Inferring task-related networks using independent component analysis in magnetoencephalography.
Inferring task-related networks using independent component analysis in magnetoencephalography.
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DOI：
10.1016/j.neuroimage.2012.04.046
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发表时间：
2012-08-01
期刊：
影响因子：
5.7
通讯作者：
Woolrich, M. W.
中科院分区：
文献类型：
--
作者：
Luckhoo, H.;Hale, J. R.;Stokes, M. G.;Nobre, A. C.;Morris, P. G.;Brookes, M. J.;Woolrich, M. W.
关键词：
提出了一种新的分析脑磁图（MEG）中任务阳性数据的框架，该框架可以识别任务相关网络。结合联合收割机波束形成，希尔伯特变换和时间独立成分分析（伊卡）的技术最近已被应用于静息态脑磁图数据，并已被证明提取类似于功能磁共振成像中发现的静息态网络。在这里，我们以两种方式扩展这种方法。首先，我们系统地研究了连接测量的时频窗口的优化。这是通过估计已知的静止状态网络的节点之间的功能连接分数的分布，并将其与完全由于逆问题引起的空间泄漏的伪像分数的分布进行对比来实现的。我们发现，功能连接，无论是在休息状态和认知任务期间，最好的估计，通过相关性的振荡包络在8-20 Hz的频率范围内，时间下采样的窗口1-4秒。其次，我们结合联合收割机伊卡与一般线性模型（GLM），将知识的任务结构到我们的连接性分析。伊卡与GLM的结合有助于克服这些技术在独立使用时的问题：即，从伊卡中代表噪声的独立分量中解释和分离感兴趣的独立分量，以及在应用GLM时对多重比较的校正。我们证明了2回工作记忆任务的方法，并表明，这种新的分析框架是能够阐明的功能网络参与的任务超出了单独使用GLM。我们在海马区发现了与任务相关的局部活动的证据，这是很难用标准方法可靠地检测到的。任务正伊卡，加上GLM，有可能成为一个强大的工具，在分析脑磁数据。我们概述了两种在任务阳性脑磁图数据中检测功能网络的方法。首先是分析以找到用于FC检测的最佳时频窗口。第二种方法将独立成分分析与一般线性模型相结合。方法应用于静息状态和工作记忆任务数据。伊卡/GLM减少了多重比较并将CARICANI本地化。
A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8–20 Hz frequency range, temporally down-sampled with windows of 1–4 s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data. ► We outline two methods for detecting functional networks in task positive MEG data. ► First is an analysis to find the optimum time-frequency window for FC detection. ► The second combines independent component analysis with the general linear model. ► Methods are applied to resting state and working memory task data. ► ICA/GLM reduces multiple comparisons and localises hippocampi.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
DOI：
10.1073/pnas.111134598
发表时间：
2001-06-05
影响因子：
11.1
作者：
Egan, MF;Goldberg, TE;Weinberger, DR
通讯作者：
Weinberger, DR
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
4.8
作者：
Abou-Elseoud, Ahmed;Starck, Tuomo;Kiviniemi, Vesa
通讯作者：
Kiviniemi, Vesa
影响因子：
5.7
作者：
Liu, Zhongming;Fukunaga, Masaki;de Zwart, Jacco A.;Duyn, Jeff H.
通讯作者：
Duyn, Jeff H.