{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T01:07:29Z","timestamp":1773450449994,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01DA040487"],"award-info":[{"award-number":["R01DA040487"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["R01DA049238"],"award-info":[{"award-number":["R01DA049238"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"crossref","award":["R01MH121246"],"award-info":[{"award-number":["R01MH121246"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s12021-022-09570-x","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T05:02:36Z","timestamp":1649134956000},"page":"981-990","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Decentralized Brain Age Estimation Using MRI Data"],"prefix":"10.1007","volume":"20","author":[{"given":"Sunitha","family":"Basodi","sequence":"first","affiliation":[]},{"given":"Rajikha","family":"Raja","sequence":"additional","affiliation":[]},{"given":"Bhaskar","family":"Ray","sequence":"additional","affiliation":[]},{"given":"Harshvardhan","family":"Gazula","sequence":"additional","affiliation":[]},{"given":"Anand D.","family":"Sarwate","sequence":"additional","affiliation":[]},{"given":"Sergey","family":"Plis","sequence":"additional","affiliation":[]},{"given":"Jingyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Verner","sequence":"additional","affiliation":[]},{"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"9570_CR1","doi-asserted-by":"publisher","first-page":"140699","DOI":"10.1109\/ACCESS.2020.3013541","volume":"8","author":"M Aledhari","year":"2020","unstructured":"Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access, 8, 140699\u2013140725.","journal-title":"IEEE Access"},{"key":"9570_CR2","unstructured":"Ashburner, J., Barnes, G., Chen, C.-C., Daunizeau, J., Flandin, G., Friston, K., Kiebel, S., Kilner, J., Litvak, V., Moran, R., et\u00a0al. (2014). Spm12 manual. Wellcome Trust Centre for Neuroimaging, London, UK 2464."},{"key":"9570_CR3","doi-asserted-by":"crossref","unstructured":"Bostami, B., Vergara, V., & Calhoun, V.\u00a0D. (2021a). Harmonization of multi-site dynamic functional connectivity network data. IEEE BIBE.","DOI":"10.1109\/BIBE52308.2021.9635538"},{"key":"9570_CR4","unstructured":"Bostami, B., Vergara, V., Calhoun, V. D., & Hillary, F. (2021b).\u00a0Networking brain networks: Federated harmonization of neuroimaging data. Complex Networks, Madrid, Spain."},{"key":"9570_CR5","first-page":"3","volume":"12","author":"K Chaudhuri","year":"2011","unstructured":"Chaudhuri, K., Monteleoni, C., & Sarwate, A. D. (2011). Differentially private empirical risk minimization. Journal of Machine Learning Research, 12, 3.","journal-title":"Journal of Machine Learning Research"},{"key":"9570_CR6","unstructured":"COINSTAC. http:\/\/coinstac.trendscenter.org."},{"issue":"2","key":"9570_CR7","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1038\/s41380-018-0098-1","volume":"24","author":"JH Cole","year":"2019","unstructured":"Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2019). Brain age and other bodily ages: implications for neuropsychiatry. Molecular psychiatry, 24(2), 266\u2013281.","journal-title":"Molecular psychiatry"},{"key":"9570_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","volume":"163","author":"JH Cole","year":"2017","unstructured":"Cole, J. H., Poudel, R. P., Tsagkrasoulis, D., Caan, M. W., Steves, C., Spector, T. D., & Montana, G. (2017). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115\u2013124.","journal-title":"NeuroImage"},{"issue":"5","key":"9570_CR9","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1038\/mp.2017.62","volume":"23","author":"JH Cole","year":"2018","unstructured":"Cole, J. H., Ritchie, S. J., Bastin, M. E., Hern\u00e1ndez, M. V., Maniega, S. M., Royle, N., et al. (2018). Brain age predicts mortality. Molecular psychiatry, 23(5), 1385\u20131392.","journal-title":"Molecular psychiatry"},{"key":"9570_CR10","doi-asserted-by":"crossref","unstructured":"Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Rahaman, M.\u00a0A., Abrol, A., Chen, J., et\u00a0al. (2019). Neuromark: a fully automated ica method to identify effective fmri markers of brain disorders. medRxiv, 19008631.","DOI":"10.1101\/19008631"},{"key":"9570_CR11","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.neuroimage.2015.07.054","volume":"122","author":"Y Du","year":"2015","unstructured":"Du, Y., Pearlson, G. D., Liu, J., Sui, J., Yu, Q., He, H., et al. (2015). A group ica based framework for evaluating resting fmri markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. Neuroimage, 122, 272\u2013280.","journal-title":"Neuroimage"},{"key":"9570_CR12","doi-asserted-by":"crossref","unstructured":"Elliott, M. L., Belsky, D. W., Knodt, A. R., Ireland, D., Melzer, T. R., Poulton, R., Ramrakha, S., Caspi, A., Moffitt, T. E., & Hariri, A. R. (2019). Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Molecular psychiatry, 1\u201310.","DOI":"10.1101\/712851"},{"issue":"2","key":"9570_CR13","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B. (2012). Freesurfer. Neuroimage, 62(2), 774\u2013781.","journal-title":"Neuroimage"},{"key":"9570_CR14","doi-asserted-by":"publisher","first-page":"789","DOI":"10.3389\/fneur.2019.00789","volume":"10","author":"K Franke","year":"2019","unstructured":"Franke, K., & Gaser, C. Ten. (2019). years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained? Frontiers in neurology, 10, 789.","journal-title":"Frontiers in neurology"},{"key":"9570_CR15","doi-asserted-by":"crossref","unstructured":"Gazula, H., Holla, B., Zhang, Z., Xu, J., Verner, E., Kelly, R., Schumann, G., & Calhoun, V.\u00a0D. (2019). Decentralized multi-site vbm analysis during adolescence shows structural changes linked to age, body mass index, and smoking: A coinstac analysis. bioRxiv, 846386.","DOI":"10.1101\/846386"},{"issue":"4","key":"9570_CR16","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1016\/j.neuroimage.2007.11.001","volume":"39","author":"MJ Jafri","year":"2008","unstructured":"Jafri, M. J., Pearlson, G. D., Stevens, M., & Calhoun, V. D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39(4), 1666\u20131681.","journal-title":"Neuroimage"},{"issue":"1","key":"9570_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-13163-9","volume":"10","author":"BA J\u00f3nsson","year":"2019","unstructured":"J\u00f3nsson, B. A., Bjornsdottir, G., Thorgeirsson, T., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D., et al. (2019). Brain age prediction using deep learning uncovers associated sequence variants. Nature communications, 10(1), 1\u201310.","journal-title":"Nature communications"},{"issue":"3","key":"9570_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50\u201360.","journal-title":"IEEE Signal Processing Magazine"},{"key":"9570_CR19","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neuroimage.2016.11.005","volume":"148","author":"F Liem","year":"2017","unstructured":"Liem, F., Varoquaux, G., Kynast, J., Beyer, F., Masouleh, S. K., Huntenburg, J. M., et al. (2017). Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage, 148, 179\u2013188.","journal-title":"Neuroimage"},{"key":"9570_CR20","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.neuroimage.2016.04.007","volume":"134","author":"E Luders","year":"2016","unstructured":"Luders, E., Cherbuin, N., & Gaser, C. (2016). Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners. Neuroimage, 134, 508\u2013513.","journal-title":"Neuroimage"},{"issue":"11","key":"9570_CR21","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., et al. (2016). Multimodal population brain imaging in the uk biobank prospective epidemiological study. Nature neuroscience, 19(11), 1523\u20131536.","journal-title":"Nature neuroscience"},{"key":"9570_CR22","doi-asserted-by":"crossref","unstructured":"Ming, J., Verner, E., Sarwate, A., Kelly, R., Reed, C., Kahleck, T., Silva, R., Panta, S., Turner, J., Plis, S., et\u00a0al. (2017). Coinstac: Decentralizing the future of brain imaging analysis. F1000Research 6.","DOI":"10.12688\/f1000research.12353.1"},{"issue":"6","key":"9570_CR23","doi-asserted-by":"publisher","first-page":"1626","DOI":"10.1002\/hbm.24899","volume":"41","author":"X Niu","year":"2020","unstructured":"Niu, X., Zhang, F., Kounios, J., & Liang, H. (2020). Improved prediction of brain age using multimodal neuroimaging data. Human brain mapping, 41(6), 1626\u20131643.","journal-title":"Human brain mapping"},{"key":"9570_CR24","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3389\/fnins.2016.00365","volume":"10","author":"SM Plis","year":"2016","unstructured":"Plis, S. M., Sarwate, A. D., Wood, D., Dieringer, C., Landis, D., Reed, C., et al. (2016). Coinstac: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data. Frontiers in neuroscience, 10, 365.","journal-title":"Frontiers in neuroscience"},{"key":"9570_CR25","doi-asserted-by":"crossref","unstructured":"Ray, B., Duan, K., Chen, J., Fu, Z., Suresh, P., Johnson, S., Calhoun, V.