Dear all,

I uniformly parcellate the GM binary mask of size 61*73*61 into 6k parcels. Each parcel is a 2*2*2 cube.

So my feature is built on parcel-level for each subject.

Can I apply the paired t-test statistical pipeline in the DPABI software?

Or do I need to get back to voxel-level feature in order to use this pipeline?

Thanks!

Dear Labs ,

We have some questions of download from ADNI's data.

As we knew , FunRaw and T1Raw data file format must the same.

尊敬的严教授及团队成员，

Hello everyone!

I'm new to this topic and am struggling with the statistical analysis of my resting state data. Right now I'm testing the toolbox with only two of my subjects. When I try to add the FC-results to the Group images for a two-sample-t-test, it says [464] in front of the pathway (2*116 ROIs for the two subjects) instead of just [2] for the two subjects, as it was displayed in the tutorial. Do I need to organize the FC-results in any way, before I can use them?

Also, is it necessary to use covariate images for the analysis?

严老师您好：

我是学习这方面的新手，把nifit文件经过一系列预处理后选择AAL脑模板，希望得到116*116的功能连接矩阵。但处理后的文件是190（volumn数）*2233（ROI数）的矩阵，我很疑惑，不清楚设置是不是出错了。

这是我设置的面板：

Hello everyone,

I am using DPABI statistical analysis to run Mixed-Effect Linear Model.

These are my groups:

G1C1
G1C2
G2C1
G2C2

I’m not sure how to interpret the interaction effect? What does it represent? Is it the group difference in the change? If so, what is the contrast effects (i.e. change in G1 – change in G2?)

Regards,
Winson

各位老师，

         请问大家单样本检验用的是哪一个mask呢？我发现我用dparsf处理完之后的all sample mask打开是全黑的。我用了这个mask做alff的单样本检验做出来几乎全脑都是激活的，不知道是不是因为mask的原因，请大家指点一下，拜托了！

小欧

各位老师好，

         我想请问一下，如果是一个被试一个被试进行数据处理的话，要怎么做group mask呢？在进行单样本检验时需要用到的mask不知要使用哪一个。

Hello everyone,

I'm relatively new to analyzing resting-state data and have a problem with quality control. When I try to do the QC on Raw T1/Fun I get the Error message "Cannot find D:\ProcessingDemoData\QC\RawT1ImgQC.tsv". Can anyone help me out? I use Matlab R2019b and the Demo Data to learn how to use DPARSF.

Thanks in advance,

Julia