The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

[1]  J. Marchini,et al.  Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics , 2019, Brain : a journal of neurology.

[2]  P. Harst,et al.  Effect of Systolic Blood Pressure on Left Ventricular Structure and Function. , 2019, Hypertension.

[3]  A. Young,et al.  Changes in Cardiac Morphology and Function in Individuals With Diabetes Mellitus , 2019, Circulation. Cardiovascular imaging.

[4]  J. Sellors,et al.  The advantages of UK Biobank's open‐access strategy for health research , 2019, Journal of internal medicine.

[5]  Euan A. Ashley,et al.  Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences , 2019, Nature Communications.

[6]  Alejandro F. Frangi,et al.  Quantitative CMR population imaging on 20, 000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation , 2019, Medical Image Anal..

[7]  J. Pell,et al.  Association between APOE e4 and white matter hyperintensity volume, but not total brain volume or white matter integrity , 2019, Brain Imaging and Behavior.

[8]  Konrad Werys,et al.  Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data , 2019, PloS one.

[9]  S. Petersen,et al.  Physical activity and left ventricular trabeculation in the UK Biobank community-based cohort study , 2019, Heart.

[10]  Mark Hamer,et al.  Association of body mass index and waist-to-hip ratio with brain structure , 2019, Neurology.

[11]  C. Sudlow,et al.  Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11,226) , 2019, Neurology.

[12]  Mark E Bastin,et al.  Associations between vascular risk factors and brain MRI indices in UK Biobank , 2019, bioRxiv.

[13]  Alejandro F. Frangi,et al.  Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging With Fisher-Discriminative 3-D CNN , 2018, IEEE Transactions on Biomedical Engineering.

[14]  Cathie Sudlow,et al.  UK Biobank: opportunities for cardiovascular research , 2017, European heart journal.

[15]  H. Lamb,et al.  Erratum: Obesity, brain volume, and white matter microstructure at MRI: A cross-sectional UK biobank study (Radiology (2019) 291 (763-771) DOI: 10.1148/radiol.2019181012) , 2019 .

[16]  Jimmy D Bell,et al.  Measurement of liver iron by magnetic resonance imaging in the UK Biobank population , 2018, PloS one.

[17]  P. Munroe,et al.  Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes , 2018, Circulation.

[18]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[19]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[20]  Tian Ge,et al.  The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology. , 2018, Cerebral cortex.

[21]  Chloe Hutton,et al.  Validation of a standardized MRI method for liver fat and T2* quantification , 2018, PloS one.

[22]  S. Yang,et al.  Bone mineral density assessment for research purpose using dual energy X-ray absorptiometry , 2018, Osteoporosis and sarcopenia.

[23]  Aron K Barbey,et al.  Small sample sizes reduce the replicability of task-based fMRI studies , 2018, Communications Biology.

[24]  Magnus Borga,et al.  Body Composition Profiling in the UK Biobank Imaging Study , 2018, Obesity.

[25]  M. Dichgans,et al.  Genetic Study of White Matter Integrity in UK Biobank (N=8448) and the Overlap With Stroke, Depression, and Dementia , 2018, Stroke.

[26]  Magnus Borga,et al.  Advanced body composition assessment: from body mass index to body composition profiling , 2018, Journal of Investigative Medicine.

[27]  S. Petersen,et al.  Variation in lung function and alterations in cardiac structure and function—Analysis of the UK Biobank cardiovascular magnetic resonance imaging substudy , 2018, PloS one.

[28]  S. Petersen,et al.  The impact of menopausal hormone therapy (MHT) on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2018, PloS one.

[29]  Jonathan W. Waks,et al.  Global ECG Measures and Cardiac Structure and Function: The ARIC Study (Atherosclerosis Risk in Communities). , 2018, Circulation. Arrhythmia and electrophysiology.

[30]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[31]  Ben Glocker,et al.  Automated cardiovascular magnetic resonance image analysis with fully convolutional networks , 2017, Journal of Cardiovascular Magnetic Resonance.

[32]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[33]  C. Sudlow,et al.  Impact of detecting potentially serious incidental findings during multi-modal imaging , 2018, Wellcome open research.

[34]  Quincy M. Samus,et al.  Dementia prevention, intervention, and care , 2017, The Lancet.

[35]  Ben Glocker,et al.  Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension , 2017, PloS one.

[36]  C. Denson The MESA Study. , 2017 .

[37]  Jackie A Cooper,et al.  The impact of cardiovascular risk factors on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2017, PloS one.

[38]  C. Sudlow,et al.  Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment , 2017, European journal of preventive cardiology.

[39]  Carmel Hayes,et al.  Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results , 2017, The International Journal of Cardiovascular Imaging.

[40]  I. Deary,et al.  Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766) , 2017, Translational Psychiatry.

[41]  C. Sudlow,et al.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population , 2017, American journal of epidemiology.

[42]  Saifeng Liu,et al.  Susceptibility‐weighted imaging: current status and future directions , 2017, NMR in biomedicine.

[43]  Stefan Neubauer,et al.  Characterisation of liver fat in the UK Biobank cohort , 2017, PloS one.

[44]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[45]  Stefan K. Piechnik,et al.  Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort , 2017, Journal of Cardiovascular Magnetic Resonance.

[46]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[47]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[48]  H. Whalley,et al.  Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank , 2016, Scientific Reports.

[49]  Magnus Borga,et al.  Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies , 2016, PloS one.

