In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures.
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机译：在这项研究中，我们使用infomax算法对来自六个受试者的12个fMRI数据集进行了101次分解。第一次分解被作为参考分解；其他的则用于形成100 x 100的成分矩阵。通过匈牙利排序方法找到了此矩阵中各组成部分之间的等价关系，定义为与参考分解的各组成部分的最大空间相关性，并用于为每个数据集形成100个等价类。然后，我们使用基于组件分布，时程和ROC曲线的不确定性度量测试了等效类中匹配组件的可重复性。 Infomax ICA很少会在不同分解中派生几乎相同的组件。即使使用比相关性和检测理论措施更严格的矢量角不确定性措施，每个数据集的复制组件也很少。
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