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首页> 外文期刊>NeuroImage >Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study.
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Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study.

机译:快速事件相关功能近红外光谱(NIRS)数据的基于模型的分析:一项参数验证研究。

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摘要

To validate the usefulness of a model-based analysis approach according to the general linear model (GLM) for functional near-infrared spectroscopy (fNIRS) data, a rapid event-related paradigm with an unpredictable stimulus sequence was applied to 15 healthy subjects. A parametric design was chosen wherein four differently graded contrasts of a flickering checkerboard were presented, allowing directed hypotheses about the rank order of the evoked hemodynamic response amplitudes. The results indicate the validity of amplitude estimation by three main findings (a) the GLM approach for fNIRS data is capable to identify human brain activation in the visual cortex with inter-stimulus intervals of 4-9 s (6.5 s average) whereas in non-visual areas no systematic activation was detectable; (b) the different contrast level intensities lead to the hypothesized rank order of the GLM amplitude parameters: visual cortex activation evoked by highest contrast>moderate contrast>lowest contrast>no stimulation; (c) analysis of null-events (no stimulation) did not produce any significant activation in the visual cortex or in other brain areas. We conclude that a model-based GLM approach delivers valid fNIRS amplitude estimations and enables the analysis of rapid event-related fNIRS data series, which is highly relevant in particular for cognitive fNIRS studies.
机译:为了验证根据通用线性模型(GLM)对功能性近红外光谱(fNIRS)数据进行的基于模型的分析方法的有效性,将具有不可预测的刺激序列的快速事件相关范例应用于15名健康受试者。选择了一种参数设计,其中呈现了闪烁的棋盘的四个不同等级的对比度,从而可以对诱发的血液动力学响应幅度的等级顺序进行有针对性的假设。结果表明,通过三个主要发现进行幅度估计的有效性(a)fNIRS数据的GLM方法能够以4-9 s(平均6.5 s)的刺激间隔识别视觉皮层中的人脑激活,而非-视觉区域,无法检测到系统激活; (b)不同的对比度水平强度导致了GLM振幅参数的假设等级顺序:最高对比度>中等对比度>最低对比度>无刺激引起的视觉皮层激活; (c)对无效事件(无刺激)的分析未在视觉皮层或其他大脑区域产生任何明显的激活。我们得出结论,基于模型的GLM方法可提供有效的fNIRS振幅估计值,并能够分析与事件相关的快速fNIRS数据序列,这尤其与认知fNIRS研究高度相关。

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