A review on the application of UK Biobank in neuroimaging_Journal of Biomedical Engineering

Authors：

 LIN Lan , XIONG Min , WU Shuicai

Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P.R.China;

Corresponding author：

LIN Lan, Email: lanlin@bjut.edu.cn

Keywords：

UK Biobank; brain aging; neuroimaging; multimodality magnetic resonance imaging; biomedical

DOI：

10.7507/1001-5515.202012059

Video：

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UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging. Firstly, neuroimage collection and imaging-derived phenotypes are summarized. Secondly, typical studies of UKB in neuroimaging areas are introduced, which include cardiovascular risk factors, regulatory factors, brain age prediction, normality, successful and morbid brain aging, environmental and genetic factors, cognitive ability and gender. Lastly, the open challenges and future directions of UKB are discussed. This article has the potential to open up a new research field for the prevention and treatment of neurological diseases.

Citation： LIN Lan, XIONG Min, WU Shuicai. A review on the application of UK Biobank in neuroimaging. Journal of Biomedical Engineering, 2021, 38(3): 594-601. doi: 10.7507/1001-5515.202012059 Copy

1.	Trehearne A. Counting on big numbers. Int J Surg, 2009, 7(5): 413-415.
2.	Mcintosh A M, Stewart R, John A, et al. Data science for mental health: a UK perspective on a global challenge. Lancet Psychiatry, 2016, 3(10): 993-998.
3.	Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015, 12(3): e1001779.
4.	Littlejohns T J, Holliday J, Gibson L M, et al. The UK biobank imaging enhancement of 100, 000 participants: rationale, data collection, management and future directions. Nat Commun, 2020, 11(1): 2624.
5.	Miller K L, Alfaro-Almagro F, Bangerter N K, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci, 2016, 19(11): 1523-1536.
6.	de Winter C F, Bastiaanse L P, Hilgenkamp T I M, et al. Cardiovascular risk factors (diabetes, hypertension, hypercholesterolemia and metabolic syndrome) in older people with intellectual disability: results of the HA-ID study. Res Dev Disabil, 2012, 33(6): 1722-1731.
7.	Sundaresan V, Griffanti L, Kindalova P, et al. Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference. Neuroimage, 2019, 185: 434-445.
8.	Feng R, Rolls E T, Cheng W, et al. Hypertension is associated with reduced hippocampal connectivity and impaired memory. EBioMedicine, 2020, 61: 103082.
9.	Parimisetty A, Dorsemans A C, Awada R, et al. Secret talk between adipose tissue and central nervous system via secreted factors-an emerging frontier in the neurodegenerative research. J Neuroinflammation, 2016, 13(1): 67.
10.	Dekkers I A, Jansen P R, Lamb H J. Obesity, brain volume, and white matter microstructure at MRI: a cross-sectional UK Biobank study. Radiology, 2019, 291(3): 763-771.
11.	Hamer M, Batty G D. Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study. Neurology, 2019, 92(6): e594-e600.
12.	Cox S R, Lyall D M, Ritchie S J, et al. Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J, 2019, 40(28): 2290-2300.
13.	Veldsman M, Tai X Y, Nichols T, et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat Commun, 2020, 11(1): 4340.
14.	林岚, 张柏雯, 王婧璇, 等. 认知储备在大脑老化中的研究进展. 医疗卫生装备, 2017, 38(9): 93-98.
15.	Hamer M, Sharma N, Batty G D. Association of objectively measured physical activity with brain structure: UK Biobank study. J Intern Med, 2018, 284(4): 439-443.
16.	Taebi A, Kiesow H, Vogeley K, et al. Population variability in social brain morphology for social support, household size and friendship satisfaction. Soc Cogn Affect Neurosci, 2020, 15(6): 635-647.
17.	Cheng W, Rolls E T, Ruan H, et al. Functional connectivities in the brain that mediate the association between depressive problems and sleep quality. JAMA Psychiatry, 2018, 75(10): 1052-1061.
