Patel Rajan S, Van De Ville Dimitri, Bowman F DuBois
Department of Biostatistics, The Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Neuroimage. 2006 Jul 1;31(3):1142-55. doi: 10.1016/j.neuroimage.2006.01.012. Epub 2006 Mar 20.
An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets.
神经影像学研究的一个活跃领域涉及检查空间上相距较远的脑区之间的功能关系。在确定两个脑区是否由于真正的功能连接而表现出显著相关性时，必须考虑神经影像学数据中固有的背景空间相关性。我们将背景相关性定义为由基于神经生理学的功能关联以外的因素（如扫描仪诱导的相关性和图像预处理）导致的数据中的时空相关性。我们开发了一种4D时空小波包重采样方法，该方法生成的替代数据仅保留轴向切片内、跨轴向切片以及通过每个体素时间序列的平均背景空间相关性，同时排除由于真正功能关系导致的特定相关性。我们还扩展了一种幅度调整算法，该算法调整我们的替代数据以紧密匹配原始数据的幅度分布。我们的方法改进了现有的基于小波的方法，并将它们扩展到4D。我们应用我们的重采样技术从静息态和运动任务功能磁共振成像数据集确定显著的功能连接性。
