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右侧颞叶癫痫患者注意网络的静息态功能磁共振研究 被引量:2

ATTENTION NETWORK IN PATIENTS WITH RIGHT TEMPORAL LOBE EPILEPSY:A RSFMRI STUDY
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摘要 目的:采用静息态功能磁共振(rs-fMRI)研究右侧颞叶癫痫(rTLE)患者的注意网络,阐明其特点及可能的损害及代偿机制。方法:联合运用rs-fMRI中的独立成分分析(ICA)方法及注意网络测试(ANT)方法对19例rTLE患者(rTLE组)及18例健康对照(对照组)的注意网络进行分析,并比较两组的平均反应时间(RT),网络功能连接(FC)情况及RT与FC两者之间的相关性。结果:在ANT测试中,rTLE组RT较对照组延长(P<0.05)。rTLE组与对照组存在类似的注意网络,网络内右侧小脑后叶、枕中回、颞中回、中央前回的FC下降,左侧额叶内侧回、右侧顶下小叶的FC增强。此外,rTLE患者右侧枕中回、颞中回的FC值与RT呈负相关关系(r=-0.49,P<0.05)。结论:rTLE患者存在注意网络损害,注意网络内左侧额叶内侧回、右侧顶下小叶的FC增强,可能与注意网络受损后的代偿作用相关。 Objective To illuminate the characteristics and possible mechanism of the impairment and com-pensation of attention network in right temporal lobe epilepsy (rTLE) patients with resting-state functional MRI(rs-fMRI). Methods: 19 rTLE patients and 18 healthy controls were performed rs-fMRI scan and be-havior test. The attention network was measured by the extraction of ind(ICA) and the average response time (RT) of attention network test (ANT) in both groups. Results: In the behavior test,the rTLE patients presented longer RT than the controls ( 〈0. 05). The attentionnetworks were similar in both groups, and rTLE patients presented less functional connectivity (FC) in the right posterior lobe of cerebellum, middle occipital gyrus, middle temporal gyrus, precentral gyrus, and more FC in the left mediate frontal lobe,right inferior parietal lobule. In addition,the FC of right middloccipital gyrus,middle temporal gyrus is negatively correlated with the RT in patients (r=-0.49,P〈0. 05). Conclusion: Attention network impairment and compensation were found in patients with rTLE.
作者 李健萍 陈雪梅 钟丹 周慕华 郑金瓯 Li Jianping Chen Xuemei Zhong Dan Zhou Muhua Zheng Jinou.(Department of Neurology,the First Affiliated Hospital of Guangxi Medical University,Nanning 530021 , China)
出处 《广西医科大学学报》 CAS 2016年第5期779-782,共4页 Journal of Guangxi Medical University
基金 国家自然科学基金资助项目(No.81360202) 2015年广西研究生教育创新项目(No.YCSZ2015109)
关键词 颞叶癫痫 注意网络 静息态功能磁共振 独立成分分析 temporal lobe epilepsy attention network resting-state magnetic resonance imaging inde-pendent component analysis
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