This paper presents a new multigroup classification method based on subtle differences in regional brain activity during the completion of a functional magnetic resonance imaging (fMRI) challenge paradigm. Classification is performed based on features derived from BOLD time intensity curves in selected regions of interest (ROI). For each ROI, a mean time intensity curve [called mean regional response (MRR)] is calculated from realigned and normalized datasets. The overall subject performance is characterized with a vector of features obtained using nonlinear modeling of all subject's MRRs with a mixture of time shifted Gaussian functions. The classification is performed in the reduced-dimension optimal discrimination space, obtained through canonical transformations of original feature space. In order to demonstrate feasibility of the proposed method, classification of three groups of subjects is presented. The three groups are defined as heavy marijuana smokers after 24 hours of abstinence, heavy marijuana smokers after 28 days of abstinence, and healthy nonusing controls. The proposed method can be useful as an analytic tool for the discrimination of different groups of subjects based on temporal features of functional magnetic resonance imaging activation.