[50]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[51]  H. Tilg,et al.  Nonalcoholic fatty liver disease and hepatocellular carcinoma. , 2016, Metabolism: Clinical and Experimental.

[52]  John Eng,et al.  Adverse Left Ventricular Remodeling and Age Assessed with Cardiac MR Imaging: The Multi-Ethnic Study of Atherosclerosis. , 2016, Radiology.

[53]  A. Hofman,et al.  The Rotterdam Scan Study: design update 2016 and main findings , 2015, European Journal of Epidemiology.

[54]  P. Matthews,et al.  UK Biobank’s cardiovascular magnetic resonance protocol , 2015, Journal of Cardiovascular Magnetic Resonance.

[55]  J. Linseisen,et al.  Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. , 2015, Radiology.

[56]  Jennifer S Gregory,et al.  Reproducibility and Diagnostic Accuracy of Kellgren-Lawrence Grading for Osteoarthritis Using Radiographs and Dual-Energy X-ray Absorptiometry Images. , 2015, Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry.

[57]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[58]  C. Morrison,et al.  Hormonal Contraception and the Risk of HIV Acquisition: An Individual Participant Data Meta-analysis , 2015, PLoS medicine.

[59]  D. Bluemke,et al.  Proximal aortic distensibility is an independent predictor of all-cause mortality and incident CV events: the MESA study. , 2014, Journal of the American College of Cardiology.

[60]  M. Robson,et al.  Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease , 2014, Journal of hepatology.

[61]  Whal Lee General principles of carotid Doppler ultrasonography , 2013, Ultrasonography.

[62]  P. Matthews,et al.  Osteoporosis epidemiology in UK Biobank: a unique opportunity for international researchers , 2013, Osteoporosis International.

[63]  Udo Hoffmann,et al.  Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. , 2013, Journal of the American College of Cardiology.

[64]  P. Matthews,et al.  Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches , 2013, Journal of Cardiovascular Magnetic Resonance.

[65]  Stuart Moss,et al.  Current Status and Future Directions , 2013 .

[66]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[67]  J. Carr,et al.  The revolution in risk assessment and disease detection made possible with non-invasive imaging: implications for population science. , 2012, Ethnicity & disease.

[68]  Jimmy D Bell,et al.  Excess body fat in obese and normal-weight subjects , 2012, Nutrition Research Reviews.

[69]  E. Vittinghoff,et al.  Association of major and minor ECG abnormalities with coronary heart disease events. , 2012, JAMA.

[70]  Jimmy D Bell,et al.  The Missing Risk: MRI and MRS Phenotyping of Abdominal Adiposity and Ectopic Fat , 2012, Obesity.

[71]  M. Szklo,et al.  The Multi-Ethnic Study of Atherosclerosis (MESA) , 2012 .

[72]  A. Dale,et al.  Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. , 2011, Radiology.

[73]  Anne Corbett,et al.  Depression and dementia. , 2011, Mental health today.

[74]  W. Brown Framingham Heart Study. , 2011, Journal of clinical lipidology.

[75]  H. Markus,et al.  The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.

[76]  Abdominal fat from spine dual-energy x-ray absorptiometry and risk for subsequent diabetes. , 2010, The Journal of clinical endocrinology and metabolism.

[77]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[78]  Udo Hoffmann,et al.  Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the Framingham Heart Study. , 2008, European heart journal.

[79]  C. Roux,et al.  DXA scanning in clinical practice. , 2008, QJM : monthly journal of the Association of Physicians.

[80]  Christopher B. Kendall,et al.  Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. , 2008, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[81]  Philip Greenland,et al.  Major and minor ECG abnormalities in asymptomatic women and risk of cardiovascular events and mortality. , 2007, JAMA.

[82]  S. Kahn,et al.  Review: The role of insulin resistance in nonalcoholic fatty liver disease. , 2006, The Journal of clinical endocrinology and metabolism.

[83]  R. Ross,et al.  Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men , 2006, Obesity.

[84]  Karl Swedberg,et al.  Influence of Ejection Fraction on Cardiovascular Outcomes in a Broad Spectrum of Heart Failure Patients , 2005, Circulation.

[85]  Ronald M Peshock,et al.  The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. , 2004, The American journal of cardiology.

[86]  R. Kronmal,et al.  Multi-Ethnic Study of Atherosclerosis: objectives and design. , 2002, American journal of epidemiology.

[87]  Francesco Fera,et al.  The Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes , 2002, NeuroImage.

[88]  R. Collins,et al.  Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. , 1999, American journal of epidemiology.

[89]  D. Hans,et al.  How Hip and Whole-Body Bone Mineral Density Predict Hip Fracture in Elderly Women: The EPIDOS Prospective Study , 1998, Osteoporosis International.

[90]  A. Folsom,et al.  Correlates of uric acid and its association with asymptomatic carotid atherosclerosis: the ARIC Study. Atherosclerosis Risk in Communities. , 1996, Annals of epidemiology.

[91]  R B D'Agostino,et al.  Left atrial size and the risk of stroke and death. The Framingham Heart Study. , 1995, Circulation.

[92]  Robert Epstein,et al.  Comparison of methods for defining prevalent vertebral deformities: The study of osteoporotic fractures , 1995, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[93]  Claus Christiansen,et al.  Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group. , 1994, World Health Organization technical report series.

[94]  D. Levy,et al.  Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. , 1990, The New England journal of medicine.

[95]  M. Goklany Report of Study Group I , 1973 .