The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ 07102, USA.
Neuroimage. 2024 Dec 1;303:120913. doi: 10.1016/j.neuroimage.2024.120913. Epub 2024 Nov 1.
Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.
近年来，大规模网络研究的进展表明，连接器枢纽和省级枢纽对于协调复杂认知任务至关重要，它们通过促进专门模块之间和内部的信息传递来实现这一目标。然而，目前用于识别这些枢纽的方法往往缺乏标准化的测量标准，从而阻碍了定量分析。本研究提出了一种利用多图理论指标计算的新的计算方法，用于定量分析脑网络中的枢纽属性。使用基准网络、随机模拟网络（N=100）、来自 ADHD-200NYU 数据集的静息 fMRI 数据（HC=110，ADHD=146）和北京数据集（HC=120，ADHD=83），我们引入了多标准定量图分析（MQGA）方法，该方法使用介数中心度、度数中心度和参与系数来确定连接器（con）枢纽指数和省级（pro）枢纽指数。通过对枢纽指数和标签的相关性分析、脆弱性测试和跨被试的一致性分析，验证了该方法的准确性、可靠性和稳定性。结果表明，随着网络稀疏度的增加，con 枢纽指数增加，而 pro 枢纽指数减少，最佳枢纽节点指数在 4%稀疏度。脆弱性测试表明，与去除 pro 节点相比，去除 con 节点对网络完整性的影响更大。con 和 pro 都在一致性分析中表现出稳定性，但 con 更稳定。疾病组的枢纽分数稳定性明显低于健康对照组。在楔前叶、中央后回和中央前回中发现了高 con 值，而在中央前回、中央后回、顶叶上回、楔前叶和颞上回中发现了高 pro 值。这种方法提高了枢纽节点识别的准确性和灵敏度，促进了精确比较，并产生了一致、可复制的结果，从而加深了我们对脑网络枢纽节点的理解，它们在认知过程中的作用，以及它们对脑疾病研究的意义。
