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Link to original content: https://doi.org/10.1038/nn.4393
Multimodal population brain imaging in the UK Biobank prospective epidemiological study | Nature Neuroscience
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Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Nature Neuroscience volume 19, pages 1523–1536 (2016)Cite this article

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Abstract

Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.

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Figure 1: Data from the three structural imaging modalities in UK Biobank brain imaging.
Figure 2: The diffusion MRI data in UK Biobank.
Figure 3: The task fMRI data in UK Biobank.
Figure 4: The resting-state fMRI data in UK Biobank.
Figure 5: Voxel-wise correlations of participants' age against several white matter measures from the dMRI and T2 FLAIR data.
Figure 6: Visualization of 2.8 million univariate cross-subject association tests between 2,501 IDPs and 1,100 other variables in the UK Biobank database.
Figure 7: Details of three modes from the doubly-multivariate CCA-ICA analyses across all IDPs and non-brain-imaging variables.
Figure 8: Hypothesis-driven study of age, BMI and smoking associations with subcortical T2*.

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Acknowledgements

We would like to acknowledge the valuable contributions of members of the UK Biobank Imaging Working Group and the UK Biobank coordinating center. We are very grateful for additional input into the imaging protocol and image processing pipelines from M. Chappell, S. Clare, E. Duff, D. Flitney, M. Hernandez Fernandez, H. Johansen-Berg, P. McCarthy, J. Miller, D. Mortimer, J. Price, G. Salimi-Khorshidi, E. Vallee, D. Vidaurre, M. Webster, A. Winkler, A. Young, E. Auerbach, S. Moeller, K. Ugurbil, D. Alexander, N. Fox, E. Kaden, S. Ourselin, G. Zhang, A. Daducci, T. Stoecker, D. Barch, N. Bloom, G. Burgess, M. Glasser, M. Harms, D. Nolan, B. Fischl, D. Greve, J. Polimeni, T. Nichols, A. Murphy, G. Parker, F. Barkhof, C. Beckmann, M. Mennes, M. Vernooij, N. Weiskopf, C. Rorden and J. Wardlaw. We are grateful for the provision of simultaneous multi-slice (multiband) pulse sequence and reconstruction algorithms from the Center for Magnetic Resonance Research, University of Minnesota. Finally, we are extremely grateful to all UK Biobank study participants, who generously donated their time to make this resource possible. UK Biobank (including the imaging enhancement) has been generously supported by the UK Medical Research Council and the Wellcome Trust. K.L.M. and S.M.S. receive further support from the Wellcome Trust. P.M.M. acknowledges support from the Edmund J Safra Foundation and Lily Safra, the Imperial College Healthcare Trust Biomedical Research Centre, and the Medical Research Council.

Author information

Authors and Affiliations

  1. Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK

    Karla L Miller, Fidel Alfaro-Almagro, Saad Jbabdi, Stamatios N Sotiropoulos, Jesper L R Andersson, Ludovica Griffanti, Gwenaëlle Douaud, Thomas W Okell, Mark Jenkinson & Stephen M Smith

  2. Department of Electrical Engineering, Brigham Young University, Provo, Utah, USA

    Neal K Bangerter

  3. Institute of Neurology, University College London, London, UK

    David L Thomas

  4. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA

    Essa Yacoub

  5. Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Junqian Xu

  6. Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany

    Andreas J Bartsch

  7. Siemens Healthcare UK, Frimley, UK

    Peter Weale & Iulius Dragonu

  8. UK Biobank, Stockport, UK

    Steve Garratt, Sarah Hudson & Rory Collins

  9. Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Rory Collins

  10. Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK

    Paul M Matthews

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  1. Karla L Miller
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Contributions

K.L.M., R.C., P.M.M. and S.M.S. provided the overall scientific strategy for UK Biobank brain imaging. K.L.M., N.K.B., D.L.T., E.Y., J.X., A.J.B., S.J., S.N.S., J.L.R.A., M.J., P.M.M. and S.M.S. developed acquisition protocols. N.K.B., K.L.M., T.W.O., P.W., I.D., S.G. and S.H. implemented the imaging protocol at the dedicated imaging center. F.A.-A., K.L.M., S.J., S.N.S., J.L.R.A., L.G., G.D., M.J. and S.M.S. developed post-processing pipelines and IDP calculation. K.L.M. and S.M.S. carried out the univariate and multivariate analyses and prepared figures. K.L.M. and S.M.S. wrote the manuscript, which was edited by all of the authors.

Corresponding author

Correspondence to Karla L Miller.

Ethics declarations

Competing interests

P.W. and I.D. are employees of Siemens Healthcare UK, the vendor of MRI scanners for UK Biobank, selected under a competitive bidding process.

Integrated supplementary information

Supplementary Figure 1 Asymptotic behavior of group average images and correlations with age, with increasing numbers of subjects.

