衰老是神经变性和痴呆的基础,影响身体中的每个地方病。大脑的正常老化与各种能力的进行性减速和中断有关,例如运动能力,认知障碍,降低信息处理速度,注意,和记忆。随着全球老龄化的加剧,更多的研究集中在老年人的大脑变化。图论,结合功能磁共振成像(fMRI),这使得通过脑建模评估不同条件下的脑网络功能连接模式成为可能。我们评估了三个不同年龄段(包括8至15岁,25到35年,和45至75岁)来自人类连接体项目(HCP)的寿命试验数据。最初,计算了基于皮尔逊相关性的连接网络并对其进行了阈值化。然后,通过计算全局和局部图形度量,比较了三个年龄组的网络特征。在静息状态的大脑网络中,我们观察到随着年龄的增长,全球效率降低,传递性增加。此外,大脑区域,包括杏仁核,壳核,海马体,precuneus,颞下回,前扣带回,和颞中回,通过统计测试和机器学习方法,被选作随年龄增长受影响最大的大脑区域。使用特征选择方法,包括费舍尔得分和克鲁斯卡尔-沃利斯,我们能够使用SVM对三个年龄组进行分类,KNN,和决策树分类器。最佳分类精度是结合Fisher评分和决策树分类器获得的,为82.2%。因此,通过使用图论检查功能连通性的度量,我们将能够探索人类大脑中与年龄相关的正常变化,它可以用作随年龄增长监测健康的工具。
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.