Background: Aggregation of functional magnetic resonance imaging (fMRI) data in regions-of-interest (ROIs) is required for complex statistical analyses not implemented in standard fMRI software. Different data-aggregation measures assess various aspects of neural activation, including spatial extent and intensity. New method: In this study, conducted within the framework of the PREDICT study, we compared different aggregation measures for voxel-wise fMRI activations to be used as prognostic factors for relapse in 49 abstinent alcohol-dependent individuals in an outpatient setting using a cue-reactivity task. We compared the importance of the data-aggregation measures as prognostic factors for treatment outcomes by calculating the proportion of explained variation. Results and comparison with existing method(s): Relapse risk was associated with cue-induced brain activation during abstinence in the ventral striatum (VS) and in the orbitofrontal cortex (OFC). While various ROI measures proved appropriate for using fMRI cue-reactivity to predict relapse, on the descriptive level the most "important" prognostic factor was a measure defined as the sum of t-values exceeding an individually defined threshold. Data collected in the VS was superior to that from other regions. Conclusions: In conclusion, it seems that fMRI cue-reactivity, especially in the VS, can be used as prognostic factor for relapse in abstinent alcohol-dependent patients. Our findings suggest that data-aggregation measures that take both spatial extent and intensity of cue-induced brain activation into account make better biomarkers for predicting relapse than measures that consider an activation's spatial extent or intensity alone.