Pourmotahari Fatemeh, Tabatabaei Seyyed Mohammad, Borumandnia Nasrin, Khadembashi Naghmeh, Olazadeh Keyvan, Alavimajd Hamid
Clinical Research and Development Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Basic Clin Neurosci. 2024 Jan-Feb;15(1):61-72. doi: 10.32598/bcn.2021.3217.1. Epub 2024 Jan 1.
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.
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.
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).
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.
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以通过分析大脑不同区域之间的模式，将帕金森病患者的拓扑结构与健康个体区分开来。因此，本研究旨在使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名健康个体和11名帕金森病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。使用一种先进的非参数贝叶斯模型来评估拓扑特征，包括脑区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）方法检查两组之间基于每条边的功能关系的显著性。
大脑连接结果显示，患者组和健康组在每个聚类中的区域数量以及相关系数方面存在重大差异。患者组和对照组中最大的聚类分别为26个和53个区域，聚类相关值分别为0.36和0.26。尽管两个聚类中有15个共同区域，但两组中这些区域之间功能关系的强度不同。此外，使用NBS和FDR方法，未观察到患者组和健康组之间每条边存在显著差异（P>0.05）。
本研究结果显示患者组和健康组之间大脑网络的拓扑结构不同，表明大脑一组区域之间的功能关系发生了变化。
