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The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure

Nature Neuroscience volume 22, pages 809–819 (2019)Cite this article

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Abstract

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1–13%, or r = 0.1–0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure–function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas.

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Fig. 1: Definition of homotopic brain regions and dMRI-derived microstructural maps.
Fig. 2: Prediction of functional homotopic connectivity from white matter microstructure.
Fig. 3: Significant associations between functional connectivity and microstructure of the connecting white matter tract.
Fig. 4: Percentage variance explained (r2) in the functional connectivity of each homotopic region pair by microstructural metrics derived from the connecting white matter tract in the training cohort (n = 7,481 subjects).
Fig. 5: Total variance explained by the multimodal regression model in the training and replication cohorts.
Fig. 6: Negative control analysis.
Fig. 7: Genome-wide associations with the microstructure–function phenotype (that is, the pattern of functional connectivity that can be predicted from white matter microstructure).

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Data availability

All source data (including raw and processed brain imaging data and genetics data) are available from UK Biobank via their standard data access procedure (see http://www.ukbiobank.ac.uk/register-apply).

Code availability

The image processing pipelines of the MRI data in the UK Biobank project can be found at http://www.fmrib.ox.ac.uk/ukbiobank. Custom-written Matlab code including the microstructure–function modeling is freely available at https://users.fmrib.ox.ac.uk/~jmollink/Biobank/Biobank.html.

References

  1. Buzsáki, G. Rhythms of the Brain (Oxford University Press, 2006).

  2. Kötter, R. & Sommer, F. T. Global relationship between anatomical connectivity and activity propagation in the cerebral cortex. Phil. Trans. R. Soc. Lond. B 355, 127–134 (2000).

    Article  Google Scholar 

  3. Rorden, C. & Karnath, H.-O. Using human brain lesions to infer function: a relic from a past era in the fMRI age? Nat. Rev. Neurosci. 5, 813–819 (2004).

    Article  Google Scholar 

  4. Schmahmann, J. D. & Pandya, D. N. in Fiber Pathways of the Brain 1–37 (Oxford University Press, 2006).

  5. Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J. & Aldroubi, A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000).

    Article  CAS  Google Scholar 

  6. Jbabdi, S. & Johansen-Berg, H. Tractography: where do we go from here? Brain Connect. 1, 169–183 (2011).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Greicius, M. D., Supekar, K., Menon, V. & Dougherty, R. F. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19, 72–78 (2009).

    Article  Google Scholar 

  9. van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S. & Hulshoff Pol, H. E. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum. Brain Mapp. 30, 3127–3141 (2009).

    Article  Google Scholar 

  10. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).

    Article  Google Scholar 

  11. Koch, M. A., Norris, D. G. & Hund-Georgiadis, M. An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage 16, 241–250 (2002).

    Article  Google Scholar 

  12. Beaulieu, C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed. 15, 435–455 (2002).

    Article  Google Scholar 

  13. van den Heuvel, M., Mandl, R., Luigjes, J. & Hulshoff Pol, H. Microstructural organization of the cingulum tract and the level of default mode functional connectivity. J. Neurosci. 28, 10844–10851 (2008).

    Article  Google Scholar 

  14. Wahl, M. et al. Human motor corpus callosum: topography, somatotopy, and link between microstructure and function. J. Neurosci. 27, 12132–12138 (2007).

    Article  CAS  Google Scholar 

  15. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012).

    Article  Google Scholar 

  16. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  Google Scholar 

  17. Stark, D. E. et al. Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations. J. Neurosci. 28, 13754–13764 (2008).

    Article  CAS  Google Scholar 

  18. Shen, K. et al. Stable long-range interhemispheric coordination is supported by direct anatomical projections. Proc. Natl Acad. Sci. USA 112, 6473–6478 (2015).

    Article  CAS  Google Scholar 

  19. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    Article  Google Scholar 

  20. Basser, P. J., Mattiello, J. & LeBihan, D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. 103, 247–254 (1994).

    Article  CAS  Google Scholar 

  21. Ennis, D. B. & Kindlmann, G. Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn. Reson. Med. 55, 136–146 (2006).

    Article  Google Scholar 

  22. 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  Google Scholar 

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

    Article  Google Scholar 

  24. Mollink, J. et al. Evaluating fibre orientation dispersion in white matter: comparison of diffusion MRI, histology and polarized light imaging. Neuroimage 157, 561–574 (2017).

