Open AccessAge prediction on the basis of brain anatomical measures
Author(s) -
Valizadeh S.A.,
Hänggi J.,
Mérillat S.,
Jäncke L.
Publication year - 2017
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23434
Subject(s) - basis (linear algebra) , support vector machine , artificial neural network , regression , pattern recognition (psychology) , linear regression , artificial intelligence , data set , brain size , feature (linguistics) , set (abstract data type) , random forest , computer science , mathematics , statistics , magnetic resonance imaging , medicine , linguistics , philosophy , geometry , radiology , programming language
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 38:997–1008, 2017 . © 2016 Wiley Periodicals, Inc.