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


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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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

Code availability

The image processing pipelines of the MRI data in the UK Biobank project can be found at Custom-written Matlab code including the microstructure–function modeling is freely available at

Additional information

Journal peer review information: Nature Neuroscience thanks Genevieve Konopka and 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.


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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.

Author information

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.

Competing interests

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

Correspondence to Jeroen Mollink.

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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).