\u00a0D., & Liu, J. (2021). Multimodal brain age prediction with feature selection and comparison. EMBC.","DOI":"10.1109\/EMBC46164.2021.9631007"},{"key":"9570_CR26","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.arr.2014.01.004","volume":"14","author":"A Reeve","year":"2014","unstructured":"Reeve, A., Simcox, E., & Turnbull, D. (2014). Ageing and parkinson\u2019s disease: why is advancing age the biggest risk factor? Ageing research reviews, 14, 19\u201330.","journal-title":"Ageing research reviews"},{"key":"9570_CR27","doi-asserted-by":"publisher","first-page":"116189","DOI":"10.1016\/j.neuroimage.2019.116189","volume":"206","author":"ET Rolls","year":"2020","unstructured":"Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J., & Joliot, M. (2020). Automated anatomical labelling atlas 3. Neuroimage, 206, 116189.","journal-title":"Neuroimage"},{"issue":"8","key":"9570_CR28","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s10916-019-1401-7","volume":"43","author":"H Sajedi","year":"2019","unstructured":"Sajedi, H., & Pardakhti, N. (2019). Age prediction based on brain mri image: a survey. Journal of medical systems, 43(8), 279.","journal-title":"Journal of medical systems"},{"key":"9570_CR29","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3389\/fninf.2014.00035","volume":"8","author":"AD Sarwate","year":"2014","unstructured":"Sarwate, A. D., Plis, S. M., Turner, J. A., Arbabshirani, M. R., & Calhoun, V. D. (2014). Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation. Frontiers in neuroinformatics, 8, 35.","journal-title":"Frontiers in neuroinformatics"},{"key":"9570_CR30","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neuroimage.2013.07.064","volume":"86","author":"TD Satterthwaite","year":"2014","unstructured":"Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., et al. (2014). Neuroimaging of the philadelphia neurodevelopmental cohort. Neuroimage, 86, 544\u2013553.","journal-title":"Neuroimage"},{"key":"9570_CR31","unstructured":"Smith, S., Woolrich, M., Behrens, T., Beckmann, C., Flitney, D., Jenkinson, M., Bannister, P., Clare, S., De\u00a0Luca, M., Hansen, P., et\u00a0al. Fmrib software library."},{"key":"9570_CR32","doi-asserted-by":"crossref","unstructured":"Stankevi\u010di\u016bt\u0117, K., Azevedo, T., Campbell, A., Bethlehem, R.\u00a0A., & Li\u00f2, P. (2020). Population graph gnns for brain age prediction. bioRxiv.","DOI":"10.1101\/2020.06.26.172171"},{"key":"9570_CR33","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.neurobiolaging.2016.01.014","volume":"40","author":"J Steffener","year":"2016","unstructured":"Steffener, J., Habeck, C., O\u2019Shea, D., Razlighi, Q., Bherer, L., & Stern, Y. (2016). Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiology of aging, 40, 138\u2013144.","journal-title":"Neurobiology of aging"},{"issue":"3","key":"9570_CR34","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., et al. (2015). Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Plos med, 12(3), e1001779.","journal-title":"Plos med"},{"key":"9570_CR35","doi-asserted-by":"crossref","unstructured":"White, T., Blok, E., & Calhoun, V. D. (2020). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Human Brain Mapping.","DOI":"10.1002\/hbm.25120"},{"key":"9570_CR36","doi-asserted-by":"crossref","unstructured":"Woolson, R. (2007). Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, 1\u20133.","DOI":"10.1002\/9780471462422.eoct979"},{"issue":"16","key":"9570_CR37","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1001\/jama.2019.3636","volume":"321","author":"L Yang","year":"2019","unstructured":"Yang, L., Cao, C., Kantor, E. D., Nguyen, L. H., Zheng, X., Park, Y., et al. (2019). Trends in sedentary behavior among the us population, 2001\u20132016. Jama, 321(16), 1587\u20131597.","journal-title":"Jama"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-022-09570-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-022-09570-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-022-09570-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T23:47:23Z","timestamp":1666568843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-022-09570-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["9570"],"URL":"https:\/\/doi.org\/10.1007\/s12021-022-09570-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.05.10.443469","asserted-by":"object"}]},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,5]]},"assertion":[{"value":"27 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}