Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
PLoS One. 2014 Jun 6;9(6):e98697. doi: 10.1371/journal.pone.0098697. eCollection 2014.
Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.
近年来，功能连接已成为研究的一个重要领域。在典型的空间分辨率下，大脑中的每个体素与其他体素之间大约有 3 亿个连接。这种连接模式被称为功能连接组。通常在实验组和条件之间进行连接性比较。由于涉及到大量的统计检验，当在整个连接组中进行比较时，用于控制第一类错误率的标准方法可能不敏感。为了解决这个问题，引入了两种新的基于聚类的方法——聚类大小统计量（CSS）和聚类质量统计量（CMS），以控制所有连接值的总体错误率。这些方法在与传统基于任务的 fMRI 中使用的基于聚类的方法类似的统计框架内运行。两种方法都是数据驱动的、基于置换的，并且需要最小的统计假设。在这里，通过使用模拟数据集，在接收者操作特征（ROC）分析中评估了每个过程的性能。还在真实数据上测试了每种方法的相对灵敏度：对 12 名受试者在正常条件下和高碳酸血症状态（通过吸入 6%CO2 在 21%O2 和 73%N2 中诱导）下进行 BOLD（血氧水平依赖）fMRI 扫描。CSS 和 CMS 都检测到了正常和高碳酸血症状态之间连接性的显著变化。在个体连接水平上进行的总体错误校正显示连接性没有显著变化。
