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Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

机译:伴随函数近红外光谱和磁共振成像数据的主体和独立分量分析

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Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores
机译:虽然近红外光谱(NIRS)现在广泛使用,但在新兴的临床技术和认知神经科学中都被广泛使用,但是这些应用的设备和信号处理方法的开发仍然是一个热门的研究主题。功能NIR中的主要未解决的问题是通过系统性和局部生理波动来分离功能信号与污染物。使用各种信号处理方法接近该问题,包括盲信号分离技术。特别是,主成分分析(PCA),独立成分分析(ICA)施加于在相同的波长获取的数据,并在功能活化过程中在人或动物的头多个站点。这些信号处理程序导致许多主要或独立组分可归因于功能活动,但它们的生理学意义仍然是未知的。另一方面,宽带网德提供了最佳的生理特异性。此外,与功能磁共振成像(FMRI)的比较允许确定FNIR信号的空间起源。在这项研究中,我们将PCA和ICA应用于宽带网德网络数据以蒸馏与呼吸保持激活范例相关的组件,并将其与同时获取的FMRI信号进行比较。使用呼吸夹持,因为它产生血液二氧化碳(CO2),其增加血氧水平依赖性(粗体)信号,因为CO2充当脑血管扩张剂。血管抑制导致增加脑血流,从脑毛细管划出脑毛细血管床中的脱氧花血红蛋白,从而增加脑血容量和氧合。虽然原始信号是相当多样的,但我们发现了很少的不同组分,其对应于大脑中不同位置的FMRI信号和不同的生理发色团

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