{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T18:54:13Z","timestamp":1735584853580},"reference-count":74,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1016\/j.neuroimage.2020.117292","type":"journal-article","created":{"date-parts":[[2020,8,21]],"date-time":"2020-08-21T15:16:36Z","timestamp":1598022996000},"page":"117292","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":99,"special_numbering":"C","title":["Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study"],"prefix":"10.1016","volume":"222","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5150-6656","authenticated-orcid":false,"given":"Ann-Marie G.","family":"de Lange","sequence":"first","affiliation":[]},{"given":"Melis","family":"Anat\u00fcrk","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5136-8302","authenticated-orcid":false,"given":"Sana","family":"Suri","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Kaufmann","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1908-5588","authenticated-orcid":false,"given":"James H.","family":"Cole","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0540-9353","authenticated-orcid":false,"given":"Ludovica","family":"Griffanti","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0478-6165","authenticated-orcid":false,"given":"Enik\u0151","family":"Zsoldos","sequence":"additional","affiliation":[]},{"given":"Daria E.A.","family":"Jensen","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1513-5269","authenticated-orcid":false,"given":"Nicola","family":"Filippini","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1244-5037","authenticated-orcid":false,"given":"Archana","family":"Singh-Manoux","sequence":"additional","affiliation":[]},{"given":"Mika","family":"Kivim\u00e4ki","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8644-956X","authenticated-orcid":false,"given":"Lars T.","family":"Westlye","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5190-7038","authenticated-orcid":false,"given":"Klaus P.","family":"Ebmeier","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neuroimage.2020.117292_bib0001","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","article-title":"Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank","volume":"166","author":"Alfaro-Almagro","year":"2018","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0002","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1093\/cercor\/bhs352","article-title":"Tracking whole-brain connectivity dynamics in the resting state","volume":"24","author":"Allen","year":"2014","journal-title":"Cerebral Cortex"},{"issue":"Suppl 1","key":"10.1016\/j.neuroimage.2020.117292_bib0003","doi-asserted-by":"crossref","first-page":"S148","DOI":"10.1016\/S1053-8119(09)71511-3","article-title":"Group comparison of resting-state FMRI data using multi-subject ICA and dual regression","volume":"47","author":"Beckmann","year":"2009","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.neuroimage.2020.117292_bib0004","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TMI.2003.822821","article-title":"Probabilistic independent component analysis for functional magnetic resonance imaging","volume":"23","author":"Beckmann","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0005","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc.: Series B (Methodol.)"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0006","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s13195-018-0434-3","article-title":"Functional connectivity in cognitive control networks mitigates the impact of white matter lesions in the elderly","volume":"10","author":"Benson","year":"2018","journal-title":"Alzheimer\u2019s Res. Therapy"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0007","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neuroimage.2014.07.067","article-title":"Changes in structural and functional connectivity among resting-state networks across the human lifespan","volume":"102","author":"Betzel","year":"2014","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0008","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.neuroimage.2012.08.010","article-title":"Classifying fMRI-derived resting-state connectivity patterns according to their daily rhythmicity","volume":"71","author":"Blautzik","year":"2013","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0009","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"18","key":"10.1016\/j.neuroimage.2020.117292_bib0010","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1016\/j.cub.2012.07.002","article-title":"Neuroanatomical assessment of biological maturity","volume":"22","author":"Brown","year":"2012","journal-title":"Current Biol."},{"issue":"11","key":"10.1016\/j.neuroimage.2020.117292_bib0011","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1038\/s41583-018-0068-2","article-title":"Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing","volume":"19","author":"Cabeza","year":"2018","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0012","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016"},{"issue":"5","key":"10.1016\/j.neuroimage.2020.117292_bib0013","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/JBHI.2016.2559938","article-title":"Importance of multimodal MRI in characterizing brain tissue and its potential application for individual age prediction","volume":"20","author":"Cherubini","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.neurobiolaging.2020.03.014","article-title":"Multi-modality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle and cognitive factors","author":"Cole","year":"2020","journal-title":"Neurobiol. Aging"},{"issue":"12","key":"10.1016\/j.neuroimage.2020.117292_bib0015","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.tins.2017.10.001","article-title":"Predicting age using neuroimaging: innovative brain ageing biomarkers","volume":"40","author":"Cole","year":"2017","journal-title":"Trends Neurosci."