Breakspear Michael, Brammer Michael J, Bullmore Ed T, Das Pritha, Williams Leanne M
Brain Dynamics Centre, Westmead Hospital and University of Sydney, Sydney, Australia.
Hum Brain Mapp. 2004 Sep;23(1):1-25. doi: 10.1002/hbm.20045.
The study of dynamic interdependences between brain regions is currently a very active research field. For any connectivity study, it is important to determine whether correlations between two selected brain regions are statistically significant or only chance effects due to non-specific correlations present throughout the data. In this report, we present a wavelet-based non-parametric technique for testing the null hypothesis that the correlations are typical of the data set and not unique to the regions of interest. This is achieved through spatiotemporal resampling of the data in the wavelet domain. Two functional MRI data sets were analysed: (1) Data from 8 healthy human subjects viewing a checkerboard image, and (2) "Null" data obtained from 3 healthy human subjects, resting with eyes closed. It was demonstrated that constrained resampling of the data in the wavelet domain allows construction of bootstrapped data with four essential properties: (1) Spatial and temporal correlations within and between slices are preserved, (2) The irregular geometry of the intracranial images is maintained, (3) There is adequate type I error control, and (4) Expected experiment-induced correlations are identified. The limitations and possible extensions of the proposed technique are discussed.
脑区之间动态相互依存关系的研究目前是一个非常活跃的研究领域。对于任何连通性研究而言，确定两个选定脑区之间的相关性是具有统计学意义，还是仅仅是由于整个数据中存在的非特异性相关性而产生的偶然效应，这一点很重要。在本报告中，我们提出了一种基于小波的非参数技术，用于检验原假设，即相关性是数据集的典型特征，而非特定感兴趣区域所特有。这是通过在小波域对数据进行时空重采样来实现的。分析了两个功能磁共振成像数据集：（1）来自8名健康人类受试者观看棋盘图像的数据，以及（2）从3名健康人类受试者闭眼休息时获得的“空”数据。结果表明，在小波域对数据进行约束重采样能够构建具有四个基本特性的自抽样数据：（1）切片内和切片间的空间和时间相关性得以保留；（2）颅内图像的不规则几何形状得以维持；（3）有足够的I型错误控制；（4）能够识别预期的实验诱导相关性。文中还讨论了所提出技术的局限性和可能的扩展。