    Article  Google Scholar 

  25. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  Google Scholar 

  26. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  Google Scholar 

  27. Matusek, T. et al. Formin proteins of the DAAM subfamily play a role during axon growth. J. Neurosci. 28, 13310–13319 (2008).

    Article  CAS  Google Scholar 

  28. Avilés, E. C. & Stoeckli, E. T. Canonical wnt signaling is required for commissural axon guidance. Dev. Neurobiol. 76, 190–208 (2016).

    Article  Google Scholar 

  29. Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  Google Scholar 

  30. Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 19, 151 (2018).

    Article  Google Scholar 

  31. Yung, Y. C., Stoddard, N. C., Mirendil, H. & Chun, J. Lysophosphatidic acid signaling in the nervous system. Neuron 85, 669–682 (2015).

    Article  CAS  Google Scholar 

  32. Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015).

    Article  CAS  Google Scholar 

  33. Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).

    Article  CAS  Google Scholar 

  34. O’Reilly, J. X. et al. Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys. Proc. Natl Acad. Sci. USA 110, 13982–13987 (2013).

    Article  Google Scholar 

  35. Roland, J. L. et al. On the role of the corpus callosum in interhemispheric functional connectivity in humans. Proc. Natl Acad. Sci. USA 114, 13278–13283 (2017).

    Article  CAS  Google Scholar 

  36. Smith, S. M. & Nichols, T. E. Statistical challenges in “big data” human neuroimaging. Neuron 97, 263–268 (2018).

    Article  CAS  Google Scholar 

  37. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    Article  CAS  Google Scholar 

  38. Tobyne, S. M. et al. A surface-based technique for mapping homotopic interhemispheric connectivity:development, characterization, and clinical application. Hum. Brain Mapp. 37, 2849–2868 (2016).

    Article  Google Scholar 

  39. Schmahmann, J. D. et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130, 630–653 (2007).

    Article  Google Scholar 

  40. Smith, S. M. The future of FMRI connectivity. Neuroimage 62, 1257–1266 (2012).

    Article  Google Scholar 

  41. Cordes, D. et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am. J. Neuroradiol. 22, 1326–1333 (2001).

    CAS  PubMed  Google Scholar 

  42. Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011).

    Article  Google Scholar 

  43. Budde, M. D. & Annese, J. Quantification of anisotropy and fiber orientation in human brain histological sections. Front. Integr. Neurosci. 7, 3 (2013).

    Article  Google Scholar 

  44. Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).

    Article  CAS  Google Scholar 

  45. Habas, R., Kato, Y. & He, X. Wnt/Frizzled activation of Rho regulates vertebrate gastrulation and requires a novel Formin homology protein Daam1. Cell 107, 843–854 (2001).

    Article  CAS  Google Scholar 

  46. Salomon, S. N., Haber, M., Murai, K. K. & Dunn, R. J. Localization of the Diaphanous-related formin Daam1 to neuronal dendrites. Neurosci. Lett. 447, 62–67 (2008).

    Article  CAS  Google Scholar 

  47. Kida, Y., Shiraishi, T. & Ogura, T. Identification of chick and mouse Daam1 and Daam2 genes and their expression patterns in the central nervous system. Brain Res. Dev. Brain Res. 153, 143–150 (2004).

    Article  CAS  Google Scholar 

  48. Behrends, U. et al. Novel tumor antigens identified by autologous antibody screening of childhood medulloblastoma cDNA libraries. Int. J. Cancer 106, 244–251 (2003).

    Article  CAS  Google Scholar 

  49. González de San Román, E. et al. Anatomical location of LPA1 activation and LPA phospholipid precursors in rodent and human brain. J. Neurochem. 134, 471–485 (2015).

    Article  Google Scholar 

  50. Fields, R. D. A new mechanism of nervous system plasticity: activity-dependent myelination. Nat. Rev. Neurosci. 16, 756–767 (2015).

    Article  CAS  Google Scholar 

  51. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).

    Article  Google Scholar 

  52. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).

    Article  Google Scholar 

  53. Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90, 449–468 (2014).

    Article  Google Scholar 

  54. Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2016).