},{"issue":"2","key":"10.1016\/j.neuroimage.2020.117292_bib0016","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1038\/s41380-018-0098-1","article-title":"Brain age and other bodily ages: implications for neuropsychiatry","volume":"24","author":"Cole","year":"2019","journal-title":"Mol. Psychiatry"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0017","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","article-title":"Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker","volume":"163","author":"Cole","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0018","doi-asserted-by":"crossref","unstructured":"Cole, J. H., Raffel, J., Friede, T., Eshaghi, A., Brownlee, W., Chard, D., De Stefano, N., Enzinger, C., Pirpamer, L., Filippi, M., et\u00a0al., 2019b. Accelerated brain ageing and disability in multiple sclerosis. bioRxiv 584888.","DOI":"10.1101\/584888"},{"issue":"5","key":"10.1016\/j.neuroimage.2020.117292_bib0019","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1038\/mp.2017.62","article-title":"Brain age predicts mortality","volume":"23","author":"Cole","year":"2018","journal-title":"Mol. Psychiatry"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0020","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1161\/01.STR.25.1.40","article-title":"Stroke risk profile: adjustment for antihypertensive medication. the framingham study","volume":"25","author":"D\u2019Agostino","year":"1994","journal-title":"Stroke"},{"issue":"37","key":"10.1016\/j.neuroimage.2020.117292_bib0021","doi-asserted-by":"crossref","first-page":"13848","DOI":"10.1073\/pnas.0601417103","article-title":"Consistent resting-state networks across healthy subjects","volume":"103","author":"Damoiseaux","year":"2006","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0043","doi-asserted-by":"crossref","unstructured":"de Lange, A-M.G., Barth, C., Kaufmann, T., Maximov, I., van der Meer, D., Agartz, I., Westlye, L. T., 2020a. Women's brain aging: Effects of sex-hormone exposure, pregnancies, and genetic risk for Alzheimer's disease. Human Brain Mapping (in press), doi:10.1002\/hbm.25180.","DOI":"10.1101\/826123"},{"key":"10.1016\/j.neuroimage.2020.117292_bib74","doi-asserted-by":"crossref","DOI":"10.1002\/hbm.25152","article-title":"The maternal brain: Region\u2010specific patterns of brain aging are traceable decades after childbirth","author":"de Lange","year":"2020","journal-title":"Human Brain Mapping"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0044","article-title":"Commentary: correction procedures in brain-age prediction","volume":"26","author":"de Lange","year":"2020","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0045","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.1910666116","article-title":"Population-based neuroimaging reveals traces of childbirth in the maternal brain","author":"de Lange","year":"2019","journal-title":"Proc. Natl. Acad. Sci"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0022","doi-asserted-by":"crossref","first-page":"c3666","DOI":"10.1136\/bmj.c3666","article-title":"The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis","volume":"341","author":"Debette","year":"2010","journal-title":"BMJ"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0023","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neurobiolaging.2018.06.013","article-title":"Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods","volume":"71","author":"Eavani","year":"2018","journal-title":"Neurobiol. Aging"},{"issue":"17","key":"10.1016\/j.neuroimage.2020.117292_bib0024","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1073\/pnas.0811879106","article-title":"Distinct patterns of brain activity in young carriers of the APOE-\u03b54 allele","volume":"106","author":"Filippini","year":"2009","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0025","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/1471-244X-14-159","article-title":"Study protocol: the whitehall II imaging sub-study","volume":"14","author":"Filippini","year":"2014","journal-title":"BMC Psychiatry"},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0026","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0027","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.pneurobio.2014.02.004","article-title":"What is normal in normal aging? Effects of aging, amyloid and Alzheimer\u2019s disease on the cerebral cortex and the hippocampus","volume":"117","author":"Fjell","year":"2014","journal-title":"Prog. Neurobiol."},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0028","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1515\/REVNEURO.2010.21.3.187","article-title":"Structural brain changes in aging: courses, causes and cognitive consequences","volume":"21","author":"Fjell","year":"2010","journal-title":"Rev. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0029","first-page":"19","article-title":"Clinical applications of resting state functional connectivity","volume":"4","author":"Fox","year":"2010","journal-title":"Front Syst. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0030","doi-asserted-by":"crossref","DOI":"10.1024\/1662-9647\/a000074","article-title":"Longitudinal changes in individual brainAGE in healthy aging, mild cognitive impairment, and Alzheimers disease","author":"Franke","year":"2012","journal-title":"GeroPsych (Bern)"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0031","doi-asserted-by":"crossref","first-page":"789","DOI":"10.3389\/fneur.2019.00789","article-title":"Ten years of brainAGE as a neuroimaging biomarker of brain aging: what insights have we gained?","volume":"10","author":"Franke","year":"2019","journal-title":"Front Neurol."