1. Fig. 5 Multivariate fMRI analysis.
Multivariate fMRI analysis. Top: Weight maps obtained performing feature selection. The right amygdala (A) and right striatum (B) were the most discriminative features. Coronal and axial slices shown here are same as for univariate analysis (see Fig. 4) to allow for direct comparison between multi- and univariate analyses. (C) Receiver operating characteristic curves [and respective areas under the curve (AUCs)] for L2-loss and L2-regularized support vector classifier (SVC; blue), L2-regularized logistic regression (LR; purple), and relevance vector machine (RVM; yellow). We used a leave-one-subject-out nested cross-validation scheme and performed hyperparameter tuning using Nelder-Mead optimization routine. (D) Scatterplot showing significant robust linear correlations between subject-wise likelihood of treatment response as estimated by RVM classifier and posttreatment BDI percentage change.
Filippo Queirazza et al. Sci Adv 2019;5:eaav4962
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).