The validity of different multiple comparison correction methods in the analysis of brain function im-age data
10. 3760/cma. j. issn. 1674-6554. 2019. 10. 015
- VernacularTitle:不同多重比较校正方法在脑功能影像数据分析中的有效性
- Author:
Yingchao SONG
1
;
Li HU
;
Meng LIANG
Author Information
1. 天津医科大学医学影像学院 300203
- Keywords:
fMRI;
Brain activation;
Multiple comparisons correction;
False positive rate;
Detection rate
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2019;28(10):941-946
- CountryChina
- Language:Chinese
-
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 acti-vation 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-pos-itive 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-posi-tive 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.
