Multimodal population brain imaging in the UK Biobank prospective epidemiological study

By Karla L. Miller, Fidel Alfaro-Almagro, N. Bangerter, David L. Thomas, Essa Yacoub, Junqian Xu, Andreas J. Bartsch, S. Jbabdi, Stamatios N. Sotiropoulos, Jesper L. R. Andersson and 11 more. Nature Neuroscience, 2016

1,045 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9d9f231bc047b2e0ec910b3cbddd5783b18e2ada

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Citations in the corpus

The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, neutral): “These innovations have been incorporated into other major neuroimaging efforts including, but not limited to, The Developing Human Connectome Project (http://www.developingconnectome.org/project/) in the UK, the UK Biobank (http://www.ukbiobank.ac.uk/; Miller et al., 2016), the Adolescent Brain Cogn”
Statistical Challenges in "Big Data" Human Neuroimaging. (2018, neutral): “For example, in (Miller et al. 2016), 9 significant CCA modes were “unmixed” into 9 ICA modes with enhanced reproducibility and biological interpretability, for example, relating measures of blood pressure, alcohol intake, and changes in white matter cell microstructure to each other, with up to…”
Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment (2017, neutral): “Previous publications have described the UK Biobank in general,(1) and the imaging substudies involving cardiac MRI,(2,9) liver MRI(10) and brain MRI.(11) In this article, we describe the carotid ultrasound scanning protocol used within the UK Biobank Imaging Enhancement Study and the development of”
Federated learning for medical image analysis: A survey (2023, background): “5) UK BioBank: UK Biobank [99] is a large-scale brain imaging dataset that consists of around 100,000 participants with brain imaging in structural, functional, and diffusion modalities.”
Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction (2018, background): “Details on the data acquisition protocols are provided elsewhere (Filippini et al., 2014; Miller et al., 2016; Sotiropoulos et al., 2013b).”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “In November 2015, there was a public release of the imaging datasets and IDPs from the first 5822 subjects (Miller et al., 2016); a new set of 4282 subjects has recently been processed and publicly released in February 2017 (Biobank, 2017). The number of datasets from every modality that were deemed”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, neutral): “Larger datasets bring increased statistical power (Elliott et al., 2008), and many population-imaging studies use rest-fMRI to relate brain imaging to neuropathologies or other behavior and population phenotypes (Miller et al., 2016; Dubois and Adolphs, 2016).”