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Age prediction on the basis of brain anatomical measures


Valizadeh, S A; Hänggi, Jürgen; Mérillat, Susan; Jäncke, Lutz (2017). Age prediction on the basis of brain anatomical measures. Human Brain Mapping, 38(2):997-1008.

Abstract

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R(2)  = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R(2)  = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R(2)  = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R(2)  = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Abstract

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R(2)  = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R(2)  = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R(2)  = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R(2)  = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

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Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
08 University Research Priority Programs > Dynamics of Healthy Aging
Dewey Decimal Classification:150 Psychology
Language:English
Date:February 2017
Deposited On:16 Nov 2016 09:30
Last Modified:11 Jan 2017 02:02
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:1065-9471
Publisher DOI:https://doi.org/10.1002/hbm.23434
PubMed ID:27807912

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