UK Biobank Brain Imaging - Online Resources

Acquisition and Analysis Documentation

Primary brain imaging documentation (this includes details of different data releases).

Acquisition: material relating to setting up and running the UK Biobank brain imaging protocol.

Analysis: material relating to the central image processing pipeline.

Genetics: Oxford Brain Imaging Genetics (BIG40) interactive web server (with SNPxIDP summary statistic downloads). Also, GWAS summary statistic resources for QSM/T2*, white matter lesions and task-fMRI.

UKB Brain Imaging "Core" Papers

• Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Miller et al, Nature Neuroscience.
  Overview of UKB brain imaging resource, with example initial analyses.   [Resources]

• Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Alfaro-Almagro et al, NeuroImage
  Description of the core image processing pipeline for UKB brain imaging.   [Resources including code]

• Confound modelling in UK Biobank brain imaging. Alfaro-Almagro et al, bioRxiv.
  [Resources]

• Genome-wide association studies of brain imaging phenotypes in UK Biobank. Elliott et al, Nature.
  Large-scale GWAS (and other genetic analyses) applied to 3144 IDPs and 11 million SNPs, in data from 10,000 participants.
  [Resources: code and IDP information and Oxford Brain Imaging Genetics (BIG) interactive web server]

• Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Wang et al, Nature Neuroscience.

• The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure. Mollink et al, Nature Neuroscience
• Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics. Wiberg et al, Brain.
• Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. Smith et al, eLife.   [Resources]
• SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Douaud et al, Nature.   [Resources]
• The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease. Manuello et al, Nature Communications.   [Resources]
• Multiscale Modes of Functional Brain Connectivity. Farahibozorg et al, Preprint.   [Resources]

Average templates and other image resources

Multiple structural and task-fMRI group-average images

White matter tract orientations

• DTI tensor fit line view (one colour per principal tract)
• Probabilistic tract line view (one colour per tract)
• DTI and probabilistic tract line views (one view per tract, coloured according to orientation)

Surface-based resting-state networks

Volumetric resting-state networks

The 3D-maps may take a few minutes to fully load. The estimation of these networks is now briefly described:

Resting-state functional MRI data from more than 4000 UK Biobank participants was combined in order to carry out a large-scale population-average mapping of the major functional networks in the brain. All subjects' structural and functional images were first spatially aligned to each other. Next, an analysis method known as "group-ICA" was used to identify the major resting-state networks/regions; this decomposes the data into a specified number of networks, and was run at two different dimensionalities (25 and 100). The dimensionality determines the number of distinct ICA components; a higher number means that the above-threshold regions within the spatial maps will be smaller.

Network matrices (representing the nodesXnodes functional connectivity) have been estimated for all subjects. Because these are symmetric about the diagonal, only the above-diagonal part of the matrices is stored by UK Biobank. See here for more information on how to work with these files.

Components that are not neuronally driven are discarded during network connectivity modelling (and so don't appear in the maps and connectomes listed above, which therefore have less than the 25/100 original number of components). The lists of good components (with component numbering starting at 1 not 0) are (for the two group-ICA dimensionalities) rfMRI_GoodComponents_d25_v1.txt and rfMRI_GoodComponents_d100_v1.txt. The full original sets of 3D spatial maps, including "bad" nodes are here: 25-components, 100-components).

The dense (voxelwise) connectome can be computed via the MIGP group-PCA Wishart-adjusted weighted eigenvectors file (1.1GB). Simply take the correlation of any voxel's timeseries with all other voxels' timeseries to get a seed-correlation map (i.e., one entry from the full dense connectome). You can generate these single-seed correlation maps on the fly in FSLeyes, by loading the eigenvectors file, clicking on a seed voxel, and pressing Overlay: Seed correlation.