Predicting human age using regional morphometry and inter-regional morphological similarity

Xun-Heng Wang; Lihua Li

doi:10.1117/12.2216041

29 March 2016 Predicting human age using regional morphometry and inter-regional morphological similarity

Xun-Heng Wang, Lihua Li

Author Affiliations +

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 978821 (2016) https://doi.org/10.1117/12.2216041
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Xun-Heng Wang and Lihua Li "Predicting human age using regional morphometry and inter-regional morphological similarity", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978821 (29 March 2016); https://doi.org/10.1117/12.2216041

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KEYWORDS

Brain

Magnetic resonance imaging

Neuroimaging

Performance modeling

Data modeling

Natural surfaces

Anisotropy

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