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UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer’s disease and mitochondrial disorders. The Elliott and Smith teams used imaging and genetics data from 40,000 volunteers in the UK Biobank healthcare study, discovering new genetic influences over brain structure and function, which are of relevance to both rare and common diseases.

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world’s largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions. Between 2014 and 2023, 100,000 UK Biobank participants are undergoing brain, heart and abdominal MRI, as well as DXA and carotid ultrasound scans. In this review, authors provide a detailed overview of the rationale for the collection of these imaging data, the procedures of data collection and management, and the future directions of the UK biobank imaging enhancement.

Brain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single ‘brain age’ is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

Interoception is the sensation, perception, and integration of signals from within the body. It has been associated with a broad range of physiological and psychological processes. Further, interoceptive variables are related to specific regions and networks in the human brain. However, it is not clear whether or how these networks relate empirically to different domains of physiological and psychological health at the population level. We analysed a data set of 19,020 individuals (10,055 females, 8965 males; mean age: 63 years, age range: 45–81 years), who have participated in the UK Biobank Study, a very large‐scale prospective epidemiological health study. Using canonical correlation analysis (CCA), allowing for the examination of associations between two sets of variables, we related the functional connectome of brain regions implicated in interoception to a selection of nonimaging health and lifestyle related phenotypes, exploring their relationship within modes of population co‐variation. In one integrated and data driven analysis, we obtained four statistically significant modes. Modes could be categorised into domains of arousal and affect and cardiovascular health, respiratory health, body mass, and subjective health (all p < .0001) and were meaningfully associated with distinct neural circuits. Circuits represent specific neural “fingerprints” of functional domains and set the scope for future studies on the neurobiology of interoceptive involvement in different lifestyle and health‐related phenotypes. Therefore, our research contributes to the conceptualisation of interoception and may lead to a better understanding of co‐morbid conditions in the light of shared interoceptive structures.

Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91–0.96), but not stroke or Parkinson’s disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.

The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P  < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing. Genome-wide association studies of brain imaging data from 8,428 individuals in UK Biobank show that many of the 3,144 traits studied are heritable, and genes associated with individual phenotypes are identified.

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts. •Two tools to automatically perform QC of diffusion MRI data.•Automated generation of single subject reports for visual inspection and database.•Group databases and reports allow to compare subjects within and between studies.•Categorical and continuous variables can be used to update the reports.

Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

We present a practical “how-to” guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.