The paper introduces a method for thresholding statistical maps in functional neuroimaging using the False Discovery Rate (FDR) to control the expected proportion of false positives among rejected hypotheses. Traditional methods like Bonferroni correction are conservative and often lead to a high number of false negatives. The FDR approach, developed in statistics, is more flexible and adaptive, allowing for automatic threshold selection based on the data's characteristics. The method is demonstrated through simulations and applied to real fMRI data from two experiments: a motor task and an auditory stimulation study. The results show that the FDR method is more powerful and adaptive compared to other methods, providing a more interpretable and robust solution to the threshold selection problem in neuroimaging.The paper introduces a method for thresholding statistical maps in functional neuroimaging using the False Discovery Rate (FDR) to control the expected proportion of false positives among rejected hypotheses. Traditional methods like Bonferroni correction are conservative and often lead to a high number of false negatives. The FDR approach, developed in statistics, is more flexible and adaptive, allowing for automatic threshold selection based on the data's characteristics. The method is demonstrated through simulations and applied to real fMRI data from two experiments: a motor task and an auditory stimulation study. The results show that the FDR method is more powerful and adaptive compared to other methods, providing a more interpretable and robust solution to the threshold selection problem in neuroimaging.