    Article  Google Scholar 

  55. Daducci, A. et al. Accelerated microstructure imaging via convex pptimization (AMICO) from diffusion MRI data. Neuroimage 105, 32–44 (2015).

    Article  Google Scholar 

  56. Griffanti, L. et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage 141, 191–205 (2016).

    Article  Google Scholar 

  57. Beckmann, C. F. & Smith, S. M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).

    Article  Google Scholar 

  58. Behrens, T. E. J. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003).

    Article  CAS  Google Scholar 

  59. Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Graña, M. & Behrens, T. E. J. Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magn. Reson. Med. 68, 1846–1855 (2012).

    Article  Google Scholar 

  60. de Groot, M. et al. Improving alignment in tract-based spatial statistics: evaluation and optimization of image registration. Neuroimage 76, 400–411 (2013).

    Article  Google Scholar 

  61. Bach, M. et al. Methodological considerations on tract-based spatial statistics (TBSS). Neuroimage 100, 358–369 (2014).

    Article  Google Scholar 

  62. Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. Permutation inference for the general linear model. Neuroimage 92, 381–397 (2014).

    Article  Google Scholar 

  63. Sotiropoulos, S. N., Behrens, T. E. J. & Jbabdi, S. Ball and rackets: inferring fiber fanning from diffusion-weighted MRI. Neuroimage 60, 1412–1425 (2012).

    Article  Google Scholar 

  64. Budde, M. D. & Frank, J. A. Examining brain microstructure using structure tensor analysis of histological sections. Neuroimage 63, 1–10 (2012).

    Article  Google Scholar 

  65. Heinrich, M. P. et al. MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16, 1423–1435 (2012).

    Article  Google Scholar 

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Acknowledgements

All data in this study were obtained from the UK Biobank project (access no. 8107). We are very grateful to all individuals who donated their time to participate in the UK Biobank study. K.L.M., M.K. and J.Mo. are supported by the Wellcome Trust (nos. 091509/Z/10/Z, 202788/Z/16/Z, 098369/Z/12/Z). The authors gratefully acknowledge funding from the Wellcome Trust UK Strategic Award (no. 098369/Z/12/Z). UK Biobank brain imaging and F.A.-A. are funded by the UK Medical Research Council and the Wellcome Trust. J.Ma. acknowledges funding for this work from the European Research Council (grant no. 617306) and the Leverhulme Trust. S.J. is supported by the UK Medical Research Council (no. MR/L009013/1). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (no. 203139/Z/16/Z). Finally, we would like to thank D. Norris and J. Marques for their valuable input on this work.

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Author notes

  1. These authors contributed equally: Saad Jbabdi and Karla L. Miller.

Authors and Affiliations

  1. Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom

    Jeroen Mollink, Stephen M. Smith, Michiel Kleinnijenhuis, Fidel Alfaro-Almagro, Saad Jbabdi & Karla L. Miller

  2. Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands

    Jeroen Mollink, Marlies Hiemstra & Anne-Marie van Cappellen van Walsum

  3. Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada

    Lloyd T. Elliott

  4. Department of Statistics, University of Oxford, Oxford, United Kingdom

    Lloyd T. Elliott & Jonathan Marchini

  5. The Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

    Jonathan Marchini

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Contributions

J.Mo., S.M.S., S.J. and K.L.M. designed the research. J.Mo. performed the research. K.L.M., F.A.-A. and S.M.S., developed acquisition and processing pipelines for the MRI data. L.T.E. and J.Ma. processed genetics data, provided tools for genome-wide associations analysis and gave feedback on genetics results. J.Mo., S.M.S., M.K., M.H., A.M.C.W., S.J. and K.L.M. analyzed the data and interpreted its outcomes. J.Mo. and K.L.M. wrote the manuscript, which was edited by all authors.

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Correspondence to Jeroen Mollink.

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Competing interests

J.Ma. is a co-founder and director of GENSCI Ltd. S.S. is a co-founder of SBGneuro.

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Journal peer review information: Nature Neuroscience thanks Genevieve Konopka and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Mollink, J., Smith, S.M., Elliott, L.T. et al. The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure. Nat Neurosci 22, 809–819 (2019). https://doi.org/10.1038/s41593-019-0379-2

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