Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy.
Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy.
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DOI：
10.1117/1.nph.7.3.035008
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发表时间：
2020-07
期刊：
影响因子：
5.3
通讯作者：
Huppert TJ
中科院分区：
文献类型：
--
作者：
Zhai X;Santosa H;Huppert TJ
重要性：功能性近红外光谱（fNIRS）使用表面放置的光源和检测器来记录由于血红蛋白水平和氧合的波动而引起的大脑中的潜在变化。由于这些测量是从头皮表面记录的，因此从脑空间中的潜在感兴趣区域（ROI）到fNIRS通道空间测量的映射取决于传感器的配准、头部/大脑的解剖结构以及这些通过组织的扩散测量的灵敏度。然而，探头位置的微小位移可能会改变fNIRS测量中记录的大脑活动的分布。目的：我们提出了一种方法，使用个人或基于图谱的脑空间解剖信息来定义基于ROI的统计假设，以测试特定区域的无效参与，这使我们能够测试受试者之间的类似ROI，同时调整fNIRS探头位置和灵敏度差异，由于头部大小的变化，而无需定位器任务。方法：我们使用光学前向模型将潜在的脑空间ROI投影到锥形对比度向量中，该向量定义了对ROI有贡献的fNIRS通道的相对权重，并允许我们在功能任务期间测试该区域中没有大脑活动的零假设。我们证明了这种方法，通过模拟和比较的敏感性，特异性，这种方法与其他传统的方法。结果如下：我们研究这种方法的性能的情况下，头部尺寸和探头配准都是一个准确的已知参数，这是未知的实验误差。将该方法与使用364种不同模拟参数组合的常规方法的性能进行了比较。结论：在基于ROI的分析中始终推荐所提出的方法，因为无论fNIRS探针配准已知或未知，它都可以在没有定位器任务的情况下显着提高分析性能。
Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.