Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study
Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study
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DOI：
10.1016/j.neuroimage.2006.11.028
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发表时间：
2007-04-01
期刊：
影响因子：
5.7
通讯作者：
Fallgatter, A. J.
中科院分区：
文献类型：
--
作者：
Plichta, M. M.;Heinzel, S.;Fallgatter, A. J.
为了验证基于功能近红外光谱 (fNIRS) 数据的通用线性模型 (GLM) 的基于模型的分析方法的有效性，将具有不可预测刺激序列的快速事件相关范例应用于 15 名健康受试者。选择了参数化设计，其中呈现了闪烁棋盘的四种不同分级的对比度，允许对诱发的血液动力学响应幅度的排序进行定向假设。结果通过三个主要发现表明了振幅估计的有效性（a）fNIRS 数据的 GLM 方法能够识别视觉皮层中刺激间隔为 4-9 秒（平均 6.5 秒）的人脑激活，而在非视觉区域没有检测到系统激活； (b) 不同的对比度强度导致 GLM 幅度参数的假设排序：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) 无效事件（无刺激）的分析没有在视觉皮层或其他大脑区域产生任何显着的激活。我们得出的结论是，基于模型的 GLM 方法可提供有效的 fNIRS 幅度估计，并能够快速分析与事件相关的 fNIRS 数据系列，这尤其与认知 fNIRS 研究高度相关。 (c) 2006 Elsevier Inc. 保留所有权利。
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 nonvisual 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. (c) 2006 Elsevier Inc. All rights reserved.