Multimodal MR Prediction Models for Late-Life Depression and Treatment Response

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, instead of underlying brain characteristics, and treatment is guided by trial and error instead of individual suitability associated with underlying brain characteristics. Also, previous brain-imaging studies attempting to resolve this issue have traditionally focused on mid-life depression using a single imaging modality and region-based approach, which may not fully explain the complexity of the underlying brain characteristics; especially for late-life depression. We aimed to evaluate and compare underlying brain characteristics of late-life depression diagnosis and treatment response by estimating accurate prediction models using multi-modal magnetic resonance imaging and non-imaging measures. Based on our finding, late-life depression diagnosis and treatment response predictors involve measures from different imaging modalities, which are indicative of differences in underlying brain characteristics

The most generative maximum margin Bayesian networks

Although discriminative learning in graphical models generally improves classification results, the generative semantics of the model are compromised. In this paper, we introduce a novel approach of hybrid generative-discriminative learning for Bayesian networks. We use an SVM-type large margin formulation for discriminative training, introducing a likelihood-weighted ℓ1- norm for the SVM-norm-penalization. This simultaneously optimizes the data likelihood and therefore partly maintains the generative character of the model. For many network structures, our method can be formulated as a convex problem, guaranteeing a globally optimal solution. In terms of classification, the resulting models outperform state-of-the art generative and discriminative learning methods for Bayesian networks, and are comparable with linear and kernelized SVMs. Furthermore, the models achieve likelihoods close to the maximum likelihood solution and show robust behavior in classification experiments with missing features. Copyright 2013 by the author(s)