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This paper presents a general framework for obtaining interpretablemultivariate discriminative models that allow efficient statistical inferencefor neuroimage analysis. The framework, termed generative discriminativemachine (GDM), augments discriminative models with a generative regularizationterm. We demonstrate that the proposed formulation can be optimized in closedform and in dual space, allowing efficient computation for high dimensionalneuroimaging datasets. Furthermore, we provide an analytic estimation of thenull distribution of the model parameters, which enables efficient statisticalinference and p-value computation without the need for permutation testing. Wecompared the proposed method with both purely generative and discriminativelearning methods in two large structural magnetic resonance imaging (sMRI)datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Usingthe AD dataset, we demonstrated the ability of GDM to robustly handleconfounding variations. Using Schizophrenia dataset, we demonstrated theability of GDM to handle multi-site studies. Taken together, the resultsunderline the potential of the proposed approach for neuroimaging analyses.

Generative discriminative models for multivariate inference and statistical mapping in medical imaging