{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T23:28:18Z","timestamp":1778628498222,"version":"3.51.4"},"reference-count":68,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2015,12,9]],"date-time":"2015-12-09T00:00:00Z","timestamp":1449619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Current brain-age prediction methods using magnetic resonance imaging (MRI) attempt to estimate the physiological brain age via some kind of machine learning of chronological brain age data to perform the classification task. Such a predictive approach imposes greater risk of either over-estimate or under-estimate, mainly due to limited training data. A new conceptual framework for more reliable MRI-based brain-age prediction is by systematic brain-age grouping via the implementation of the phylogenetic tree reconstruction and measures of information complexity. Experimental results carried out on a public MRI database suggest the feasibility of the proposed concept.<\/jats:p>","DOI":"10.3390\/e17127868","type":"journal-article","created":{"date-parts":[[2015,12,9]],"date-time":"2015-12-09T15:21:41Z","timestamp":1449674501000},"page":"8130-8151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4255-5130","authenticated-orcid":false,"given":"Tuan","family":"Pham","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Link\u00f6ping 581 83, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taishi","family":"Abe","sequence":"additional","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryuichi","family":"Oka","sequence":"additional","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8966-1021","authenticated-orcid":false,"given":"Yung-Fu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan"},{"name":"Department of Health Services Administration, China Medical University, Taichung 40402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Teverovskiy, L.A., Becker, J.T., Lopez, O.L., and Liu, Y. (2008, January 14\u201317). Quantified brain asymmetry for age estimation of normal and AD\/MCI subjects. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), Paris, France.","DOI":"10.1109\/ISBI.2008.4541295"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.jneumeth.2011.04.022","article-title":"MRI-based age prediction using hidden Markov models","volume":"199","author":"Wang","year":"2011","journal-title":"J. Neurosci. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e22193","DOI":"10.1371\/journal.pone.0022193","article-title":"Age correction in dementia\u2013matching to a healthy brain","volume":"6","author":"Dukart","year":"2011","journal-title":"PLoS ONE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/978-3-642-38868-2_8","article-title":"Predicting cognitive data from medical images using sparse linear regression","volume":"Volume 7917","author":"Gee","year":"2013","journal-title":"Information Processing in Medical Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1007\/s11682-014-9321-0","article-title":"Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood","volume":"9","author":"Irimia","year":"2015","journal-title":"Brain Imaging Behav."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1002\/ana.24367","article-title":"Prediction of brain age suggests accelerated atrophy after traumatic brain injury","volume":"77","author":"Cole","year":"2015","journal-title":"Ann. Neurol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1016\/j.neurobiolaging.2008.08.018","article-title":"Whole brain atrophy rate predicts progression from MCI to Alzheimer\u2019s disease","volume":"31","author":"Spulber","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"016004","DOI":"10.1088\/1741-2560\/8\/1\/016004","article-title":"The hidden-Markov brain: Comparison and inference of white matter hyperintensities on magnetic resonance imaging (MRI)","volume":"8","author":"Pham","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/978-3-642-36669-7_34","article-title":"Predicting the age of healthy adults from structural MRI by sparse representation","volume":"Volume 7751","author":"Yang","year":"2013","journal-title":"Intelligent Science and Intelligent Data Engineering"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e67346","DOI":"10.1371\/journal.pone.0067346","article-title":"BrainAGE in mild cognitive impaired patients: Predicting the conversion to Alzheimer\u2019s disease","volume":"8","author":"Gaser","year":"2013","journal-title":"PLoS ONE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bigler, E.D. (2013). Traumatic brain injury, neuroimaging, and neurodegeneration. Front. Hum. Neurosci., 7.","DOI":"10.3389\/fnhum.2013.00395"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nn1008","article-title":"Mapping cortical change across the human life span","volume":"6","author":"Sowell","year":"2003","journal-title":"Nat. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.neubiorev.2006.07.001","article-title":"Differential aging of the brain: Patterns, cognitive correlates and modifiers","volume":"30","author":"Raz","year":"2006","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_15","unstructured":"Wang, B., and Pham, T.D. (2011, January 4\u20136). HMM-based brain age interpolation using kriging estimator. Proceedings of the IEEE International Symposium on Image and Signal Processing and Analysis, Dubrovnik, Croatia."