{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:40:24Z","timestamp":1743046824780,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-41774-0_52","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"661-674","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Brain Age Estimation via\u00a0Regression Models and\u00a0MRI-Derived Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3614-4124","authenticated-orcid":false,"given":"Mansoor","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9274-3797","authenticated-orcid":false,"given":"Usama","family":"Sardar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-2168","authenticated-orcid":false,"given":"Sarwan","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9566-8040","authenticated-orcid":false,"given":"Shafiq","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4329-0234","authenticated-orcid":false,"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-6168","authenticated-orcid":false,"given":"Imdad Ullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"52_CR1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.3389\/fnagi.2018.00252","volume":"10","author":"HM Aycheh","year":"2018","unstructured":"Aycheh, H.M., Seong, J.K., Shin, J.H., et al.: Biological brain age prediction using cortical thickness data: a large scale cohort study. Front. Aging Neurosci. 10, 252 (2018)","journal-title":"Front. Aging Neurosci."},{"issue":"8","key":"52_CR2","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1002\/hbm.25368","volume":"42","author":"L Baecker","year":"2021","unstructured":"Baecker, L., Dafflon, J., Da Costa, P.F., et al.: Brain age prediction: a comparison between machine learning models using region and voxel based morphometric data. Hum. Brain Mapp. 42(8), 2332\u20132346 (2021)","journal-title":"Hum. Brain Mapp."},{"key":"52_CR3","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/s12021-022-09570-x","volume":"20","author":"S Basodi","year":"2022","unstructured":"Basodi, S., Raja, R., Ray, B., et al.: Decentralized brain age estimation using MRI data. Neuroinformatics 20, 981\u2013990 (2022)","journal-title":"Neuroinformatics"},{"key":"52_CR4","doi-asserted-by":"publisher","first-page":"106585","DOI":"10.1016\/j.cmpb.2021.106585","volume":"214","author":"I Beheshti","year":"2022","unstructured":"Beheshti, I., Maikusa, N., Matsuda, H.: The accuracy of T1-weighted voxel-wise and region-wise metrics for brain age estimation. Comput. Meth. Program. Biomed. 214, 106585 (2022)","journal-title":"Comput. Meth. Program. Biomed."},{"issue":"3","key":"52_CR5","doi-asserted-by":"publisher","first-page":"618","DOI":"10.14336\/AD.2019.0617","volume":"11","author":"I Beheshti","year":"2020","unstructured":"Beheshti, I., Mishra, S., Sone, D., et al.: T1-weighted MRI-driven brain age estimation in Alzheimer\u2019s disease and Parkinson\u2019s disease. Aging Dis. 11(3), 618 (2020)","journal-title":"Aging Dis."},{"key":"52_CR6","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., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163, 115\u2013124 (2017)","journal-title":"Neuroimage"},{"issue":"5","key":"52_CR7","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1038\/mp.2017.62","volume":"23","author":"JH Cole","year":"2017","unstructured":"Cole, J.H., Ritchie, S.J., Bastin, M.E., et al.: Brain age predicts mortality. Mol. Psychiatry 23(5), 1385\u20131392 (2017)","journal-title":"Mol. Psychiatry"},{"issue":"3","key":"52_CR8","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., S\u00e9gonne, F., Fischl, B., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral-based regions of interest. Neuroimage 31(3), 968\u2013980 (2006)","journal-title":"Neuroimage"},{"key":"52_CR9","doi-asserted-by":"publisher","first-page":"119637","DOI":"10.1016\/j.neuroimage.2022.119637","volume":"263","author":"B Dufumier","year":"2022","unstructured":"Dufumier, B., Grigis, A., Victor, J., et al.: OpenBHB: a large-scale multi-site brain MRI data-set for age prediction and debiasing. Neuroimage 263, 119637 (2022)","journal-title":"Neuroimage"},{"issue":"10","key":"52_CR10","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s12264-020-00520-8","volume":"36","author":"W Ediri Arachchi","year":"2020","unstructured":"Ediri Arachchi, W., Peng, Y., Zhang, X., et al.: A systematic characterization of structural brain changes in schizophrenia. Neurosci. Bull. 36(10), 1107\u20131122 (2020)","journal-title":"Neurosci. Bull."