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. 2019 Aug 14;10:789. doi: 10.3389/fneur.2019.00789

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Copyright © 2019 Franke and Gaser.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Influences of the various parameters on BrainAGE estimation accuracy. (1) The accuracy of age estimation essentially depends on the number of subjects used for training the age estimation model (blue lines: full training sample; green lines: ½ training sample; red lines: ¼ training sample). (2) The method for preprocessing the T1-weighted MRI images also showed a strong influence on the accuracy of age estimation. (3) Data reduction via principal component analysis (PCA) only had a moderate effect on the mean absolute error (MAE). AF, affine registration; NL, non-linear registration; R4/8, re-sampling to spatial resolution of 4/8 mm; S4/8, smoothing with FWHM smoothing kernel of 4/8 mm. [Figure and legend modified from Franke et al. (32), with permission from Elsevier, Amsterdam].