基于独立成分分析的精神分裂症患者动态脑功能网络分析新方法

发布时间：2018-04-19 06:00

本文选题：磁共振成像 + 动态脑功能网络　；参考：《中国医学影像技术》2015年06期

【摘要】：目的提出一种新的基于独立成分分析法进行动态脑功能网络分析的方法,并应用该方法探讨精神分裂症患者在动态全脑功能网络上的变异。方法首先基于滑动时间窗方法计算正常被试和精神分裂症患者的动态全脑功能网络,然后使用组信息指导独立成分分析方法,提取每个被试的动态全脑功能网络的功能连接状态及相应的时间波动,比较正常被试和精神分裂症患者在功能连接状态上的差异。结果两组的最重要功能连接状态的模式有相似性。正常被试在额叶、顶叶相关区域较精神分裂症患者具有更强的正功能连接;在小脑相关区域精神分裂症患者呈现出更多的正功能连接,而正常被试呈现出更多的负功能连接。结论组信息指导独立成分分析方法可有效提取动态脑功能网络的功能连接状态,可揭示精神分裂症患者在动态脑功能网络的变异。
[Abstract]:Objective to propose a new method for dynamic brain functional network analysis based on independent component analysis (ICA), and to explore the variation of dynamic global functional network in schizophrenic patients.Methods the dynamic global functional network of normal subjects and patients with schizophrenia was calculated based on the sliding time window method, and then the group information was used to guide the independent component analysis method.In order to compare the difference between normal subjects and schizophrenic patients, the functional connection state of dynamic global functional network and the corresponding time fluctuation were extracted from each subject.Results the patterns of the most important functional connections were similar between the two groups.The normal subjects had stronger positive functional connections in frontal and parietal lobe regions than in schizophrenic patients, and more positive functional connections in cerebellar related regions, and more negative functional connections in normal subjects.Conclusion the independent component analysis method guided by group information can effectively extract the functional connectivity of dynamic brain functional network and reveal the variation of dynamic brain functional network in schizophrenic patients.
【作者单位】：中北大学信息与通信工程学院;The
【基金】：国家自然科学基金(81471367) 中国科学院百人计划基金
【分类号】：R749.3;R445.2

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