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As classifier we used a linear discriminant analysis (LDA) implemented through the MATLAB function classify. For validation, we performed 1000 repetitions of a 10-fold cross validation. As feature we quantified structural asymmetry of the paired 63 structures (cf. Sections 2.1.2?and?Appendix A). We employed an absolute asymmetry index (AAI) (Galaburda et al., 1987?and?Bonilha et al., 2014) based on a structure��s volume (V) in the left and right hemisphere, defined as: equation(8) AAI=100%|Vleft-Vright|0.5(Vleft+Vright)Specifically, we used the sum of the absolute asymmetry indices of either all 63 structures, all 49 cortical structures or all 14 non-cortical structures. http://www.selleckchem.com/products/bay80-6946.html The results for distinguishing two MC (MC http://www.selleckchem.com/products/Bafetinib.html the average of sensitivity and specificity, to report classification accuracy. Table 6 shows that our method yields 76.0% accuracy in distinguishing groups in the Marshall Classification system based on acute-phase images. The Marshall Classification system is not a linear scale as it takes both midline shift and the size of lesions into account (compare Appendix B). However, in the classification experiment we were able to discriminate between classes without (MC http://en.wikipedia.org/wiki/MYO10 Based on the segmentations of non-cortical structures in the follow-up images, we achieved 66.8% accuracy in GOS classification. The classification results are summarised in Table 6. The high specificity for MC classification shows that the presented method does very well in detecting normal appearing brains at the acute stage. The high specificity for GOS classification confirms that the presented approach is able to predict a favourable outcome of a TBI. These findings suggest that structural brain asymmetry could be a sufficient criterion to indicate an unfavourable disease outcome. On the other hand, symmetry seems to be a necessary criterion for favourable disease outcome. It is not, however, a sufficient criterion to rule out an unfavourable outcome. Receiver operating characteristic (ROC) curves for these classification experiments are shown in Fig. 11.