8, after they learned to read. We found the VWFA developed functionally in this interval and that its location in a particular child at age 8 could be predicted from that child's connectivity fingerprints (but not functional responses) at age 5. These results suggest that early connectivity instructs the functional development of the VWFA, possibly reflecting a general mechanism of cortical development.

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

Figure 1: Percent signal change (PSC) for each fROI.
Figure 2: PSC in the VWFA as a function of fROI volume.
Figure 3: Actual versus predicted fMRI activation for words > objects on the ventral surface of an example subject.
Figure 4: Correlations of actual word selectivity at age 8 with predicted word selectivity.
Figure 5: Left-lateralized regions that are preferentially connected with the VWFA versus lFFA or lPFS at age 5.

Similar content being viewed by others

References

  1. Saygin, Z.M. et al. Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat. Neurosci. 15, 321–327 (2012).

    Article  CAS  Google Scholar 

  2. Osher, D.E. et al. Structural connectivity fingerprints predict cortical selectivity for multiple visual categories across cortex. Cereb. Cortex 26, 1668–1683 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mahon, B.Z. & Caramazza, A. What drives the organization of object knowledge in the brain? Trends Cogn. Sci. 15, 97–103 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hannagan, T., Amedi, A., Cohen, L., Dehaene-Lambertz, G. & Dehaene, S. Origins of the specialization for letters and numbers in ventral occipitotemporal cortex. Trends Cogn. Sci. 19, 374–382 (2015).

    Article  PubMed  Google Scholar 

  5. Sur, M., Garraghty, P.E. & Roe, A.W. Experimentally induced visual projections into auditory thalamus and cortex. Science 242, 1437–1441 (1988).

    Article  CAS  PubMed  Google Scholar 

  6. Roe, A.W., Pallas, S.L., Hahm, J.O. & Sur, M. A map of visual space induced in primary auditory cortex. Science 250, 818–820 (1990).

    Article  CAS  PubMed  Google Scholar 

  7. Roe, A.W., Pallas, S.L., Kwon, Y.H. & Sur, M. Visual projections routed to the auditory pathway in ferrets: receptive fields of visual neurons in primary auditory cortex. J. Neurosci. 12, 3651–3664 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sharma, J., Angelucci, A. & Sur, M. Induction of visual orientation modules in auditory cortex. Nature 404, 841–847 (2000).

    Article  CAS  PubMed  Google Scholar 

  9. Horng, S. et al. Differential gene expression in the developing lateral geniculate nucleus and medial geniculate nucleus reveals novel roles for Zic4 and Foxp2 in visual and auditory pathway development. J. Neurosci. 29, 13672–13683 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. McCandliss, B.D., Cohen, L. & Dehaene, S. The visual word form area: expertise for reading in the fusiform gyrus. Trends Cogn. Sci. 7, 293–299 (2003).

    Article  PubMed  Google Scholar 

  11. Dehaene, S. et al. How learning to read changes the cortical networks for vision and language. Science 330, 1359–1364 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Glezer, L.S., Jiang, X. & Riesenhuber, M. Evidence for highly selective neuronal tuning to whole words in the “visual word form area”. Neuron 62, 199–204 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Glezer, L.S. & Riesenhuber, M. Individual variability in location impacts orthographic selectivity in the “visual word form area”. J. Neurosci. 33, 11221–11226 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Glezer, L.S., Kim, J., Rule, J., Jiang, X. & Riesenhuber, M. Adding words to the brain's visual dictionary: novel word learning selectively sharpens orthographic representations in the VWFA. J. Neurosci. 35, 4965–4972 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Baker, C.I. et al. Visual word processing and experiential origins of functional selectivity in human extrastriate cortex. Proc. Natl. Acad. Sci. USA 104, 9087–9092 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Srihasam, K., Vincent, J.L. & Livingstone, M.S. Novel domain formation reveals proto-architecture in inferotemporal cortex. Nat. Neurosci. 17, 1776–1783 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Grill-Spector, K., Kourtzi, Z. & Kanwisher, N. The lateral occipital complex and its role in object recognition. Vision Res. 41, 1409–1422 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Dehaene, S. & Cohen, L. Cultural recycling of cortical maps. Neuron 56, 384–398 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Dehaene, S. & Cohen, L. The unique role of the visual word form area in reading. Trends Cogn. Sci. 15, 254–262 (2011).

