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Entropy 2015, 17(12), 8130-8151; doi:10.3390/e17127868

Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping

1
Department of Biomedical Engineering, Linköping University, Linköping 581 83, Sweden
2
Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
3
Department of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan
4
Department of Health Services Administration, China Medical University, Taichung 40402, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 8 October 2015 / Revised: 13 November 2015 / Accepted: 30 November 2015 / Published: 9 December 2015
View Full-Text   |   Download PDF [512 KB, uploaded 9 December 2015]   |  

Abstract

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. View Full-Text
Keywords: brain-age grouping; brain-age prediction; magnetic resonance imaging; phylogenetic tree reconstruction; measures of complexity; chaos; nonlinear dynamics; neurodegeneration brain-age grouping; brain-age prediction; magnetic resonance imaging; phylogenetic tree reconstruction; measures of complexity; chaos; nonlinear dynamics; neurodegeneration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pham, T.D.; Abe, T.; Oka, R.; Chen, Y.-F. Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping. Entropy 2015, 17, 8130-8151.

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