Soto Juan L P, Pantazis Dimitrios, Jerbi Karim, Lachaux Jean-Phillipe, Garnero Line, Leahy Richard M
Signal and Image Processing Institute, University of Southern California, 3740 McClintock Ave., Los Angeles, CA 90089-2564, USA.
Hum Brain Mapp. 2009 Jun;30(6):1922-34. doi: 10.1002/hbm.20765.
We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects.
我们描述了一种使用多变量统计推断从脑磁图（MEG）数据计算的电流密度皮质约束图中检测脑激活的方法。我们对各个时间段应用时频（小波）分析，以生成多个时频带中大脑皮质上脑信号功率的动态图像。我们通过连接每个频带中的功率来形成向量观测值，并将它们拟合到每个时间带和皮质位置的单独多变量线性模型中，以实验条件作为预测变量。使用置换检验和最大统计量方法对所得的罗伊最大根统计量图进行显著性阈值处理。如果一个源超过统计阈值，则认为该源具有显著性，该阈值被选择用于控制整个皮质表面的族系错误率或至少一个假阳性的概率。我们在模拟数据和来自MEG视觉运动任务研究的实验数据上，将多变量方法与现有的单变量时频MEG信号分析方法进行比较和评估。我们的结果表明，当频带间功率存在相关性时，多变量方法在检测实验效应方面比单变量方法更强大。我们进一步描述了受保护的F检验和线性判别分析，以识别对实验效应有显著贡献的各个频率。
