Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Geroscience. 2024 Oct;46(5):5303-5320. doi: 10.1007/s11357-024-01128-w. Epub 2024 Mar 18.
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.
衰老是神经退行性变和痴呆的基础，影响着体内的每个组织。大脑的正常衰老与各种能力的逐渐减缓有关，如运动能力、认知障碍、信息处理速度、注意力和记忆力下降。随着全球老龄化的加剧，越来越多的研究关注老年人的大脑变化。图论与功能磁共振成像 (fMRI) 相结合，可以通过大脑建模来评估不同条件下大脑网络的功能连接模式。我们在人类连接组计划 (HCP) 的寿命试点数据中评估了三个不同年龄组（8 至 15 岁、25 至 35 岁和 45 至 75 岁）的大脑网络通信模型变化。最初，我们计算了基于 Pearson 相关的连接网络并进行了阈值处理。然后，通过计算全局和局部图度量值，比较了三个年龄组之间的网络特征。在静息状态大脑网络中，我们观察到随着年龄的增长，全局效率降低，转导性增加。此外，通过统计检验和机器学习方法，我们选择了杏仁核、壳核、海马体、楔前叶、下颞叶、前扣带回和中颞叶等大脑区域作为受年龄影响最大的大脑区域。使用 Fisher 得分和 Kruskal-Wallis 等特征选择方法，我们能够使用 SVM、KNN 和决策树分类器对三个年龄组进行分类。使用 Fisher 得分和决策树分类器组合得到的分类精度最高，为 82.2%。因此，通过使用图论检查功能连接的度量标准，我们将能够探索人类大脑正常的年龄相关变化，这可以用作随着年龄监测健康的工具。
