The validity of different multiple comparison correction methods in the analysis of brain function image data

Abstract

Objective To explore the effectiveness of different multiple comparisons correction methods by comparing the detection rate and false positive rate of brain activation analysis using functional magnetic resonance imaging (fMRI) data. Methods On the basis of task-based fMRI dataset (including low-intensity and high-intensity stimuli condition, n=20) and resting-state fMRI dataset(n=32), brain activation results were corrected by multiple comparsion correction methods in SPM and SnPM13 software, and the activation detection rate and false positive rate were compared with different correction methods. Results Voxel-or peak-based correction methods had relatively low false positive rate.When P<0.05 after correction, the proportion of the subjects with false-positive were 0.19 and 0.16, and the number of false-positive voxels were 404 and 2 448, respectively.But the two methods had low detection rate, which were more suitable for detecting strong activation.While cluster-based correction methods had relative high detection rate and high false positive rate.When P<0.05 after correction, the proportion of the subjects with false-positive were 0.34 and 0.38, and the number of false-positive voxels were 7 870 and 8 320, respectively.And thus they were more suitable for detecting weak activation. Group-level analysis could effectively reduce false positive rate. Conclusion In practice, researchers should choose a suitable correction method based on their specific research objectives and data to achieve a balance between the detection rate and false positive rate. Key words: fMRI; Brain activation; Multiple comparisons correction; False positive rate; Detection rate