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Proceedings Paper

Predicting human age using regional morphometry and inter-regional morphological similarity
Author(s): Xun-Heng Wang; Lihua Li
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Paper Abstract

The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17±20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p < 0.00001, evaluated by Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

Paper Details

Date Published: 29 March 2016
PDF: 7 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978821 (29 March 2016); doi: 10.1117/12.2216041
Show Author Affiliations
Xun-Heng Wang, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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