(a) Resting-state network group-averages formed from 5 different group sizes. Individual subjects' preprocessed resting-state data were used to generate subject-specific effect-size maps (arbitrary but consistent units of resting activity strength) of one of the low-dimensional resting-state networks (the default mode network, here shown with both positive=red and negative=blue involvement in this network, with the same color-coding and thresholding applied in all cases). These were then averaged across subjects for a range of subject numbers. Increasing n suppresses background noise as expected, and the non-noise network structure asymptotes towards a constant map as n rises over 100. Although any imperfections in functional spatial alignment across subjects limits the sharpness of the asymptotic group-average, as n is raised even further to 5000, there is no sign of a degradation (e.g., blurring) of the group-average map (compared with lower subject numbers), as expected. (b-d) Voxelwise correlation of the same resting-state network with increasing subject age, again to illustrate the statistical effect of increasing the number of subjects used. (b) The age correlation map when using 5000 subjects; with increasing age the correlations are dominantly negative, indicating a weakening of this cognitive network (r>0.1, Pcorrected<10-10). (c) The 10 voxels having the strongest (positive or negative) correlation with age, that are also at least 10mm distant from each other, are used to form 10 plots of age-correlation against number of subjects used in the correlation, from 10 to 5000 subjects. As expected, the plots asymptote, with increasing subject numbers, towards the "true" final value, with noticeable instability up to as many as 2000 subjects. (d) From the same 10 sets of correlations, the statistical significance (-log10(P)) is shown. Whereas r asymptotes towards its true final constant value with increasing n, statistical significance (for a non-null correlation) has an ever-increasing trend with increasing n. (e) Theoretical relationship between increasing statistical power and subject numbers, assuming a true correlation between any two variables of r=0.1. As n increases (here up to 7000), the number of distinct tests that will pass Bonferroni multiple-comparison correction rises to very large numbers - here up to around 1015.

Supplementary Figure 2 Visualisation of 2.8 million univariate cross-subject association tests between 2501 IDPs and 1100 other variables in the UK Biobank database, showing variance explained on the y axis.

Whereas the version of this plot in Figure 6 reported statistical significance (-log10(P)), here we show variance explained (r2). The relationship between P and r is not here exactly a fixed one-to-one mapping, due to the different numbers of valid (non-missing) data in different pairs of variables being tested. The Manhattan plot shows, for each of the 1100 non-brain-imaging variables, the strongest r2 association of that variable with each distinct imaging sub-modality’s IDPs. (i.e., 6 results plotted for each x axis position, each with a color indicating a brain imaging modality; this plot differs from the other Manhattan plots, which show correlations with all IDPs).

Supplementary Figure 3 Visualisation of modes 1, 2 and 3 from the doubly-multivariate CCA-ICA analyses across all IDPs and non-brain-imaging variables.

(a) Mode 1 links physical measures of body size, metabolic rate and hand grip strength to brain structure sizes and a range of dMRI-derived measures. (b) Mode 2 primarily links bone density measures to brain structure sizes, T2* levels and a range of dMRI-derived measures. (c) Mode 3 primarily links measures of body fat to T2* levels and resting-state activity fluctuation amplitudes. As seen in Supp Fig 8a, modes 1 and 2 are associated with aging (and sex), while mode 3 is not strongly associated; from Supp Fig 8b, we see that modes 1 and (more strongly) 3 are associated with hypertension.

Supplementary Figure 4 Visualisation of modes 4, 5 and 6 from the doubly-multivariate CCA-ICA analyses across all IDPs and non-brain-imaging variables.

(a) Mode 4 links cardiac measures (including heart rate) to resting-state amplitudes and connectivities (rfMRI summary images are shown larger in Supp Fig 5). Observing these 3 types of measures linked together suggests that the apparent change in functional connectivity in this mode likely reflects changes in vascular processes rather than underlying neural connectivity. (b) Mode 5 links a range of lifestyle and biophysical measures (most strongly alcohol intake and smoking, red blood cell and cardiac measures) to T2* subcortical intensity (e.g., iron deposition) and the resting-state amplitudes of many brain areas (rfMRI summary images shown larger in Supp Fig 6). (c) Mode 6 links early-life measures (birth weight and breast feeding) along with several other physical and lifestyle measures to many imaging measures of both diffusivity and functional connectivity (rfMRI summary images are shown larger in Supp Fig 7).

Supplementary Figure 5 More detailed visualization of the rfMRI summary measures (resting-state fluctuation amplitudes and functional connectivity) from CCA-ICA mode 4.

Supplementary Figure 6 More detailed visualization of the rfMRI summary measures (resting-state fluctuation amplitudes and functional connectivity) from CCA-ICA mode 5.

Supplementary Figure 7 More detailed visualization of the rfMRI summary measures (resting-state fluctuation amplitudes and functional connectivity) from CCA-ICA mode 6.

Supplementary Figure 8 Associations of the nine CCA-ICA modes with confounds and other variables of interest.

(a) As with the univariate analyses, data fed into the multivariate analyses were first adjusted for parameters that might otherwise induce apparent relationships based on potentially non-interesting factors (age, sex, head size, head motion). Here we show how, by projecting the CCA-ICA modes back onto the original data, we can estimate how strongly the discovered modes relate to these parameters. For example, modes 1,2,4,5,7,8 are associated with aging, whereas 3,6,9 are not strongly associated with age. Considering this in the light of the fact that all data input to the CCA had been age-adjusted suggests that, while these modes reflect meaningful biological processes related to aging, their identification here was not driven by trivial corruption of IDPs by aging (e.g., reduced fMRI signal due to cortical thinning). (b) Correlation of the CCA-ICA modes against several variables of interest, including some clinical outcomes (for which, at this stage, numbers are naturally quite limited). (c) Scatterplot of CCA-ICA modes 1 vs. 8 (one point per subject), showing the associations between these modes, age and sex. Colors running from green to red indicate increasing age in females; colors running from blue to pink indicate increasing age in males.

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Supplementary Figures 1–8 and Supplementary Table 1 (PDF 3206 kb)

Supplementary Methods Checklist (PDF 400 kb)

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Miller, K., Alfaro-Almagro, F., Bangerter, N. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19, 1523–1536 (2016). https://doi.org/10.1038/nn.4393

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