    Article  PubMed  Google Scholar 

  20. Haxby, J.V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. Behrmann, M. & Plaut, D.C. Distributed circuits, not circumscribed centers, mediate visual recognition. Trends Cogn. Sci. 17, 210–219 (2013).

    Article  PubMed  Google Scholar 

  22. Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 352, 216–220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bouhali, F. et al. Anatomical connections of the visual word form area. J. Neurosci. 34, 15402–15414 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yeatman, J.D., Rauschecker, A.M. & Wandell, B.A. Anatomy of the visual word form area: adjacent cortical circuits and long-range white matter connections. Brain Lang. 125, 146–155 (2013).

    Article  PubMed  Google Scholar 

  25. Cantlon, J.F., Pinel, P., Dehaene, S. & Pelphrey, K.A. Cortical representations of symbols, objects, and faces are pruned back during early childhood. Cereb. Cortex 21, 191–199 (2011).

    Article  PubMed  Google Scholar 

  26. Saygin, Z.M. et al. Tracking the roots of reading ability: white matter volume and integrity correlate with phonological awareness in prereading and early-reading kindergarten children. J. Neurosci. 33, 13251–13258 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Langer, N. et al. White matter alterations in infants at risk for developmental dyslexia. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bhv281 (2015).

  28. Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, 1909).

  29. Lorenz, S. et al. Two new cytoarchitectonic areas on the human mid-fusiform gyrus. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bhv225 (2015).

  30. Woodcock, R.W. Woodcock Reading Mastery Tests, Revised, Examiner's Manual (American Guidance Service, 1998).

  31. Kaufman, A.S. & Kaufman, N.L. Kaufman Brief Intelligence Test (Wiley Online Library, 2004).

  32. Dunn, D.M. & Dunn, L.M. Peabody Picture Vocabulary Test: Manual (Pearson, 2007).

  33. van der Kouwe, A.J.W., Benner, T., Salat, D.H. & Fischl, B. Brain morphometry with multiecho MPRAGE. Neuroimage 40, 559–569 (2008).

    Article  PubMed  Google Scholar 

  34. Tisdall, M.D. et al. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn. Reson. Med. 68, 389–399 (2012).

    Article  PubMed  Google Scholar 

  35. Dale, A.M., Fischl, B. & Sereno, M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004).

    Article  PubMed  Google Scholar 

  38. 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 

  39. Thesen, S., Heid, O., Mueller, E. & Schad, L.R. Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn. Reson. Med. 44, 457–465 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5, 13 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S. & Kanwisher, N. New method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104, 1177–1194 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Julian, J.B., Fedorenko, E., Webster, J. & Kanwisher, N. An algorithmic method for functionally defining regions of interest in the ventral visual pathway. Neuroimage 60, 2357–2364 (2012).

    Article  CAS  PubMed  Google Scholar 

  43. Postelnicu, G., Zollei, L. & Fischl, B. Combined volumetric and surface registration. IEEE Trans. Med. Imaging 28, 508–522 (2009).

    Article  PubMed  Google Scholar 

  44. Zöllei, L., Stevens, A., Huber, K., Kakunoori, S. & Fischl, B. Improved tractography alignment using combined volumetric and surface registration. Neuroimage 51, 206–213 (2010).

    Article  PubMed  Google Scholar 

  45. Yendiki, A. et al. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinform. 5, 23 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Wang, H. & Yushkevich, P.A. Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation. Front. Neuroinform. 7, 27 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. Menze, B.H. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015).

    Article  PubMed  Google Scholar 

  48. Tustison, N.J. et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 99, 166–179 (2014).

    Article  PubMed  Google Scholar 

  49. Avants, B.B. et al. The Insight ToolKit image registration framework. Front. Neuroinform. 8, 44 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Jain, V. et al. Longitudinal reproducibility and accuracy of pseudo-continuous arterial spin-labeled perfusion MR imaging in typically developing children. Radiology 263, 527–536 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Lawson, G.M., Duda, J.T., Avants, B.B., Wu, J. & Farah, M.J. Associations between children's socioeconomic status and prefrontal cortical thickness. Dev. Sci. 16, 641–652 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Pineda, R.G. et al. Alterations in brain structure and neurodevelopmental outcome in preterm infants hospitalized in different neonatal intensive care unit environments. J. Pediatr. 164, 52–60 (2014).

    Article  PubMed  Google Scholar 

  53. Reuter, M., Rosas, H.D. & Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 53, 1181–1196 (2010).

    PubMed  Google Scholar 

  54. Reuter, M. & Fischl, B. Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage 57, 19–21 (2011).

    Article  PubMed  Google Scholar 

  55. Reuter, M., Schmansky, N.J., Rosas, H.D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012).

    Article  PubMed  Google Scholar 

  56. Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S. & Woolrich, M.W. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34, 144–155 (2007).

