利用非参数贝叶斯模型对帕金森病患者大脑连接网络的研究

Fatemeh Pourmotahari, Seyyed Mohammad Tabatabaei, Nasrin Borumandnia, Naghmeh Khadembashi, Keyvan Olazadeh, Hamid Alavimajd
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引用次数: 0

摘要

导言帕金森病是一种破坏大脑功能网络的神经退行性疾病。许多神经退行性疾病都与大脑通信模式的变化有关。静息态功能连接研究可通过分析大脑不同区域之间的模式来区分帕金森病患者和健康人的拓扑结构。因此,本研究旨在利用贝叶斯方法确定帕金森病患者的大脑拓扑特征和功能连接:本研究的数据从开放神经网站下载。这些数据包括 11 名健康人和 11 名帕金森患者的静息态功能磁共振成像(rs-fMRI),他们的平均年龄分别为 64.36 岁和 63.73 岁。研究采用了先进的非参数贝叶斯模型来评估拓扑特征,包括大脑区域的聚类和聚类的相关系数。通过错误发现率(FDR)和基于网络的统计(NBS)方法检验了两组之间基于各边缘的功能关系的重要性:结果:大脑连通性结果显示,患者组和健康组在每个簇的区域数量和相关系数方面存在很大差异。患者组和对照组最大的聚类分别为 26 个和 53 个区域,聚类相关系数分别为 0.36 和 0.26。虽然两个聚类中有 15 个共同区域,但这些区域之间的功能关系强度在两组中是不同的。此外,使用 NBS 和 FDR 方法观察,患者组和健康组的各边缘均无显著差异(P>0.05):本研究结果显示,患者组和健康组的大脑网络拓扑结构不同,表明大脑一组区域之间的功能关系发生了变化。
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A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model.

Introduction: Parkinson disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson disease, using a Bayesian approach.

Methods: The data of this study were downloaded from the open neuro site. These data include resting-state functional magnetic resonance imaging (rs-fMRI) of 11 healthy individuals and 11 Parkinson patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods.

Results: Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (P>0.05).

Conclusion: The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

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