},{"issue":"6","key":"52_CR11","doi-asserted-by":"publisher","first-page":"899","DOI":"10.14336\/AD.2017.0502","volume":"8","author":"F Farokhian","year":"2017","unstructured":"Farokhian, F., Yang, C., Beheshti, I., et al.: Age-related gray and white matter changes in normal adult brains. Aging Dis. 8(6), 899\u2013909 (2017)","journal-title":"Aging Dis."},{"issue":"1","key":"52_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1093\/cercor\/bhg087","volume":"14","author":"B Fischl","year":"2004","unstructured":"Fischl, B., Van Der Kouwe, A., Destrieux, C., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14(1), 11\u201322 (2004)","journal-title":"Cereb. Cortex"},{"key":"52_CR13","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 years of BrainAGE as a neuroimaging biomarker of brain aging: what insights have we gained? Front. Neurol. 10, 789 (2019)","journal-title":"Front. Neurol."},{"issue":"3","key":"52_CR14","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.neuroimage.2010.01.005","volume":"50","author":"K Franke","year":"2010","unstructured":"Franke, K., Ziegler, G., Kl\u00f6ppel, S., et al.: Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50(3), 883\u2013892 (2010)","journal-title":"Neuroimage"},{"key":"52_CR15","doi-asserted-by":"crossref","unstructured":"Fujimoto, R., Ito, K., Wu, K., et al.: Brain age estimation from T1-weighted images using effective local features. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 3028\u20133031 (2017)","DOI":"10.1109\/EMBC.2017.8037495"},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Gaser, C., Dahnke, R.: CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. bioRxiv (2022)","DOI":"10.1101\/2022.06.11.495736"},{"issue":"6","key":"52_CR17","doi-asserted-by":"publisher","first-page":"e67346","DOI":"10.1371\/journal.pone.0067346","volume":"8","author":"C Gaser","year":"2013","unstructured":"Gaser, C., Franke, K., Kl\u00f6ppel, S., et al.: BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer\u2019s Disease. PLoS ONE 8(6), e67346 (2013)","journal-title":"PLoS ONE"},{"issue":"6","key":"52_CR18","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1111\/acel.12271","volume":"13","author":"A Hafkemeijer","year":"2014","unstructured":"Hafkemeijer, A., Altmann-Schneider, I., de Craen, A.J., et al.: Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults. Aging Cell 13(6), 1068\u20131074 (2014)","journal-title":"Aging Cell"},{"key":"52_CR19","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.3389\/fneur.2019.01346","volume":"10","author":"H Jiang","year":"2020","unstructured":"Jiang, H., Lu, N., Chen, K., et al.: Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Front. Neurol. 10, 1346 (2020)","journal-title":"Front. Neurol."},{"issue":"1","key":"52_CR20","doi-asserted-by":"publisher","first-page":"5409","DOI":"10.1038\/s41467-019-13163-9","volume":"10","author":"BA J\u00f3nsson","year":"2019","unstructured":"J\u00f3nsson, B.A., Bjornsdottir, G., Thorgeirsson, T., et al.: Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10(1), 5409 (2019)","journal-title":"Nat. Commun."},{"key":"52_CR21","doi-asserted-by":"crossref","unstructured":"Lee, P.L., Kuo, C.Y., Wang, P.N., et al.: Regional rather than global brain age mediates cognitive function in cerebral small vessel disease. Brain Commun. 