},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0032","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.neuroimage.2010.01.005","article-title":"Estimating the age of healthy subjects from t1-weighted MRI scans using kernel methods: exploring the influence of various parameters","volume":"50","author":"Franke","year":"2010","journal-title":"Neuroimage"},{"issue":"7615","key":"10.1016\/j.neuroimage.2020.117292_bib0033","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature18933","article-title":"A multi-modal parcellation of human cerebral cortex","volume":"536","author":"Glasser","year":"2016","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.neuroimage.2020.117292_bib0034","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1038\/nrn3256","article-title":"The cognitive neuroscience of ageing","volume":"13","author":"Grady","year":"2012","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0035","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.neuroimage.2018.05.077","article-title":"A supervised learning approach for diffusion MRI quality control with minimal training data","volume":"178","author":"Graham","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0036","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neuroimage.2016.07.018","article-title":"Bianca (brain intensity abnormality classification algorithm): a new tool for automated segmentation of white matter hyperintensities","volume":"141","author":"Griffanti","year":"2016","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0037","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neuroimage.2012.06.038","article-title":"Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure","volume":"63","author":"Groves","year":"2012","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0038","doi-asserted-by":"crossref","first-page":"e1794","DOI":"10.1371\/journal.pone.0001794","article-title":"Modulation of brain resting-state networks by sad mood induction","volume":"3","author":"Harrison","year":"2008","journal-title":"PLoS ONE"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0039","doi-asserted-by":"crossref","first-page":"116276","DOI":"10.1016\/j.neuroimage.2019.116276","article-title":"Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics","volume":"206","author":"He","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0040","doi-asserted-by":"crossref","first-page":"450","DOI":"10.3389\/fneur.2019.00450","article-title":"Cross-sectional and longitudinal MRI brain scans reveal accelerated brain aging in multiple sclerosis","volume":"10","author":"H\u00f8gest\u00f8l","year":"2019","journal-title":"Front Neurol."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0041","doi-asserted-by":"crossref","DOI":"10.1038\/s41593-019-0471-7","article-title":"Common brain disorders are associated with heritable patterns of apparent aging of the brain","author":"Kaufmann","year":"2019","journal-title":"Nat. Neurosci"},{"issue":"5","key":"10.1016\/j.neuroimage.2020.117292_bib0042","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/schbul\/sbt142","article-title":"Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders","volume":"40","author":"Koutsouleris","year":"2013","journal-title":"Schizophr Bull."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0046","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2018.00317","article-title":"A nonlinear simulation framework supports adjusting for age when analyzing brainAGE","volume":"10","author":"Le","year":"2018","journal-title":"Front Aging Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0047","series-title":"Proceedings of the \u00a0IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"101","article-title":"Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks","author":"Li","year":"2018"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0048","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neuroimage.2016.11.005","article-title":"Predicting brain-age from multimodal imaging data captures cognitive impairment","volume":"148","author":"Liem","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0049","doi-asserted-by":"crossref","first-page":"405","DOI":"10.3389\/fnhum.2017.00405","article-title":"Advances in studying brain morphology: the benefits of open-access data","volume":"11","author":"Madan","year":"2017","journal-title":"Front Hum. Neurosci."},{"issue":"11","key":"10.1016\/j.neuroimage.2020.117292_bib0050","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1038\/nn.4393","article-title":"Multimodal population brain imaging in the UK biobank prospective epidemiological study","volume":"19","author":"Miller","year":"2016","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0051","series-title":"MRI Atlas of Human White Matter","author":"Mori","year":"2005"},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0052","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1016\/j.neuroimage.2012.08.004","article-title":"Network-specific effects of age and in-scanner subject motion: a resting-state fMRI study of 238 healthy adults","volume":"63","author":"Mowinckel","year":"2012","journal-title":"Neuroimage"},{"issue":"6","key":"10.1016\/j.neuroimage.2020.117292_bib0053","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1093\/cercor\/bhy117","article-title":"Evaluating the prediction of brain maturity from functional connectivity after motion artifact denoising","volume":"29","author":"Nielsen","year":"2019","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0054","article-title":"Improved prediction of brain age using multimodal neuroimaging data","author":"Niu","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0055","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.eplepsyres.2017.03.007","article-title":"Structural brain changes in medically refractory focal epilepsy resemble premature brain aging","volume":"133","author":"Pardoe","year":"2017","journal-title":"Epilepsy Res."