The validity of different multiple comparison correction methods in the analysis of brain function image data / 中华行为医学与脑科学杂志

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.