Du Yue, Wang Guangyu, Wang Chengcheng, Zhang Yangyang, Xi Xiaoming, Zhang Limei, Liu Mingxia
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong, China.
School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, China.
Front Aging Neurosci. 2023 Feb 16;15:1101879. doi: 10.3389/fnagi.2023.1101879. eCollection 2023.
Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain.
To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules.
To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.
从功能磁共振成像（fMRI）数据估计的功能性脑网络（FBNs）已成为计算机辅助诊断神经疾病（如轻度认知障碍（MCI），阿尔茨海默病（AD）的前驱阶段）的一种潜在有用方法。目前，皮尔逊相关性（PC）是构建FBNs最广泛使用的方法。尽管其受欢迎且简单，但传统的基于PC的方法通常会导致密集网络，其中感兴趣区域（ROIs）紧密相连。这与大脑中ROIs可能稀疏连接的生物学先验不符。为了解决这个问题，先前的研究提出采用阈值或l_1正则化器来构建稀疏FBNs。然而，这些方法通常忽略了丰富的拓扑结构，例如模块化，而模块化已被证明是提高大脑信息处理能力的重要属性。
为此，在本文中，我们提出了一种精确模块诱导PC（AM-PC）模型，通过对网络的拉普拉斯矩阵施加稀疏和低秩约束来估计具有清晰模块化结构的FBNs。基于图拉普拉斯矩阵的零特征值表示连通分量的性质，该方法可以将拉普拉斯矩阵的秩降低到预定义的数量，并获得具有准确模块数量的FBNs。
为了验证所提方法的有效性，我们使用估计的FBNs将MCI患者与健康对照进行分类。对来自阿尔茨海默病神经成像倡议（ADNI）的143名静息态功能磁共振成像受试者的实验结果表明，所提方法比先前方法具有更好的分类性能。
