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Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
University of Aizu, Japan.
University of Aizu, Japan.
Central Taiwan University of Science and Technology, Taiwan; China Medical University, Taiwan.
2015 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 17, no 12, 8130-8151 p.Article in journal (Refereed) Published
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Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI AG , 2015. Vol. 17, no 12, 8130-8151 p.
Keyword [en]
brain-age grouping; brain-age prediction; magnetic resonance imaging; phylogenetic tree reconstruction; measures of complexity; chaos; nonlinear dynamics; neurodegeneration
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-124513DOI: 10.3390/e17127868ISI: 000367443600019OAI: oai:DiVA.org:liu-124513DiVA: diva2:899557
Available from: 2016-02-02 Created: 2016-02-01 Last updated: 2017-11-30

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Pham, Tuan

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  • apa
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