This chapter discusses a now established linear multivariate technique called source-based morphometry (SBM), a data-driven multivariate approach for decomposing structural brain imaging data into commonly covarying components and subject-specific loading parameters. It has been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of data-driven multivariate techniques over univariate analysis for imaging studies. We then discuss results from a range of recent imaging studies which have successfully applied this linear technique. We also present extensions of this framework such as nonlinear SBM, morphometric analysis using independent vector analysis (IVA), and related approaches such as parallel independent component analysis with reference (pICA-R). This chapter thus reviews a wide range of multivariate, data-driven approaches which have been successfully applied to brain imaging studies.