基于模糊近似熵的抑郁症患者静息态功能磁共振成像信号复杂度分析

发布时间：2018-05-03 12:07

本文选题：血氧水平依赖功能磁共振成像信号 + 模糊近似熵　；参考：《物理学报》2016年21期

【摘要】：提出采用模糊近似熵的方法对功能磁共振成像(functional magnetic resonance imaging,fMRI)复杂度量化分析,并与样本熵进行比较.采用的22个成年抑郁症患者中,11位男性,年龄在18—65岁之间.我们期望测量的静息态fMRI信号复杂度与Goldberger/Lipsitz模型一致,越健康、越稳健其生理表现的复杂度越大,且复杂度随年龄的增大而降低.全脑平均模糊近似熵与年龄之间差异性显著(r=-0.512,p0.001).相比之下,样本熵与年龄之间差异性不显著(r=-0.102,p=0.482).模糊近似熵同样与年龄相关脑区(额叶、顶叶、边缘系统、颞叶、小脑顶叶)之间差异性显著(p0.05),样本熵与年龄相关脑区之间差异性不显著性.这些结果与Goldberger/Lipsitz模型一致,说明采用模糊近似熵分析fMRI数据复杂度是一个有效的新方法.
[Abstract]:A fuzzy approximate entropy method is proposed to quantify the complexity of functional magnetic resonance imagingfMRI in functional magnetic resonance imaging (fMRI), and to compare it with the sample entropy. Of the 22 adults with depression, 11 were men aged 18-65 years. We expect the measured fMRI signal complexity to be consistent with that of the Goldberger/Lipsitz model. The healthier the measurement is, the more robust the complexity of physiological performance is, and the lower the complexity is with the increase of age. The difference between the average fuzzy approximate entropy and the age of the whole brain is significant. In contrast, there was no significant difference between sample entropy and age. The difference between fuzzy approximate entropy and age-related brain regions (frontal lobe, parietal lobe, marginal system, temporal lobe, cerebellar parietal lobe) was significant (p 0.05), but there was no significant difference between sample entropy and age-related brain area. These results are consistent with the Goldberger/Lipsitz model, which shows that the fuzzy approximate entropy is an effective new method to analyze the complexity of fMRI data.
【作者单位】：北京工业大学国际WIC研究院;前桥工业大学生命科学与信息工程系;首都医科大学安定医院;
【基金】：国家重点基础研究发展计划(批准号:2014CB744600) 国家自然科学基金(批准号:61272345,61105118)资助的课题~~
【分类号】：R445.2;R749.4

【相似文献】