本文選題：靜息態(tài)功能磁共振　切入點：原發(fā)性失眠　出處：《中山大學學報(醫(yī)學科學版)》2017年03期

【摘要】：【目的】基于靜息態(tài)功能磁共振成像技術(fMRI),利用度中心度(DC)分析方法探討原發(fā)性失眠(PI)患者靜息態(tài)下腦神經(jīng)網(wǎng)絡功能連接的異常屬性,找尋原發(fā)性失眠相關神經(jīng)機制的影像學證據(jù)�！痉椒ā繉�59例PI患者及47例年齡、性別、受教育程度均匹配的健康對照組進行靜息狀態(tài)下的功能磁共振成像掃描,經(jīng)預處理和后處理計算出全腦DC值圖,采用雙樣本t檢驗進行組間比較,分別檢測PI組與NC組間DC分布圖間的統(tǒng)計學差異�！窘Y果】對比正常對照組,PI患者DC值明顯降低的腦區(qū)主要包括雙側額中回(MFG)、雙側前扣帶回(ACG)及右側腦島(Insula)等區(qū)域。DC值明顯增加的腦區(qū)主要包括右側顳葉中回(MTG)及左側楔葉(CUN)等腦區(qū)(P0.05)�！窘Y論】基于靜息態(tài)功能MRI數(shù)據(jù)的DC分析觀察到PI患者在腦區(qū)間功能連接改變,DC作為一種新的基于體素水平描述腦網(wǎng)絡屬性的方法,可能對理解PI神經(jīng)機制及觀察其腦網(wǎng)絡屬性的改變提供了一個新視角。
[Abstract]:[objective] based on the resting functional magnetic resonance imaging (fMRI) technique and using the degree of centrality (DCV) analysis method, the abnormal properties of functional connections of neural networks in patients with primary insomnia were studied. To find the imaging evidence of neural mechanism related to primary insomnia. [methods] 59 patients with Pi and 47 healthy controls matched with age, sex and education were examined by functional magnetic resonance imaging (fMRI). After preprocessing and post-processing, the whole brain DC value map was calculated, and the comparison between groups was carried out by double sample t-test. The difference of DC distribution between Pi group and NC group was detected respectively. [results] compared with the normal control group, the decrease of DC value in patients with Pi mainly included bilateral middle frontal gyrus MFGG, bilateral anterior cingulate gyrus (ACGG) and right islet Insula). The significant increase in DC value mainly includes the right temporal lobe middle gyrus (MTG) and left cuneiform (CUN). [conclusion] DC analysis based on resting functional MRI data shows that the change of functional junctions in Pi patients is a new method. A method for describing brain network attributes based on voxel level, It may provide a new perspective for understanding Pi neural mechanism and observing the changes of brain network properties.
【作者單位】： 廣東省第二人民醫(yī)院;
【基金】：廣州市科技計劃立項項目(201607010056)
【分類號】：R740

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