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基于功能磁共振成像的戒烟前后视觉诱导功能连接 被引量:2

Visual evoked functional connectivity before and after smoking cessation based on functional magnetic resonance imaging
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摘要 目的:利用功能磁共振成像技术观察吸烟成瘾者在戒烟前后视觉信息处理的差异。方法:17名吸烟成瘾者同意戒烟并完成了试验。利用独立分量分析方法分解出初级视觉网络和次级视觉网络。在两个网络内,戒烟前后存在显著差异的区域作为感兴趣区域,并对这些区域进行基于感兴趣区的功能连接分析。结果:戒烟14 d后在次级视觉网络内、两个网络之间的功能连接均有所增强,在初级视觉网络内的功能连接相对稳定。结论:吸烟成瘾者在戒烟14 d后视觉信息处理能力显著增强。 Objective: To explore differences in visual information processing for smoking addicts between before and after smoking cessation based on functional magnetic resonance imaging technology. Methods: Seventeen volunteers, with heavy dependence on nicotine, agreed to give up smoking and to complete the test. The primary visual network and secondary visual network were extracted by independent component analysis technology. The region of interest was defined as the region with significant difference between before and after smoking cessation. The functional connectivity analysis was carried out based on the selected region of interest. Results: After the subjects quitting smoking for 14 days, the increased functional connectivity was observed not only within secondary visual network but also between 2 interested networks; however, no functional connectivity was changed within primary visual network. Conclusion: The ability of visual information processing for smoking addicts could be significantly enhanced after 14 days of smoking cessation.
作者 莫少锋 张晓凯 陈洪波 汤井田 MO Shaofeng ZHANG Xiaokai CHEN Hongbo TANG Jingtianx(School of Geosci and Info-physics, Central South University, Changsha 410083 Tumor Hospital of Jilin Province Two Department of Thoracic Surgery, Changchun 130012 School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin Guang'xi 541004, China)
出处 《中南大学学报(医学版)》 CAS CSCD 北大核心 2017年第1期49-54,共6页 Journal of Central South University :Medical Science
基金 国家自然科学基金(81460273) 广西自然科学基金(2013GXNSFAA019325)~~
关键词 功能连接 戒烟 功能磁共振成像 独立分量分析 functional connectivity smoking cessation functional magnetic resonance imaging independent component analysis
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