PDF(Pubmed)
Abstract：
The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson\'s correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain\'s operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs\' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI\'s K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson\'s correlation (K-PC) has some advantage over deep learning.
摘要：
功能磁共振成像（fMRI）技术的采用和发展，特别是通过使用皮尔逊相关性(PC)构建脑功能网络(BFN)，通过揭示大脑的运作机制并提供早期检测的生物标志物，显著推进了脑疾病诊断。然而,PC总是倾向于制造密集的BFN，这违反了生物先验。因此,在实践中,研究人员使用硬阈值去除弱连接边或引入l1范数作为正则化项来获得稀疏BFN。然而,这些方法忽略了感兴趣区域(ROI)之间的空间邻域信息，距离较近的ROI比距离较远的ROI具有更高的连接前景，这是由于在最近的研究中简单的布线成本原则。因此，本文提出了一种邻域结构引导的BFN估计方法。详细来说,我们计算ROI的欧氏距离并对其进行排序。然后，我们应用K最近邻(KNN)来找出最接近当前ROI的前K个邻居，其中每个ROI的K个邻居彼此独立。我们建立了ROI与这K个邻居之间的连接关系，并根据二进制网络构造了全局拓扑邻接矩阵。使用边将ROI节点与k个最近邻连接以生成邻接图，形成邻接矩阵。基于邻接矩阵，PC计算由边连接的ROI之间的相关系数，并生成BFN。为了评估所介绍方法的性能，我们利用估计的BFN来区分患有轻度认知障碍(MCI)的个体和健康个体。实验结果表明，该方法比基线具有更好的分类性能。此外,我们将其与深度学习中最常用的时间序列方法进行了比较。K-最近邻-皮尔森相关(K-PC)性能的结果优于深度学习。