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jneumeth.2013.03.018","article-title":"Entropy and regularity dimension in complexity analysis of cortical surface structure in early Alzheimer\u2019s disease and aging","volume":"215","author":"Chen","year":"2013","journal-title":"J. Neurosci. Methods"},{"key":"ref_17","unstructured":"What is Alzheimer\u2019s?. Available online: http:\/\/www.alz.org\/alzheimers_disease_what_is_alzheimers.asp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.neuroimage.2005.08.062","article-title":"Fully-automated detection of cerebral water content changes: Study of age- and gender-related H2O patterns with quantitative MRI","volume":"29","author":"Neeb","year":"2006","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","article-title":"A fast diffeomorphic image registration algorithm","volume":"38","author":"Ashburner","year":"2007","journal-title":"NeuroImage"},{"key":"ref_20","unstructured":"Brown, T.A. (2002). Genomics, Wiley. [2nd ed.]."},{"key":"ref_21","unstructured":"Radford, A., Atkinson, M., Britain, D., Clahsen, H., and Spencer, A. (1999). Linguistics: An Introduction, Cambridge University Press. [2nd ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9523","DOI":"10.1073\/pnas.1301816110","article-title":"Preventing Alzheimer\u2019s disease-related gray matter atrophy by B-vitamin treatment","volume":"110","author":"Douaud","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4965","DOI":"10.1002\/hbm.22525","article-title":"Shape analysis, a field in need of careful validation","volume":"35","author":"Gao","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_24","unstructured":"The Brain Geek. Available online: http:\/\/thebraingeek.blogspot.jp\/2012\/04\/folds-of-brain.html."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.neuron.2013.10.045","article-title":"Cortical evolution: Judge the brain by its cover","volume":"80","author":"Geschwind","year":"2013","journal-title":"Neuron"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1038\/nrn3707","article-title":"Growth and folding of the mammalian cerebral cortex: From molecules to malformations","volume":"15","author":"Sun","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_27","unstructured":"Keogh, E., Wei, L., Xi, X., Lee, S.H., and Vlachos, M. (2006, January 12\u201315). LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tak, Y.S., and Hwang, E. (2007, January 16\u201319). A leaf image retrieval scheme based on partial dynamic time warping and two-level filtering. Proceedings of the 7th IEEE International Conference on Computer and Information Technology, Fukushima, Japan.","DOI":"10.1109\/CIT.2007.158"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2005.21","article-title":"WARP: Accurate retrieval of shapes using phase of Fourier descriptors and time warping distance","volume":"27","author":"Bartolini","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","first-page":"275","article-title":"Chaos and the new science of the brain","volume":"1","author":"Skarda","year":"1990","journal-title":"Concepts Neurosci."},{"key":"ref_31","unstructured":"Liebovitch, L.S. (1998). Fractals and Chaos Simplified for the Life Science, Oxford University Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.euroneuro.2012.10.010","article-title":"Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI","volume":"23","author":"Stam","year":"2013","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref_33","unstructured":"Strogatz, S.H. (2014). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, Westview. [2nd ed.]."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pham, T.D. (2014). Classification of complex biological aging images using fuzzy Kolmogorov-Sinai entropy. J. Phys. D Appl. Phys., 47.","DOI":"10.1088\/0022-3727\/47\/48\/485402"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3390\/e17010123","article-title":"Assessment of time and frequency domain entropies to detect sleep apnoea in heart rate variability recordings from men and women","volume":"17","author":"Alvarez","year":"2015","journal-title":"Entropy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3390\/e17010231","article-title":"Multiscale entropy analysis of heart rate variability for assessing the severity of sleep disordered breathing","volume":"17","author":"Pan","year":"2015","journal-title":"Entropy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1063\/1.166092","article-title":"Approximate entropy (ApEn) as a complexity measure","volume":"5","author":"Pincus","year":"1995","journal-title":"Chaos"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.chaos.2012.03.001","article-title":"Regularity dimension of sequences and its application to phylogenetic tree reconstruction","volume":"45","author":"Pham","year":"2012","journal-title":"Chaos Soliton. Fract."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","article-title":"Recurrence plots of dynamical systems","volume":"4","author":"Eckmann","year":"1987","journal-title":"EPL Europhys. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/0167-2789(93)90009-P","article-title":"A practical method for calculating largest Lyapunov exponents from small data sets","volume":"65","author":"Rosenstein","year":"1993","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Williams, G.P. (1997). Chaos Theory Tamed, Joseph Henry Press.","DOI":"10.1201\/9781482295412"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal Process"},{"key":"ref_44","unstructured":"Rabiner, L.R., and Juang, B. (1993). Fundamentals of Speech Recognition, Prentice-Hall."