18.	林岚, 王婧璇, 付振荣, 等. 脑老化中脑年龄预测模型研究综述. 生物医学工程学杂志, 2019, 36(3): 493-498.
19.	Dinsdale N K, Bluemke E, Smith S M, et al. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage, 2021, 224: 117401.
20.	Peng H, Gong W, Beckmann C F, et al. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal, 2021, 68: 101871.
21.	Cole J H. Multimodality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging, 2020, 92: 34-42.
22.	Smith S M, Elliott L T, Alfaro-Almagro F, et al. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. Elife, 2020, 9: e52677.
23.	林岚, 张格, 吴水才. 脑年龄估计差作为脑老化生物标志物的研究进展. 北京工业大学学报, 2021, 47(3): 303-310.
24.	Kolbeinsson A, Filippi S, Panagakis Y, et al. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep, 2020, 10(1): 19940.
25.	Smith S M, Vidaurre D, Alfaro-Almagro F, et al. Estimation of brain age delta from brain imaging. Neuroimage, 2019, 200: 528-539.
26.	Feng X, Guo J, Sigmon H C, et al. Brain regions vulnerable and resistant to aging without Alzheimer's disease. PLoS One, 2020, 15(7): e0234255.
27.	Estebsari F, Dastoorpoor M, Khalifehkandi Z R, et al. The concept of successful aging: a review article. Curr Aging Sci, 2020, 13(1): 4-10.
28.	Tseng W Y I, Hsu Y C, Chen C L, et al. Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7 167 UK Biobank participants. Neurobiol Aging, 2021, 98: 160-172.
29.	Buchanan C R, Bastin M E, Ritchie S J, et al. The effect of network thresholding and weighting on structural brain networks in the UK Biobank. Neuroimage, 2020, 211: 116443.
30.	Cox S R, Ritchie S J, Tucker-Drob E M, et al. Ageing and brain white matter structure in 3, 513 UK Biobank participants. Nat Commun, 2016, 7: 13629.
31.	Tian Q, Pilling L C, Atkins J L, et al. The relationship of parental longevity with the aging brain-results from UK Biobank. Geroscience, 2020, 42(5): 1377-1385.
32.	Nobis L, Manohar S G, Smith S M, et al. Hippocampal volume across age: nomograms derived from over 19, 700 people in UK Biobank. Neuroimage Clin, 2019, 23: 101904.
33.	Foo H, Thalamuthu A, Jiang J, et al. Associations between Alzheimer's disease polygenic risk scores and hippocampal subfield volumes in 17, 161 UK Biobank participants. Neurobiol Aging, 2021, 98: 108-115.
34.	Newbury J B, Arseneault L, Beevers S, et al. Association of air pollution exposure with psychotic experiences during adolescence. JAMA Psychiatry, 2019, 76(6): 614-623.
35.	Gale S D, Erickson L D, Kunzelman J E, et al. Association between exposure to air pollution and prefrontal cortical volume in adults: a cross-sectional study from the UK Biobank. Environ Res, 2020, 185: 109365.
36.	Hedges D W, Erickson L D, Kunzelman J, et al. Association between exposure to air pollution and hippocampal volume in adults in the UK Biobank. Neurotoxicology, 2019, 74: 108-120.
37.	Elliott L T, Sharp K, Alfaro-Almagro F, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 2018, 562(7726): 210-216.
38.	Satizabal C L, Adams H, Hibar D P, et al. Genetic architecture of subcortical brain structures in 38, 851 individuals. Nat Genet, 2019, 51(11): 1624-1636.
39.	Cox S R, Ritchie S J, Fawns-Ritchie C, et al. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence, 2019, 76: 101376.
40.	Ritchie S J, Cox S R, Shen X, et al. Sex differences in the adult human brain: evidence from 5 216 UK Biobank participants. Cereb Cortex, 2018, 28(8): 2959-2975.
41.	康文杰, 林岚, 孙珅, 等. 生成对抗网络及其在神经影像应用中的研究进展. 医疗卫生装备, 2020, 41(9): 87-93, 108.
42.	吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
43.	田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
44.	Alfaro-Almagro F, Mccarthy P, Afyouni S, et al. Confound modelling in UK Biobank brain imaging. Neuroimage, 2021, 224: 117002.

