Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks
Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks
复制标题
DOI：
10.1002/hbm.20581
复制
发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Pearlson, Godfrey D.
中科院分区：
文献类型：
--
作者：
Calhoun, Vince D.;Kiehl, Kent A.;Pearlson, Godfrey D.
人们越来越多地使用功能磁共振成像（fMRI）来研究表现出时间相干波动的大脑区域。这种网络通常是在休息期间收集的功能磁共振成像扫描的背景下识别的（因此被称为“休息状态网络”）；然而，它们也存在于执行认知任务期间（并受其调节）。在本文中，我们将此类网络称为时间相干网络（TCN）。尽管对于这些波动的生理来源仍存在一些争论，但 TCN 正在以多种方式进行研究。最近的研究探讨了 TCN 可用于识别与各种脑部疾病（例如精神分裂症、自闭症或阿尔茨海默病）相关的模式的方法。独立成分分析 (ICA) 是一种用于识别 TCN 的方法。 ICA 是一种数据驱动的方法，对于在多个操作同时发生的复杂认知任务期间分解激活特别有用。在本文中，我们回顾了最近的 TCN 研究，重点关注那些使用 ICA 的研究。我们还提出了新的结果，表明 TCN 是稳健的，并且可以在健康个体和精神分裂症患者中在休息和执行认知任务期间一致地识别。此外，多个 TCN 在认知任务与休息期间显示出时间和空间调制。总之，TCN 作为脑部疾病的潜在成像生物标志物显示出相当大的前景，尽管每个网络都需要进行更详细的研究。
Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail.