{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:39:37Z","timestamp":1776267577630,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2021R1C1C1009436"],"award-info":[{"award-number":["2021R1C1C1009436"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["HI22C0108"],"award-info":[{"award-number":["HI22C0108"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute (KHIDI)","doi-asserted-by":"publisher","award":["2021R1C1C1009436"],"award-info":[{"award-number":["2021R1C1C1009436"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute (KHIDI)","doi-asserted-by":"publisher","award":["HI22C0108"],"award-info":[{"award-number":["HI22C0108"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain structural morphology varies over the aging trajectory, and the prediction of a person\u2019s age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual\u2019s brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22\u201337), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18\u201388), and the Information eXtraction from Images (IXI, n = 567, age range 19\u201386). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75\u20133.12, 7.08\u201310.50, and 8.04\u20139.86 years, as well as Pearson\u2019s correlation coefficients of 0.11\u20130.42, 0.64\u20130.85, and 0.63\u20130.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.<\/jats:p>","DOI":"10.3390\/s22208077","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8077","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data"],"prefix":"10.3390","volume":"22","author":[{"given":"Juhyuk","family":"Han","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"}]},{"given":"Seo Yeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"}]},{"given":"Junhyeok","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"}]},{"given":"Won Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","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."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111270","DOI":"10.1016\/j.pscychresns.2021.111270","article-title":"Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?","volume":"310","author":"Lee","year":"2021","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neurobiolaging.2021.10.007","article-title":"Brain-Predicted age difference is associated with cognitive processing in later-Life","volume":"109","author":"Wrigglesworth","year":"2022","journal-title":"Neurobiol. Aging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1002\/hbm.25316","article-title":"Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging","volume":"42","author":"Anaturk","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103600","DOI":"10.1016\/j.ebiom.2021.103600","article-title":"Machine learning for brain age prediction: Introduction to methods and clinical applications","volume":"72","author":"Baecker","year":"2021","journal-title":"EBioMedicine"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1002\/hbm.25837","article-title":"Mind the gap: Performance metric evaluation in brain-age prediction","volume":"43","author":"Anaturk","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5346","DOI":"10.1038\/s41467-021-25492-9","article-title":"Accelerated functional brain aging in pre-clinical familial Alzheimer\u2019s disease","volume":"12","author":"Gonneaud","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_8","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"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e52677","DOI":"10.7554\/eLife.52677","article-title":"Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations","volume":"9","author":"Smith","year":"2020","journal-title":"Elife"},{"key":"ref_10","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":"Hogestol","year":"2019","journal-title":"Front. Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neurobiolaging.2020.03.014","article-title":"Multimodality neuroimaging brain-age in UK biobank: Relationship to biomedical, lifestyle, and cognitive factors","volume":"92","author":"Cole","year":"2020","journal-title":"Neurobiol. Aging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1038\/s41593-019-0471-7","article-title":"Common brain disorders are associated with heritable patterns of apparent aging of the brain","volume":"22","author":"Kaufmann","year":"2019","journal-title":"Nat. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Moskalev, A. (2019). Quantification of the Biological Age of the Brain Using Neuroimaging. Biomarkers of Human Aging, Springer International Publishing.","DOI":"10.1007\/978-3-030-24970-0"},{"key":"ref_14","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"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1002\/hbm.23434","article-title":"Age prediction on the basis of brain anatomical measures","volume":"38","author":"Valizadeh","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1002\/hbm.25368","article-title":"Brain age prediction: A comparison between machine learning models using region-and voxel-based morphometric data","volume":"42","author":"Baecker","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1176\/appi.ajp.2017.17010100","article-title":"Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group","volume":"175","author":"Anagnostou","year":"2018","journal-title":"Am. J. Psychiatry"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9676","DOI":"10.1038\/s41598-019-46145-4","article-title":"Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants","volume":"9","author":"Corps","year":"2019","journal-title":"Sci Rep.-UK"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1176\/appi.ajp.2020.19030331","article-title":"Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups","volume":"177","author":"Boedhoe","year":"2020","journal-title":"Am. J. Psychiatry"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5124","DOI":"10.1038\/s41380-020-0754-0","article-title":"Brain aging in major depressive disorder: Results from the ENIGMA major depressive disorder working group","volume":"26","author":"Han","year":"2021","journal-title":"Mol. Psychiatry"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neuron.2017.11.039","article-title":"Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation","volume":"97","author":"Seidlitz","year":"2018","journal-title":"Neuron"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gaser, C., Franke, K., Kloppel, S., Koutsouleris, N., Sauer, H., and Alzheimer\u2019s Disease Neuroimaging, I. (2013). BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer\u2019s Disease. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0067346"},{"key":"ref_23","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":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn human connectome project: An overview","volume":"80","author":"Smith","year":"2013","journal-title":"Neuroimage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12883-014-0204-1","article-title":"The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: A cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing","volume":"14","author":"Shafto","year":"2014","journal-title":"BMC Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","article-title":"The minimal preprocessing pipelines for the Human Connectome Project","volume":"80","author":"Glasser","year":"2013","journal-title":"Neuroimage"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1006\/nimg.1998.0395","article-title":"Cortical surface-based analysis. I. Segmentation and surface reconstruction","volume":"9","author":"Dale","year":"1999","journal-title":"Neuroimage"},{"key":"ref_28","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":"ref_29","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"Neuroimage"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Constantinides, C., Han, L.K., Alloza, C., Antonucci, L., Arango, C., Ayesa-Arriola, R., Banaj, N., Bertolino, A., Borgwardt, S., and Bruggemann, J. (2022). Brain ageing in schizophrenia: Evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. medRxiv.","DOI":"10.1038\/s41380-022-01897-w"},{"key":"ref_31","unstructured":"Ali, M. (2021, September 01). PyCaret: An Open Source, Low-Code Machine Learning Library in Python. Available online: https:\/\/www.pycaret.org."},{"key":"ref_32","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, The MIT Press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. Roy. Stat. Soc. B Met."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Stat."},{"key":"ref_36","unstructured":"Rubinstein, R., Zibulevsky, M., and Elad, M. (2008). Efficient Implementation of the K-SVD Algorithm Using Batch Orthogonal Matching Pursuit, Computer Science Department, Technion."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian Interpolation","volume":"4","author":"Mackay","year":"1992","journal-title":"Neural Comput"},{"key":"ref_38","first-page":"551","article-title":"Online passive aggressive algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1090\/conm\/443\/08555","article-title":"A robust hybrid of lasso and ridge regression","volume":"443","author":"Owen","year":"2007","journal-title":"Contemp. Math."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_42","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"Tipping","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Dang, X., Peng, H., Wang, X., and Zhang, H. (2021, September 01). Theil-Sen Estimators in a Multiple Linear Regression Model. Olemiss Edu, Available online: http:\/\/home.olemiss.edu\/~xdang\/papers\/MTSE.pdf."},{"key":"ref_44","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1997","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rasmussen, C., and Williams, C. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_47","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."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_49","unstructured":"Drucker, H. (1997, January 8\u201312). Improving regressors using boosting techniques. Proceedings of the ICML, Nashville, TN, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_53","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30, MIT Press."},{"key":"ref_54","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems 31, MIT Press."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liang, H., Zhang, F., and Niu, X. (2019). Investigating Systematic Bias in Brain Age Estimation with Application to Post-Traumatic Stress Disorders, Wiley Online Library.","DOI":"10.1002\/hbm.24588"},{"key":"ref_56","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30, MIT Press."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"118036","DOI":"10.1016\/j.neuroimage.2021.118036","article-title":"Individual variation underlying brain age estimates in typical development","volume":"235","author":"Ball","year":"2021","journal-title":"Neuroimage"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1093\/brain\/awaa160","article-title":"MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide","volume":"143","author":"Bashyam","year":"2020","journal-title":"Brain"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","article-title":"Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI","volume":"28","author":"Schaefer","year":"2018","journal-title":"Cereb. Cortex"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"101871","DOI":"10.1016\/j.media.2020.101871","article-title":"Accurate brain age prediction with lightweight deep neural networks","volume":"68","author":"Peng","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"102091","DOI":"10.1016\/j.media.2021.102091","article-title":"Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan","volume":"72","author":"He","year":"2021","journal-title":"Med. Image Anal."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8077\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:31Z","timestamp":1760144431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8077"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":61,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208077"],"URL":"https:\/\/doi.org\/10.3390\/s22208077","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}