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Assortativity changes in Alzheimer's diesease: A resting-state FMRI study

机译:阿尔茨海默氏病的分类变化:静态FMRI研究

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There is a growing trend toward using resting-state functional magnetic resonance imaging (rs-fMRI) data in studying brain network, and finding altered brain regions in neurological and psychiatric disorders. In this paper, we investigated the brain network of 15 normal and 15 Alzheimer subjects, using rs-fMRI data. To overcome the shortcomings of anatomical atlases in functional connectivity studies, we defined the regions based on functional atlases. We produced two functional parcellations: an individual parcellation for each subject separately, and a group-wise parcellation based on the whole dataset. For each subject, two functional graphs were constructed through these atlases. Finally, common network measures such as clustering coefficient and also assortativity coefficient were extracted from the resulted graphs. Comparison between corresponding network measures in patients and normal groups indicate that assortativity coefficient is significantly lower in both group-wise atlas-driven graphs (p-value = 0.0429), and individual atlas-driven graphs (p-value = 0.0334). Reduced assortativity coefficient, which might reveal disturbed primary order in vertices' degrees, can help in better distinguishing Alzheimer's subjects from normal ones.
机译:使用静息状态功能磁共振成像(rs-fMRI)数据来研究大脑网络,以及发现神经系统疾病和精神病患者的大脑区域变化的趋势正在日益增长。在本文中,我们使用rs-fMRI数据调查了15名正常人和15名阿尔茨海默氏症患者的大脑网络。为了克服功能连接研究中解剖图谱的缺点,我们基于功能图谱定义了区域。我们生成了两个功能分割:分别针对每个主题的单个分割,以及基于整个数据集的逐组分割。对于每个主题,通过这些地图集构建了两个功能图。最后,从结果图中提取出聚类系数和分类系数等常用网络度量。患者和正常组中相应网络测量值的比较表明,在两组图集驱动的图(p值= 0.0429)和单个图集驱动的图(p值= 0.0334)中,分类系数均显着较低。降低的分类系数可能揭示出顶点度受干扰的原始顺序,可以帮助更好地区分阿尔茨海默氏症的受试者和正常受试者。

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