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0056","doi-asserted-by":"crossref","first-page":"e5908","DOI":"10.7717\/peerj.5908","article-title":"Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry","volume":"6","author":"Richard","year":"2018","journal-title":"PeerJ"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0057","first-page":"102159","article-title":"Brain age prediction in stroke patients: highly reliable but limited sensitivity to cognitive performance and response to cognitive training","author":"Richard","year":"2019","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0058","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neuroimage.2017.12.059","article-title":"Quantitative assessment of structural image quality","volume":"169","author":"Rosen","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0059","doi-asserted-by":"crossref","first-page":"663","DOI":"10.3389\/fpsyg.2015.00663","article-title":"Reorganization of brain networks in aging: a review of functional connectivity studies","volume":"6","author":"Sala-Llonch","year":"2015","journal-title":"Front Psychol."},{"issue":"6","key":"10.1016\/j.neuroimage.2020.117292_bib0060","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1176\/appi.ajp.2015.15070922","article-title":"Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study","volume":"173","author":"Schnack","year":"2016","journal-title":"Am. J. Psychiatry"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0061","doi-asserted-by":"crossref","first-page":"e52677","DOI":"10.7554\/eLife.52677","article-title":"Brain aging comprises multiple modes of structural and functional change with distinct genetic and biophysical associations","volume":"9","author":"Smith","year":"2020","journal-title":"Elife"},{"issue":"11","key":"10.1016\/j.neuroimage.2020.117292_bib0062","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nn.4125","article-title":"A positive-negative mode of population covariation links brain connectivity, demographics and behavior","volume":"18","author":"Smith","year":"2015","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2020.117292_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.06.017","article-title":"Estimation of brain age delta from brain imaging","author":"Smith","year":"2019","journal-title":"Neuroimage"},{"issue":"12","key":"10.1016\/j.neuroimage.2020.117292_bib0064","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.tics.2013.09.016","article-title":"Functional connectomics from resting-state fMRI","volume":"17","author":"Smith","year":"2013","journal-title":"Trends Cogn. Sci. (Regul. Ed.)"},{"issue":"9","key":"10.1016\/j.neuroimage.2020.117292_bib0065","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1089\/brain.2014.0286","article-title":"Age-related reorganizational changes in modularity and functional connectivity of human brain networks","volume":"4","author":"Song","year":"2014","journal-title":"Brain Connect"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0066","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neuroimage.2017.07.049","article-title":"Distinct resting-state functional connections associated with episodic and visuospatial memory in older adults","volume":"159","author":"Suri","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0067","doi-asserted-by":"crossref","first-page":"j2353","DOI":"10.1136\/bmj.j2353","article-title":"Moderate alcohol consumption as risk factor for adverse brain outcomes and cognitive decline: longitudinal cohort study","volume":"357","author":"Topiwala","year":"2017","journal-title":"BMJ"},{"key":"10.1016\/j.neuroimage.2020.117292_bib0068","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3389\/fnagi.2014.00264","article-title":"The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer\u2019s disease","volume":"6","author":"Voevodskaya","year":"2014","journal-title":"Front Aging Neurosci."},{"issue":"9859","key":"10.1016\/j.neuroimage.2020.117292_bib0069","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1016\/S0140-6736(12)61729-2","article-title":"Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990\u20132010: a systematic analysis for the global burden of disease study 2010","volume":"380","author":"Vos","year":"2012","journal-title":"The Lancet"},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0070","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1002\/hbm.20069","article-title":"Effect of prior cognitive state on resting state networks measured with functional connectivity","volume":"24","author":"Waites","year":"2005","journal-title":"Hum. Brain Mapp."},{"issue":"3","key":"10.1016\/j.neuroimage.2020.117292_bib0071","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.neuroimage.2007.02.049","article-title":"Reproducibility of quantitative tractography methods applied to cerebral white matter","volume":"36","author":"Wakana","year":"2007","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.neuroimage.2020.117292_bib0072","first-page":"391","article-title":"Correcting two-sample \u201cz\u201d and \u201ct\u201d tests for correlation: an alternative to one-sample tests on difference scores","volume":"33","author":"Zimmerman","year":"2012","journal-title":"Psicologica: Int. J. Methodol. Exp. Psychol."},{"issue":"1","key":"10.1016\/j.neuroimage.2020.117292_bib0073","doi-asserted-by":"crossref","first-page":"6411","DOI":"10.1038\/s41598-018-24398-9","article-title":"Allostatic load as a predictor of grey matter volume and white matter integrity in old age: the whitehall II MRI study","volume":"8","author":"Zsoldos","year":"2018","journal-title":"Sci. Rep."}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811920307783?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811920307783?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T02:05:41Z","timestamp":1715306741000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811920307783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":74,"alternative-id":["S1053811920307783"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117292","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.01.28.923094","asserted-by":"object"}]},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2020,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117292","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"117292"}}