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

机译:功能性近红外光谱和磁共振成像数据的主成分和独立成分分析

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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:尽管近红外光谱法(NIRS)现在已广泛用于新兴的临床技术和认知神经科学中,但针对这些应用的设备和信号处理方法的开发仍然是热门的研究主题。功能性NIRS的主要未解决问题是通过全身和局部生理波动将功能性信号与污染物分离。通过使用包括盲信号分离技术在内的各种信号处理方法来解决此问题。特别是,在功能激活期间,将主成分分析(PCA)和独立成分分析(ICA)应用于在相同波长下在人或动物头部的多个波长处获取的数据。这些信号处理程序导致许多主要或独立的成分,可以归因于功能活动,但其生理意义仍然未知。另一方面,宽带NIRS提供了最佳的生理特异性。而且,与功能磁共振成像(fMRI)的比较允许确定fNIRS信号的空间起源。在这项研究中,我们将PCA和ICA应用于宽带NIRS数据,以提取与屏气激活范例相关的成分,并将其与同时获取的fMRI信号进行比较。使用屏住呼吸是因为它会产生血液中的二氧化碳(CO2),由于CO2充当脑血管扩张剂,因此会增加血氧水平依赖性(BOLD)信号。血管舒张引起脑血流量的增加,其将脱氧血红蛋白从脑毛细血管床中洗出,从而增加了脑血量和氧合。尽管原始信号非常多样,但我们发现很少有不同的成分对应于大脑不同部位的fMRI信号以及不同的生理生色团。

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