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1103\/PhysRevA.28.2591","article-title":"Estimation of the Kolmogorov entropy from a chaotic signal","volume":"28","author":"Grassberger","year":"1983","journal-title":"Phys. Rev. A"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1103\/RevModPhys.57.617","article-title":"Ergodic theory of chaos and strange attractors","volume":"57","author":"Eckmann","year":"1985","journal-title":"Rev. Mod. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Schroeder, M. (1991). Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise, W.H. Freeman.","DOI":"10.1063\/1.2810323"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S0167-2789(97)82003-9","article-title":"Recurrence plots revisited","volume":"108","author":"Casdagli","year":"1997","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.physrep.2006.11.001","article-title":"Recurrence plots for the analysis of complex systems","volume":"438","author":"Marwan","year":"2007","journal-title":"Phys. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.physd.2008.09.013","article-title":"Generalized recurrence plots for the analysis of images from spatially distributed systems","volume":"238","author":"Facchini","year":"2009","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_51","unstructured":"Metin, A. (2006). Wiley Encyclopedia of Biomedical Engineering, John Wiley & Sons."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.chaos.2014.04.007","article-title":"The butterfly effect in ER dynamics and ER-mitochondrial contacts","volume":"65","author":"Pham","year":"2014","journal-title":"Chaos Soliton. Fract."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Pham, T.D. (2015). Validation of computer models for evaluating the efficacy of cognitive stimulation therapy. Wirel. Pers. Commun.","DOI":"10.1007\/s11277-015-3017-7"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1007\/BFb0091924","article-title":"Detecting strange attractors in turbulence","volume":"898","author":"Takens","year":"1981","journal-title":"Lect. Notes Math."},{"key":"ref_55","unstructured":"Ecker, J.G., and Kupferschmid, M. (1988). Introduction to Operations Research, John Wiley & Sons."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e0118739","DOI":"10.1371\/journal.pone.0118739","article-title":"Computerized assessment of communication for cognitive stimulation for people with cognitive decline using spectral-distortion measures and phylogenetic inference","volume":"10","author":"Pham","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"130","DOI":"10.2307\/2406046","article-title":"A quantitative approach to a problem in classification","volume":"11","author":"Michener","year":"1957","journal-title":"Evolution"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref_59","unstructured":"IXI (Information eXtraction from Images) Dataset. Available online: http:\/\/www.brain-development.org."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.neuroimage.2010.03.004","article-title":"Age-related changes in grey and white matter structure throughout adulthood","volume":"51","author":"Giorgio","year":"2010","journal-title":"Neuroimage"},{"key":"ref_61","unstructured":"SPM: Statistical Parametric Mapping. Available online: http:\/\/www.fil.ion.ucl.ac.uk\/spm."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","article-title":"Unified segmentation","volume":"26","author":"Ashburner","year":"2005","journal-title":"Neuroimage"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1475-925X-12-S1-S2","article-title":"Development of a brain MRI-based hidden Markov model for dementia recognition","volume":"12","author":"Chen","year":"2013","journal-title":"BioMed. Eng. Online"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Theodoridis, S., Pikrakis, A., Koutroumbas, K., and Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach, Academic Press.","DOI":"10.1016\/B978-1-59749-272-0.50003-7"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sprott, J.C. (2003). Chaos and Time-Series Analysis, Oxford University Press.","DOI":"10.1093\/oso\/9780198508397.001.0001"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1148\/radiology.169.3.3055034","article-title":"Exclusion of fetal ventriculomegaly with a single measurement: The width of the lateral ventricular atrium","volume":"169","author":"Cardoza","year":"1988","journal-title":"Radiology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1093\/cercor\/bhi044","article-title":"Regional brain changes in aging healthy adults: General trends, individual differences and modifiers","volume":"15","author":"Raz","year":"2005","journal-title":"Cereb. Cortex"},{"key":"ref_68","unstructured":"Craik, F.I.M., and Salthouse, T.A. (2008). The Handbook of Aging and Cognition, Psychology Press. [3rd ed.]."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/12\/7868\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:53:43Z","timestamp":1760216023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/12\/7868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,12,9]]},"references-count":68,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2015,12]]}},"alternative-id":["e17127868"],"URL":"https:\/\/doi.org\/10.3390\/e17127868","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,12,9]]}}}