June 2016

rest1.8 EXTRACT ROI SIGNAL and FC

    rest 里面的extract ROI Signal功能,我用自己弄的90个脑区作为ROI,最后得到的resultCorr.txt(90*90矩阵),是90个脑区的相关系数,接着去除了这90个脑区的协变量,再做了功能连接之后的ROI-WISE也得到FCMap.txt 或zFCMap.txt(90*90矩阵),这个也是90个脑区相关系数。

臧玉峰文章引用检索报告

编号:2016-NLC-LWCZ-1371
 
 
 
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检索课题:臧玉峰发表的学术论文被SCI和SSCI、ESI数据库收录引用情况
委托单位:杭州师范大学
委 托 人:臧玉峰
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 Science Citation Index Expanded (SCI-EXPANDED) 1900-pre
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如何分析三组被试ALFF在哪些脑区有差异并与行为学指标进行相关分析

(新手)我有三组数据要进行分析,下面是我尝试的分析处理过程,但是不知道对不对,求老师指导一下,谢谢!

利用rest首先进行的ANOVA,找到差异性显著的clusters,(不知如何选择ROI做mask,所以只是单纯的存为clusterA.txt)。然后进行two sample t-test,又出来一些差异性显著的clusters,3组每进行两两比较就得到3个这样的clusters,分别记为cluster1,2,3。然后我将clusterA中具体的cluster与cluster1,2,3中的进行比对,将能对应起来的看作是两两比较时有差异性的脑区。这样做对吗?如果不对的话,麻烦教一下正确的方法。

另外,在rest中如何将ALFF voxel转换成 ALFF value? 用ALFF与行为学指标做相关是需要ALFF value吗?不是的话,那又该怎么做呢?

ICA结果中表征组间差异问题

各位好:

         我研究的是中风病人和正常人ICA网络的组间差异问题,拿运动网络为例,我们发现病人组和正常人组的单样本图差异很大(前提是卡了同一个阈值),主要是涉及的脑区范围存在显著差异,正常人组单样本图比较正常,病人组的单样本图明显比正常人组小了好几圈,为了定量表征两组间这种差异,体素个数上的差异(或者说网络覆盖程度的差异),我具体的应该怎样统计呢?我的预想是将每个被试的运动网络的map的体素个数输出来,然后对正常人和病人进行双样本T检验,但是,问题是,我在想每个个体的map在计数前得先卡个阈值,可是卡多大呢?还有是在全脑内计数,还是在一个motor网络的mask内计数呢?而这个mask我选择什么呢?

        希望表达清楚了,望老师能帮菜鸟指点,万分感谢!

Compatibility with MATLAB 2015

Hi,

I am trying to use this toolbox for RSFC and I have MATLAB2015b installed on my LINUX computer. It looks like the current version of the toolbox is not compatible with MATLAB 2015b. Whenever I click something on GUI of toolbox, it keeps on giving me errors.

I would really appreciate any help.

Thanks,

Sahil

Multiple Comparison Problem ROI to ROI analysis using anatomical atlas

Hi all,

 

I am conducting a resting state functional connectivity analysis and I am using a self defined seed region and want to look at the functional connectivity to the ROIs based on the Harvard Oxford Atlas. I was wondering how to deal with the multiple comparison problem. Is the functional connectivity between my seed region and each of the ROIs from the template computed sequentially or simultaneously? If I understand this process directly than this would have big implications for the multiple comparison problem.

 

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