摘要目的 通过比较脑影像数据分析中不同多重比较校正方法的脑激活检出率和假阳性率来探讨不同校正方法的有效性.方法 基于真实的任务态(包括强、弱两种刺激条件,n=20)和静息态功能磁共振数据(n=32),用脑影像数据分析软件SPM和SnPM13中的多重比较校正方法进行脑激活分析,并对结果从激活检出率和假阳性率两个方面进行比较.结果 基于体素或峰值点的校正方法假阳性率较低,当校正后P<0. 05时,假阳性被试的比例分别为0. 19和0. 16,假阳性体素个数分别为404和2 448,但激活检出率也较低,适合强激活情况;而基于团块的校正方法则相反,当校正后P<0. 05时,假阳性被试的比例分别为0. 34和0. 38,假阳性体素个数分别为7 870和8 320,适合弱激活情况;组水平统计可有效降低假阳性率.结论 在应用中应根据研究目的及数据情况选择适合的校正方法,以平衡激活检出率和假阳性率.

abstractsObjective 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.