Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an important, but non-trivial issue. We propose the use of logistic regression with a least absolute shrinkage and selection operator (LASSO) to capture the most critical input features. In particular, we consider application of group LASSO to select brain areas relevant to diagnosis. An additional advantage of LASSO is its probabilistic output, which allows evaluation of diagnosis certainty. To verify our approach, we obtained semantic and phonological verbal fluency fMRI data from 31 depression patients and 31 control subjects, and compared the performances of group LASSO (gLASSO), and sparse group LASSO (sgLASSO) to those of standard LASSO (sLASSO), Support Vector Machine (SVM), and Random Forest. Over 90% classification accuracy was achieved with gLASSO, sgLASSO, as well as SVM; however, in contrast to SVM, LASSO approaches allow for identification of the most discriminative weights and estimation of prediction reliability. Semantic task data revealed contributions to the classification from left precuneus, left precentral gyrus, left inferior frontal cortex (pars triangularis), and left cerebellum (c rus1). Weights for the phonological task indicated contributions from left inferior frontal operculum, left post central gyrus, left insula, left middle frontal cortex, bilateral middle temporal cortices, bilateral precuneus, left inferior frontal cortex (pars triangularis), and left precentral gyrus. The distribution of normalized odds ratios further showed, that predictions with absolute odds ratios higher than 0.2 could be regarded as certain.