Thirion Bertrand, Pinel Philippe, Tucholka Alan, Roche Alexis, Ciuciu Philippe, Mangin Jean-François, Poline Jean-Baptiste
INRIA Futurs Research Institute, Parc Club Orsay Universit ZAC des Vignes, 91893 Orsay Cedex, France.
IEEE Trans Med Imaging. 2007 Sep;26(9):1256-69. doi: 10.1109/TMI.2007.903226.
Group studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses.
功能磁共振成像数据集的组研究通常基于对每个体素处各受试者平均信号的计算（随机效应分析），假设所有受试者都已置于相同的解剖空间中（归一化）。尽管这种方法能保证正确的特异性（误检率），但由于三个原因其效率不高：i）其潜在假设，即个体数据集的完美配准以及组水平上测量信号的正态性经常被违反；ii）一般来说组规模较小，因此参数分布的渐近近似不成立；iii）图像尺寸较大，需要一些保守策略来控制误检率，这有可能增加假阴性的数量。鉴于构建受试者间变异性的生成模型或参数模型仍然非常具有挑战性，我们依赖基于规则的自下而上的方法：我们提出了一组程序，从每个受试者的数据中检测感兴趣的结构，然后在受试者之间寻找对应关系，并勾勒出所研究组中最可重复的激活区域。这个框架能够严格控制误检数量。结果表明，与标准方法相比，这种分析具有更高的有效性，并且提高了组分析的敏感性和可靠性。此外，它直接提供了关于激活区域在受试者之间的空间位置对应关系或变异性的信息，这在基于体素的标准分析中很难获得。