1. Trehearne A. Counting on big numbers. Int J Surg, 2009, 7(5): 413-415.
2. Mcintosh A M, Stewart R, John A, et al. Data science for mental health: a UK perspective on a global challenge. Lancet Psychiatry, 2016, 3(10): 993-998.
3. Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015, 12(3): e1001779.
4. Littlejohns T J, Holliday J, Gibson L M, et al. The UK biobank imaging enhancement of 100, 000 participants: rationale, data collection, management and future directions. Nat Commun, 2020, 11(1): 2624.
5. Miller K L, Alfaro-Almagro F, Bangerter N K, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci, 2016, 19(11): 1523-1536.
6. de Winter C F, Bastiaanse L P, Hilgenkamp T I M, et al. Cardiovascular risk factors (diabetes, hypertension, hypercholesterolemia and metabolic syndrome) in older people with intellectual disability: results of the HA-ID study. Res Dev Disabil, 2012, 33(6): 1722-1731.
7. Sundaresan V, Griffanti L, Kindalova P, et al. Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference. Neuroimage, 2019, 185: 434-445.
8. Feng R, Rolls E T, Cheng W, et al. Hypertension is associated with reduced hippocampal connectivity and impaired memory. EBioMedicine, 2020, 61: 103082.
9. Parimisetty A, Dorsemans A C, Awada R, et al. Secret talk between adipose tissue and central nervous system via secreted factors-an emerging frontier in the neurodegenerative research. J Neuroinflammation, 2016, 13(1): 67.
10. Dekkers I A, Jansen P R, Lamb H J. Obesity, brain volume, and white matter microstructure at MRI: a cross-sectional UK Biobank study. Radiology, 2019, 291(3): 763-771.
11. Hamer M, Batty G D. Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study. Neurology, 2019, 92(6): e594-e600.
12. Cox S R, Lyall D M, Ritchie S J, et al. Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J, 2019, 40(28): 2290-2300.
13. Veldsman M, Tai X Y, Nichols T, et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat Commun, 2020, 11(1): 4340.
14. 林岚, 张柏雯, 王婧璇, 等. 认知储备在大脑老化中的研究进展. 医疗卫生装备, 2017, 38(9): 93-98.
15. Hamer M, Sharma N, Batty G D. Association of objectively measured physical activity with brain structure: UK Biobank study. J Intern Med, 2018, 284(4): 439-443.
16. Taebi A, Kiesow H, Vogeley K, et al. Population variability in social brain morphology for social support, household size and friendship satisfaction. Soc Cogn Affect Neurosci, 2020, 15(6): 635-647.
17. Cheng W, Rolls E T, Ruan H, et al. Functional connectivities in the brain that mediate the association between depressive problems and sleep quality. JAMA Psychiatry, 2018, 75(10): 1052-1061.
18. 林岚, 王婧璇, 付振荣, 等. 脑老化中脑年龄预测模型研究综述. 生物医学工程学杂志, 2019, 36(3): 493-498.
19. Dinsdale N K, Bluemke E, Smith S M, et al. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage, 2021, 224: 117401.
20. Peng H, Gong W, Beckmann C F, et al. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal, 2021, 68: 101871.
21. Cole J H. Multimodality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging, 2020, 92: 34-42.
22. Smith S M, Elliott L T, Alfaro-Almagro F, et al. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. Elife, 2020, 9: e52677.
23. 林岚, 张格, 吴水才. 脑年龄估计差作为脑老化生物标志物的研究进展. 北京工业大学学报, 2021, 47(3): 303-310.
24. Kolbeinsson A, Filippi S, Panagakis Y, et al. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep, 2020, 10(1): 19940.
25. Smith S M, Vidaurre D, Alfaro-Almagro F, et al. Estimation of brain age delta from brain imaging. Neuroimage, 2019, 200: 528-539.
26. Feng X, Guo J, Sigmon H C, et al. Brain regions vulnerable and resistant to aging without Alzheimer's disease. PLoS One, 2020, 15(7): e0234255.
27. Estebsari F, Dastoorpoor M, Khalifehkandi Z R, et al. The concept of successful aging: a review article. Curr Aging Sci, 2020, 13(1): 4-10.
28. Tseng W Y I, Hsu Y C, Chen C L, et al. Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7 167 UK Biobank participants. Neurobiol Aging, 2021, 98: 160-172.
29. Buchanan C R, Bastin M E, Ritchie S J, et al. The effect of network thresholding and weighting on structural brain networks in the UK Biobank. Neuroimage, 2020, 211: 116443.
30. Cox S R, Ritchie S J, Tucker-Drob E M, et al. Ageing and brain white matter structure in 3, 513 UK Biobank participants. Nat Commun, 2016, 7: 13629.
31. Tian Q, Pilling L C, Atkins J L, et al. The relationship of parental longevity with the aging brain-results from UK Biobank. Geroscience, 2020, 42(5): 1377-1385.
32. Nobis L, Manohar S G, Smith S M, et al. Hippocampal volume across age: nomograms derived from over 19, 700 people in UK Biobank. Neuroimage Clin, 2019, 23: 101904.
33. Foo H, Thalamuthu A, Jiang J, et al. Associations between Alzheimer's disease polygenic risk scores and hippocampal subfield volumes in 17, 161 UK Biobank participants. Neurobiol Aging, 2021, 98: 108-115.
34. Newbury J B, Arseneault L, Beevers S, et al. Association of air pollution exposure with psychotic experiences during adolescence. JAMA Psychiatry, 2019, 76(6): 614-623.
35. Gale S D, Erickson L D, Kunzelman J E, et al. Association between exposure to air pollution and prefrontal cortical volume in adults: a cross-sectional study from the UK Biobank. Environ Res, 2020, 185: 109365.
36. Hedges D W, Erickson L D, Kunzelman J, et al. Association between exposure to air pollution and hippocampal volume in adults in the UK Biobank. Neurotoxicology, 2019, 74: 108-120.
37. Elliott L T, Sharp K, Alfaro-Almagro F, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 2018, 562(7726): 210-216.
38. Satizabal C L, Adams H, Hibar D P, et al. Genetic architecture of subcortical brain structures in 38, 851 individuals. Nat Genet, 2019, 51(11): 1624-1636.
39. Cox S R, Ritchie S J, Fawns-Ritchie C, et al. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence, 2019, 76: 101376.
40. Ritchie S J, Cox S R, Shen X, et al. Sex differences in the adult human brain: evidence from 5 216 UK Biobank participants. Cereb Cortex, 2018, 28(8): 2959-2975.
41. 康文杰, 林岚, 孙珅, 等. 生成对抗网络及其在神经影像应用中的研究进展. 医疗卫生装备, 2020, 41(9): 87-93, 108.
42. 吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
43. 田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
44. Alfaro-Almagro F, Mccarthy P, Afyouni S, et al. Confound modelling in UK Biobank brain imaging. Neuroimage, 2021, 224: 117002.

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