慢性期皮层下脑梗死患者网络内和网络间功能连接异常

发布时间：2018-03-16 00:19

  本文选题：脑网络　切入点：功能磁共振　出处：《天津医科大学》2014年硕士论文　论文类型：学位论文

【摘要】：目的： 采用独立成分分析(independent component analysis, ICA)法,探索静息态下运动功能恢复良好的慢性期皮层下脑梗死患者网络内和网络间功能连接(functional connectivity, FC)改变。 材料与方法： 选择运动功能恢复良好的慢性期单发脑梗死患者25例(7女,18男),病灶位于内囊及其邻近区域。另外,选择22名(11女,11男)年龄相匹配的健康志愿者为对照组。采用GE Signa HD-X3.0T磁共振扫描仪进行3D高分辨率T1WI解剖像和静息态fMRI扫描。 1.采用基于Matlab的SPM8和DPARSF对静息态fMRI数据进行预处理,其过程包括：时间校正、头动校正、空间标准化、重采样至3×3×3mm3的立方体素及空间平滑(6×6×6mm3)。 2.采用组ICA功能磁共振工具包来提取静息态脑网络(resting-state network, RSNs),得到11个RSNs。控制年龄、性别后比较患者组与正常对照组之间每个RSN功能连接强度的差异。提取出网络内功能连接有显著性差异的脑区作为感兴趣区(region of interest, ROI),然后运用一般线性模型(将年龄与性别作为协变量)进行组间比较。同时运用Monte Carlo模拟进行多重比较校正。 3.对于网络间功能连接的分析,其预处理过程在ICA预处理基础上进一步处理,包括滤波和去除协变量。首先,计算每一对RSNs个体平均时间序列的Pearson相关系数,然后进行Fisher's Z专换。最后将所有受试者的年龄、性别作为协变量,对具有统计学意义的网络间功能连接进行组间比较。 4.将患者组网络内和网络间功能连接值分别与病灶体积大小做偏相关,被试的年龄、性别为协变量,统计阈值为P0.05。 5.未翻转数据组与翻转数据组预处理及统计分析过程一样,两次ICA运算得到两套RSNs。 结果： 1.11个RSNs包括：腹侧感觉运动网络(vSMN)、背侧感觉运动网络(dSMN)、枕极视觉网络(pVN)、内侧视觉网络(mVN)、外侧视觉网络(1VN)、听觉网络(AN)、背侧注意网络(DAN)、前默认网络(aDMN)、后默认网络(pDMN)、左额顶网络(1FPN)和右额顶网络(rFPN)。 2.与正常对照组比较,脑梗死患者的感觉运动网络、视觉网络、听觉网络、背侧注意网络和默认网络功能连接增加。而且,还发现了患者功能连接减低的网络,包括额顶网络和前默认网络。 3.脑梗死患者在VN-AN之间和VN-SMN之间从正常对照组的无功能连接转变为显著的负功能连接。而且与正常对照组比较,脑梗死患者在pDMN-rFPN之间和aDMN-pDMN之间呈正功能连接减低的改变,在AN-rFPN之间、pDMN-dSMN之间呈负功能连接减低的改变。 4.偏相关分析结果显示,病灶体积与脑梗死患者网络内、网络间功能连接值之间均没有显著性相关(P0.05),表明病灶大小对本研究结果没有显著影响。 结论： 1.慢性期皮层下脑梗死患者存在多个脑功能网络的改变,而且功能网络重组与损伤共存； 2.慢性期皮层下脑梗死患者不仅网络内功能连接发生改变,网络间功能连接也发生一定的改变； 3.从大尺度脑网络的角度分析患者脑功能的改变,增加对皮层下脑梗死患者复杂临床症状的理解。
[Abstract]:Objective:
Independent component analysis (ICA) was used to explore the changes of functional connectivity (functional connectivity, FC) in patients with chronic subcortical cerebral infarction who had good motor function recovery in resting state.
Materials and methods:
Select the recovery of motor function in chronic period good single cerebral infarction 25 cases (7 female, 18 male), was located in the internal capsule and its adjacent areas. In addition, 22 patients (11 female, 11 male) and age-matched healthy volunteers as control group. Using GE Signa HD-X3.0T magnetic resonance scanner for high resolution 3D T1WI anatomy as a resting state fMRI scan.
1., we use Matlab based SPM8 and DPARSF to preprocess resting state fMRI data. The process includes: time correction, head correction, spatial standardization, resample to cube 3 and 3 x 3mm3, and spatial smoothing (6 * 6 * 6mm3).
The 2. group ICA with functional magnetic resonance imaging kit to extract restingstate networks (resting-state network, RSNs), 11 RSNs. after controlling for age, gender differences in each function were compared between RSN group and normal control group. The connection strength of the extracted network functional connectivity in brain regions has a significant difference of interest area (region of interest, ROI), and then using the general linear model (the age and gender as a covariate) were compared between the two groups. At the same time using the Monte Carlo simulation of correction for multiple comparisons.
3. for network functional connectivity analysis, the pretreatment process for further processing based on ICA pre processing, including filtering and removal of covariates. Firstly, the Pearson correlation coefficient to calculate the average time series of each individual RSNs, then Fisher's Z designed for. In the end, all the subjects were age, gender as co the statistically significant variables, the network connection between functional group were compared.
4. in the patient group, the value of the function connection between the network and the network is partial to the size of the lesion, the age of the subjects, the gender as the covariate, the statistical threshold is P0.05.
The 5. unflipped data group is the same as the preprocessing and statistical analysis of the overturned data group, and the two ICA operation gets two sets of RSNs.
Result锛，

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