Neuroscience Training Program, University of Wisconsin Madison, WI, USA.
Front Hum Neurosci. 2012 Oct 11;6:281. doi: 10.3389/fnhum.2012.00281. eCollection 2012.
Parallel Independent Component Analysis (para-ICA) is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs) covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para-ICA in cases with unbalanced dimensions using simulated data.
平行独立成分分析（para-ICA）是一种多元方法，可以通过同时对每个数据集执行独立成分分析，同时在两个数据集之间找到互信息，从而识别不同数据模态之间的复杂关系。我们使用 para-ICA 来检验以下假设：即常见静息态网络（RSN）的空间子成分与特定行为测量相关。从 24 名年轻成年人中收集了静息状态扫描和一系列行为指标。通过与公共模板的空间相关性进行组独立成分分析（group ICA），确定了常见的 RSN。共鉴定出 9 个 RSN，并在每个网络上运行 para-ICA，将行为测量矩阵作为第二种数据类型。有 5 个网络的空间子成分与行为成分显著相关。其中包括注意的颞顶网络的一个子成分，它与持续注意任务的不同试验类型有差异协变；默认模式网络的子成分与注意力和工作记忆任务有协变；以及双侧额叶网络的一个子成分，根据其细胞结构将左额下回分为三个簇，与工作记忆表现有差异协变。此外，我们使用模拟数据证明了 para-ICA 在不平衡维度情况下的有效性。
