University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States.
Universite de Picardie Jules Verne, Department of Medicine, Amiens, France.
J Biomed Opt. 2017 May 1;22(5):55002. doi: 10.1117/1.JBO.22.5.055002.
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of red to near-infrared light to measure changes in cerebral blood oxygenation. Spontaneous (resting state) functional connectivity (sFC) has become a critical tool for cognitive neuroscience for understanding task-independent neural networks, revealing pertinent details differentiating healthy from disordered brain function, and discovering fluctuations in the synchronization of interacting individuals during hyperscanning paradigms. Two of the main challenges to sFC-NIRS analysis are (i) the slow temporal structure of both systemic physiology and the response of blood vessels, which introduces false spurious correlations, and (ii) motion-related artifacts that result from movement of the fNIRS sensors on the participants’ head and can introduce non-normal and heavy-tailed noise structures. In this work, we systematically examine the false-discovery rates of several time- and frequency-domain metrics of functional connectivity for characterizing sFC-NIRS. Specifically, we detail the modifications to the statistical models of these methods needed to avoid high levels of false-discovery related to these two sources of noise in fNIRS. We compare these analysis procedures using both simulated and experimental resting-state fNIRS data. Our proposed robust correlation method has better performance in terms of being more reliable to the noise outliers due to the motion artifacts.
功能近红外光谱（fNIRS）是一种非侵入性的神经影像学技术，使用低水平的红到近红外光来测量脑血氧变化。自发（静息状态）功能连接（sFC）已成为认知神经科学的重要工具，用于理解与任务无关的神经网络，揭示健康和紊乱大脑功能之间的差异，并在超扫描范式中发现相互作用个体的同步波动。sFC-NIRS 分析的两个主要挑战是（i）系统生理学和血管反应的缓慢时间结构，这会引入虚假的虚假相关性，以及（ii）源自 fNIRS 传感器在参与者头部移动的运动相关伪影，可引入非正态和重尾噪声结构。在这项工作中，我们系统地检查了几种用于描述 sFC-NIRS 的时频域功能连接度量的假发现率。具体来说，我们详细介绍了这些方法的统计模型的修改，以避免与 fNIRS 中这两种噪声源相关的高假发现率。我们使用模拟和实验静息状态 fNIRS 数据比较了这些分析过程。我们提出的稳健相关方法在抗噪离群值方面表现更好，因为它对运动伪影引起的噪声更可靠。