4(5) (2022)","DOI":"10.1093\/braincomms\/fcac233"},{"key":"52_CR22","doi-asserted-by":"publisher","first-page":"111270","DOI":"10.1016\/j.pscychresns.2021.111270","volume":"310","author":"WH Lee","year":"2021","unstructured":"Lee, W.H., Antoniades, M., Schnack, H.G., et al.: Brain age prediction in schizophrenia: does the choice of machine learning algorithm matter? Psychiatry Res. Neuroimaging 310, 111270 (2021)","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"52_CR23","doi-asserted-by":"publisher","first-page":"105285","DOI":"10.1016\/j.compbiomed.2022.105285","volume":"143","author":"X Liu","year":"2022","unstructured":"Liu, X., Beheshti, I., Zheng, W., et al.: Brain age estimation using multi-feature-based networks. Comput. Biol. Med. 143, 105285 (2022)","journal-title":"Comput. Biol. Med."},{"key":"52_CR24","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.: Estimating brain age using high-resolution pattern recognition: younger brains in long term meditation practitioners. Neuroimage 134, 508\u2013513 (2016)","journal-title":"Neuroimage"},{"issue":"11","key":"52_CR25","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132604 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"52_CR26","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1002\/jmri.21372","volume":"27","author":"A Mikheev","year":"2008","unstructured":"Mikheev, A., Nevsky, G., Govindan, S., et al.: Fully automatic segmentation of the brain from T1-weighted MRI using bridge burner algorithm. J. Magn. Reson. Imaging\u202f: JMRI 27(6), 1235\u20131241 (2008)","journal-title":"J. Magn. Reson. Imaging : JMRI"},{"key":"52_CR27","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/RBME.2021.3107372","volume":"16","author":"S Mishra","year":"2021","unstructured":"Mishra, S., Beheshti, I., Khanna, P.: A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Rev. Biomed. Eng. 16, 371\u2013385 (2021)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"17","key":"52_CR28","doi-asserted-by":"publisher","first-page":"5126","DOI":"10.1002\/hbm.26010","volume":"43","author":"A Modabbernia","year":"2022","unstructured":"Modabbernia, A., Whalley, H.C., Glahn, D.C., et al.: Systematic evaluation of ML algorithms for neuroanatomically-based age prediction in youth. Hum. Brain Mapp. 43(17), 5126\u20135140 (2022)","journal-title":"Hum. Brain Mapp."},{"key":"52_CR29","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.pscychresns.2017.05.006","volume":"266","author":"I Nenadi\u0107","year":"2017","unstructured":"Nenadi\u0107, I., Dietzek, M., Langbein, K., et al.: BrainAGE Score Indicates Accelerated Brain Aging in Schizophrenia, but Not Bipolar Disorder. Psychiatry Research: Neuroimaging 266, 86\u201389 (2017)","journal-title":"Psychiatry Research: Neuroimaging"},{"issue":"8","key":"52_CR30","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.: Age Prediction Based on Brain MRI Image: A Survey. J. Med. Syst. 43(8), 279 (2019)","journal-title":"J. Med. Syst."},{"issue":"15","key":"52_CR31","doi-asserted-by":"publisher","first-page":"4689","DOI":"10.1002\/hbm.25983","volume":"43","author":"N Sanford","year":"2022","unstructured":"Sanford, N., Ge, R., Antoniades, M., et al.: Sex differences in predictors and regional patterns of brain age gap estimates. Hum. Brain Mapp. 43(15), 4689\u20134698 (2022)","journal-title":"Hum. Brain Mapp."},{"issue":"7","key":"52_CR32","doi-asserted-by":"publisher","first-page":"e22734","DOI":"10.1371\/journal.pone.0022734","volume":"6","author":"Y Taki","year":"2011","unstructured":"Taki, Y., Thyreau, B., Kinomura, S., et al.: Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals. PLoS ONE 6(7), e22734 (2011)","journal-title":"PLoS ONE"},{"key":"52_CR33","doi-asserted-by":"publisher","first-page":"119621","DOI":"10.1016\/j.neuroimage.2022.119621","volume":"263","author":"A Taylor","year":"2022","unstructured":"Taylor, A., Zhang, F., Niu, X., et al.: Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer\u2019s disease related neurodegeneration. Neuroimage 263, 119621 (2022)","journal-title":"Neuroimage"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:36:14Z","timestamp":1695278174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_52","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"218","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.01","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.86","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}