Point process analysis in brain networks of patients with diabetes
Point process analysis in brain networks of patients with diabetes
复制标题
糖尿病患者脑网络点过程分析
DOI：
10.1016/j.neucom.2014.05.045
复制
发表时间：
2014-12-05
期刊：
影响因子：
6
通讯作者：
Dai, Hui
中科院分区：
文献类型：
--
作者：
Li, Wei;Li, Yapeng;Dai, Hui
噪声和个体差异来自于静息态功能磁共振图像（fMRI）数据集有效使用的干扰。在这项研究中，点过程被用来处理健康对照组和糖尿病患者的fMRI数据集，然后，使用两组BOLD信号建立受试者的功能脑网络。结果表明，正常人与患者的点过程信号的差异比非点过程信号的差异更明显。我们的研究结果还表明，有一个较高的识别精度的信号通过预处理与点过程。这些发现可能表明，点处理方法可以降低BOLD信号噪声，为功能磁共振数据预处理提供了一种新的方法，并可能为计算机辅助疾病诊断中的早期数据预处理提供一种有前途的方法。(C)2014爱思唯尔有限公司版权所有。
Noise and individual differences arise from disturbances in the effective use of resting-state functional magnetic resonance image (fMRI) datasets. In this study, the point process is used to treat fMRI datasets of healthy controls and patients with diabetes; then, functional brain networks of subjects are established using two sets of BOLD signals. The results illustrate that differences between the healthy controls and the patients were more obvious in point process signals than nonpoint process signals. Our results also suggest that there is a higher recognition accuracy of the signals by preprocessing with the point process. These findings may suggest that the point process approach can reduce BOLD signals noise, providing a new method for functional magnetic resonance data preprocessing, and may provide a promising method for early data preprocessing in computer-aided disease diagnostics. (C) 2014 Elsevier B.V. All rights reserved.