Investigation of the sensitivity-specificity of canonical- and deconvolution-based linear models in evoked functional near-infrared spectroscopy.
Investigation of the sensitivity-specificity of canonical- and deconvolution-based linear models in evoked functional near-infrared spectroscopy.
复制标题
研究诱发功能近红外光谱中基于规范和反卷积的线性模型的灵敏度特异性。
DOI：
10.1117/1.nph.6.2.025009
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发表时间：
2019
期刊：
影响因子：
5.3
通讯作者：
Huppert,TheodoreJ
中科院分区：
文献类型：
--
作者：
Santosa,Hendrik;Fishburn,Frank;Zhai,Xuetong;Huppert,TheodoreJ
功能性近红外光谱（fNIRS）是一种无创性脑成像技术，用于测量脑血氧的诱发变化。在许多诱发任务研究中，fNIRS实验的分析基于时间线性回归模型，其包括块平均、去卷积和典型分析模型。然后，该模型的统计参数在fNIRS测量通道上进行空间映射，以推断大脑活动。使用规范或去卷积/块平均模型的变化的灵敏度和特异性的权衡尚不清楚。我们定量研究如何选择基集的回归线性模型的影响fNIRS分析的灵敏度和特异性的变异性或系统性偏差的存在下，潜在的诱发反应。对于基于幅度假设的统计参数映射，我们得出结论，这些模型对任务持续时间>10 s的回归基础的参数相当不敏感，并且我们在这些条件下使用低自由度典型模型报告了最高的灵敏度-特异性结果。对于持续时间较短的任务（<10 s），数据的信噪比在这个决定中也很重要，我们发现反卷积或块平均模型在高信噪比下优于规范模型，但在较低水平下则不然。
Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique to measure evoked changes in cerebral blood oxygenation. In many evoked-task studies, the analysis of fNIRS experiments is based on a temporal linear regression model, which includes block-averaging, deconvolution, and canonical analysis models. The statistical parameters of this model are then spatially mapped across fNIRS measurement channels to infer brain activity. The trade-offs in sensitivity and specificity of using variations of canonical or deconvolution/block-average models are unclear. We quantitatively investigate how the choice of basis set for the regression linear model affects the sensitivity and specificity of fNIRS analysis in the presence of variability or systematic bias in underlying evoked response. For statistical parametric mapping of amplitude-based hypotheses, we conclude that these models are fairly insensitive to the parameters of the regression basis for task durations >10 s and we report the highest sensitivity-specificity results using a low degree-of-freedom canonical model under these conditions. For shorter duration task (<10 s), the signal-to-noise ratio of the data is also important in this decision and we find that deconvolution or block-averaging models outperform the canonical models at high signal-to-noise ratio but not at lower levels.