    Article  CAS  PubMed  Google Scholar 

  57. Greve, D.N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).

    Article  PubMed  Google Scholar 

  58. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).

  59. Huth, A.G., de Heer, W.A., Griffiths, T.L., Theunissen, F.E. & Gallant, J.L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Finn, A.S., Sheridan, M.A., Kam, C.L.H., Hinshaw, S. & D'Esposito, M. Longitudinal evidence for functional specialization of the neural circuit supporting working memory in the human brain. J. Neurosci. 30, 11062–11067 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank B. Fischl and M. Reuter for their guidance and advice on longitudinal registration, S. Robinson and O. Ozernov-Palchik for assistance with participant coordination and A. Park for technical assistance. We thank the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology and its staff. We also thank our READ Study research testers, school coordinators and principals, and participating families. This work was funded by NICHD/NIH grant F32HD079169 to Z.M.S., NIH/NICHD R01HD067312 to J.D.E.G. and N.G., Ellison Medical Foundation, EY13455 to N.K., and grant 1444913 from McGovern Institute for Brain Research MINT to N.K. and Z.M.S.

Author information

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Zeynep M Saygin, Sara D Beach, Jenelle Feather, John D E Gabrieli & Nancy Kanwisher

  2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Zeynep M Saygin, Sara D Beach, John D E Gabrieli & Nancy Kanwisher

  3. Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA

    David E Osher

  4. Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA

    Elizabeth S Norton

  5. Department of Biological Sciences, Barnard College, Columbia University, New York, New York, USA

    Deanna A Youssoufian

  6. Boston Children's Hospital, Boston, Massachusetts, USA

    Nadine Gaab

Authors
  1. Zeynep M Saygin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David E Osher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elizabeth S Norton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deanna A Youssoufian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sara D Beach
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jenelle Feather
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nadine Gaab
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. John D E Gabrieli
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nancy Kanwisher
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.M.S., D.E.O. and N.K. designed the experiments. Z.M.S., E.S.N., D.A.Y., S.D.B., N.G., J.D.E.G. and N.K. conducted the experiments or supplied data. Z.M.S., D.E.O., D.A.Y. and J.F. analyzed the data. Z.M.S., D.E.O. and N.K. wrote the manuscript.

Corresponding author

Correspondence to Zeynep M Saygin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 fMRI experiment on 8 year olds.

(a) At age 8, children were shown words, scrambled words, line-drawings of objects, and line-drawings of faces over 6 experimental runs in a blocked design. A grid was overlaid on top of the stimuli so that all stimulus types (not just scrambled words) had edges. Each stimulus was presented for 500ms (ISI=0.193s) and overlaid on a different single-color background. Each run consisted of 18 blocks (4 blocks per stimulus type and 3 fixation blocks per run), and participants performed a one-back task. (b) fROIs were defined in each individual by intersecting that individual’s relevant thresholded activation contrast map with the relevant constraint region (e.g. for the VWFA, words > line-drawings of objects within VWFA constraint region). Constraint regions were defined from fMRI data in a separate group of adult participants as parcels within which most subjects had a significant activation, and were registered to each child’s native anatomy. Constraint regions for the VWFA (magenta), lFFA (yellow), and lPFS (cyan) are shown on an example subject’s inflated cortical surface. Note that while constraint regions are large (to accommodate individual variation) and overlap, the resulting fROIs within an individual are small and do not overlap.

Supplementary Figure 2 Percent signal change (PSC) for each fROI and age in pre-readers and readers.

