Zhou Yikun, Gao Shuang, Deng Lingli, Lin Genjin, Dong Jiyang
Institute of Artificial Intelligence, Xiamen University, Xiamen, 361005, China.
Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
Sci Rep. 2025 Mar 29;15(1):10888. doi: 10.1038/s41598-025-95190-9.
Network modeling are widely using in resting-state functional magnetic resonance imaging (rs-fMRI) for Alzheimer's disease (AD) research. Typically, Pearson correlation coefficient (PCC) was widely applied to construct brain connectivity network from BOLD signals of regions of interest. However, it often results in significant intra-group variability and complicates the identification of disease-specific functional connectivity patterns. To address this issue, we propose a novel brain network construction strategy, called SNBG, which uses aggregated information from the control group to derive a single-sample network. We compare SNBG and the PCC based method on a dataset from an Alzheimer's Disease Neuroimaging Initiative (ADNI) study. SNBG method captures more stable connections between regions of interest (ROIs) and increases classification accuracy from 89.24% of PCC based method to 97.13%. In addition, in AD-related local networks, such as default mode network (DMN), medial frontal network (MFN) and frontoparietal network (FPN), SNBG demonstrates lower intra-group heterogeneity than the PCC based method.
网络建模在静息态功能磁共振成像（rs-fMRI）用于阿尔茨海默病（AD）研究中被广泛应用。通常，皮尔逊相关系数（PCC）被广泛用于从感兴趣区域的血氧水平依赖（BOLD）信号构建脑连接网络。然而，它常常导致显著的组内变异性，并使疾病特异性功能连接模式的识别变得复杂。为了解决这个问题，我们提出了一种新颖的脑网络构建策略，称为SNBG，它使用来自对照组的聚合信息来推导单样本网络。我们在一项来自阿尔茨海默病神经影像倡议（ADNI）研究的数据集上比较了SNBG和基于PCC的方法。SNBG方法捕获了感兴趣区域（ROI）之间更稳定的连接，并将分类准确率从基于PCC方法的89.24%提高到了97.13%。此外，在与AD相关的局部网络中，如默认模式网络（DMN）、内侧额叶网络（MFN）和额顶网络（FPN），SNBG比基于PCC的方法表现出更低的组内异质性。
