■功能近红外光谱是一种光学脑监测技术,使用NIRS进行功能神经成像。它使用近红外光来测量大脑活动并估计由于运动活动而引起的大脑中的皮质血液动力学活动。功能性NIRS通过光学吸收测量氧合和脱氧血红蛋白中氧水平的变化。fNIRS信号分析中的主要挑战之一是由于来自多个源的噪声和伪影的干扰而导致的信号劣化。
■在这种情况下,本研究旨在利用图论分析大脑不同区域之间的连通性,从而分析大脑网络在功能参数方面的几何关联。在这项研究中,两个噪声去除过程(CBSI和TDDR)的影响,以及两种类型的相关fNIRS,如皮尔逊相关(PC),和互相关(CC)和各种全脑网络体系结构对单个参与者的图形测量的可重复性进行了仔细检查,以了解5%至50%的不同密度。使用测试-重测变异性(TRT)研究了图形在个体水平上的可重复性。
■在低密度下,二元网络中全局测量的重测可变性很大,不管噪声去除方法或相关性的种类。对于加权网络观察到非常低的测试重置值,并且对于整个图的测量结果具有很好的再现性。比较各种方法的重测值时,这种相关性,相关性的绝对值,权重计算方法对原始相关值均有显著的重大影响。
■基于以绝对互相关为权重的加权网络,这项研究表明,归一化的全局图测量是可靠的。用于去除噪声的节点定义技术对于可再现的归一化图度量不是必需的。
UNASSIGNED: Functional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption. One of the main challenges in the analysis of fNIRS signals is the signal degradation due to the interference from noise and artifacts from multiple sources.
UNASSIGNED: In this context, this research aims to analyze the connectivity between different regions of the brain using graph theory and hence the geometrical association of brain networks in terms of functional parameters. In this study, the impact of two noise removal processes (CBSI and TDDR), along with two types of correlation fNIRS such as Pearson\'s Correlation (PC), and Cross Correlation (CC) and various whole-brain network architectures on the reproducibility of graph measurements for individual participants has been carefully examined for different densities ranging from 5% to 50%.The graph measures\' repeatability at the individual level was studied using the test-retest variability (TRT).
UNASSIGNED: The test-retest variability for global measurements in binary networks was substantially large at low densities, regardless of the noise removal method or the kind of correlation. Very low test -reset values are observed for weighted networks and great reproducibility for measures of the entire graph. When comparing the test-retest values for various methods, the kind of correlation, the absolute value of the correlation, and the weight calculation method on the raw correlation value all had significant major effects.
UNASSIGNED: Based on a weighted network with the absolute cross correlation functioning as the weight, this study revealed that normalized global graph measurements were reliable. The node definition techniques that were utilized to remove noise were not essential for the normalized graph measures to be reproducible.