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. Author manuscript; available in PMC: 2018 Apr 18.
Published in final edited form as: Neuroimage. 2017 Sep 5;170:54–67. doi: 10.1016/j.neuroimage.2017.08.068

Fig. 6. Sex prediction accuracies using parcellation schemes as features, for the numbers of networks from K = 2 to K = 25.

Fig. 6

A) The sex prediction accuracies for a 10- fold cross-validation using gradient boosting machine (GBM) as the classifier. The classifier is fed with the node-to-network assignment vectors (with 188 elements) as features and a binary output (male vs. female) is predicted for an unseen fold of subjects. The mean and standard deviation across all folds are depicted in blue error bars. To determine the significance of our predictive model, the accuracies derived from the null distributions are also depicted in orange error bars. B) 2-tailed t-test comparison of the head motion between the two sex groups. There are no significant differences in head motion between female (N = 458, mean = 8.9e-02, s.d. = 3.41e-02) and male (N = 367, mean = 8.8e-02, s.d. = 3.55e-02) subjects (two-tailed t-test: t(825) = 0.47, p = 0.64).