(a) Example stimuli used for the age 5 fMRI experiment. The fMRI experiments at age 5 and age 8 were slightly different e.g. used different scanning protocols and different stimuli. To test whether the age 5 fMRI experiment could detect orthographic selectivity when it is present at age 5, we analyzed another cohort of children who could already read at age 5 (“readers”, right). We performed the same fROI percent signal change analysis that we did for the main cohort of children who could not read at age 5, defining fROIs on the age-8 data and measuring response magnitudes in those fROIs in independent data. (b) Percent signal change in VWFA, lFFA, and lPFS at age 8 (bottom) and age 5 (top) for children who could not read at age 5 (“pre- readers”, left; note that this is the same data as Figure 1 but is included here for comparison to the readers). (c) Percent signal change analysis in the children who could read (“readers") at both age 5 (top) and age 8 (bottom). Like the pre-readers (b), we find clear selectivity for faces in lFFA (F3,21 = 23.52, P = 6.49 x10-7; faces vs. words: P = 2.51 x 10-4; T = 6.81; faces vs. objects: P = 4.77 x 10-5; T = 8.85; faces vs. scrambled words: P = 7.41 x 10-4; T = 5.69) and clear selectivity for words in the VWFA for readers at age 8 (repeated measures ANOVA by condition: F3,21 = 5.92, P = 4.33 x10-3; words vs. objects: P = 2.01 x 10-3; T = 4.78; vs. faces: P = 2.99 x 10-2; T = 2.72; vs. scrambled words: P = 9.82 x 10-3; T = 3.51). However, while pre-readers do not show selectivity to letters in the VWFA at age 5, the readers do show selectivity to letters (bottom bar plots in b and c; repeated measures ANOVA by condition: F2,14 = 4.59, P = 2.93 x10-2; letters vs. faces: P = 3.48 x 10-2; T = 2.61; vs. false fonts: P = 2.07 x 10-2; T = 2.97). The lFFA also showed strong selectivity to faces over letters or false fonts at age 5 (F2,14 = 33.51, P = 4.60 x10-6; faces vs. letters: P = 1.14 x 10-4; T = 7.73; vs. false fonts: P = 8.66 x 10-4; T = 5.54. The response profile of the VWFA was distinct from both the lFFA (fROI x condition repeated measure ANOVA: F2,28 = 21.13, P = 2.55 x10-6) and from the lPFS at age 5 (F2,28 = 9.78, P = 6.01 x10-4) as well as at age 8 (VWFA vs. lFFA same three conditions as age 5, i.e. no objects condition: F2,28 = 18.30, P = 8.26 x10-6 and VWFA vs. lPFS: F2,28 = 5.93, P = 7.12 x10-3). Error bars = SE and horizontal bars reflect individual posthoc tests significant at P < 0.05.

Supplementary Figure 3 Percent signal change in the VWFA as a function of fROI volume in pre-readers and readers.

(a) Children who were not able to read at age 5 (“pre-readers”) did not show selectivity for letters as compared to faces or false fonts (top; for 5th percentile: letters vs. false fonts: P = 0.126; T(13) = 1.63; letters vs. faces: P = 0.711; T(13) = -0.379). We also defined percentiles in exactly the same way from age 8 data (bottom), and found strong selectivity for words (words vs. scrambled words: P = 3.02 x10-2; T(13) = 2.43; words vs. faces: P = 1.38 x10-2; T(13) = 4.05). Note that (a) is the same figure as Figure 2 in main text but also included here for comparison to children who could already read at age 5 (“readers”). (b) Readers do show selectivity for letters as compared to faces or false fonts, even in the most selective bin (top 5%) at age 5 (top; letters vs. false fonts: P = 1.79x10-2; T(7) = 3.08; letters vs. faces: P = 2.04 x 10-3; T(7) = 4.77) as well as at age 8 (bottom; words vs. scrambled words: P = 2.65 x10-2; (t7) = 2.80; words vs. faces: P = 2.19 x10-2; T(7) = 2.93). Error bars = SE.

Supplementary Figure 4 MVPA accuracy on age 5 fMRI data for all voxels within the VWFA parcel.

In children who could not yet read (left), classification accuracy was at chance for discriminating letters (from faces or false fonts; P = 0.30; T(13) = 1.07). In contrast, classification performance was significantly above chance for letters in children who could already read at age 5 (P = 3.10 x10-2; T(7) = 2.70). The region that will later become the VWFA cannot discriminate letters from faces or false fonts before these children can read. Thick lines reflect mean, thin lines reflect SE. Dotted line is chance accuracy.

Supplementary Figure 5 Group average (random effects) map for words > objects for leave-one-subject-out analysis on template brain.

Although the VWFA usually falls within the lateral surface of the occipitotemporal cortex, its precise location varies substantially across individuals13. Thus the group average maps of word-selectivity do not show above threshold activations as illustrated here. Colorbar scale indicates -log(p) values.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 1–3 (PDF 1085 kb)

Supplementary Methods Checklist

(PDF 551 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saygin, Z., Osher, D., Norton, E. et al. Connectivity precedes function in the development of the visual word form area. Nat Neurosci 19, 1250–1255 (2016). https://doi.org/10.1038/nn.4354

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4354

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