Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.
Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.
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DOI：
10.1016/j.neuroimage.2013.03.019
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发表时间：
2013-08-01
期刊：
影响因子：
5.7
通讯作者：
Frederick, Blaise deB
中科院分区：
文献类型：
--
作者：
Tong, Yunjie;Hocke, Lia M.;Nickerson, Lisa D.;Licata, Stephanie C.;Lindsey, Kimberly P.;Frederick, Blaise deB
关键词：
独立成分分析（ICA）广泛应用于静息态功能连接研究。 ICA 是一种数据驱动的方法，不使用先验的解剖或功能假设。然而，其结果是，它仍然依赖于用户来区分与神经元激活、外周起源信号（没有直接归因的神经元起源，例如呼吸、心脏搏动和迈耶波）和采集伪影相对应的独立分量（IC）。在这项同时进行的近红外光谱 (NIRS)/功能磁共振成像 (fMRI) 静息态研究中，我们开发了一种方法来系统地、定量地识别那些表现出来自外围信号的强烈贡献的 IC。我们将 ICA 组（来自 FSL 的 MELODIC）应用于 10 名健康参与者的静息状态数据。 NIRS 在每个参与者的指尖同时检测到的系统低频振荡 (LFO) 被用作回归器，与每个特定主题的 IC 时间进程相关联。与系统 LFO 高度相关的 IC 是那些与之前描述的感觉运动、视觉和听觉网络密切相关的 IC。与默认模式和额顶网络相关的 IC 受外围信号的影响较小。使用引导法评估结果的一致性和再现性。这一结果表明，血流动力学特性的系统性低频振荡覆盖了 ICA 分析中识别的许多空间模式的时间进程，这使得大脑这些区域的连接性检测和解释变得复杂。
Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant’s fingertip by NIRS was used as a regressor to correlate with every subject-specific IC timecourse. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the timecourses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain
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影响因子：
5.7
作者：
Frederick, Blaise deB.;Nickerson, Lisa D.;Tong, Yunjie
通讯作者：
Tong, Yunjie
影响因子：
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作者：
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DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
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通讯作者：
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影响因子：
5.7
作者：
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通讯作者：
Duyn, Jeff H.
影响因子：
3.7
作者：
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通讯作者：
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