DPABI/DPABISurf/DPARSF

严老师，好！

Hello，

我用自己的数据生成的AutoMasks显示如下，可以明显看出是有问题的。但是我用ProcessingDemoData按照同样的方式去做，显示是正常的，如下所示。我按照要求放入FunRaw和T1Raw,请问这是什么原因造成的呢？是我的数据有问题吗？有人能够帮助我吗？谢谢！

Hello

我用自己的数据生成的AutoMasks显示如下，可以明显看出是有问题的。但是我用ProcessingDemoData按照同样的方式去做，显示是正常的，如下所示。我按照要求放入FunRaw和T1Raw,请问这是什么原因造成的呢？是我的数据有问题吗？有人能够帮助一下我吗？谢谢！

Hi All,

I am Syida , also  a newbie here and very new for neuroimaging datat processing as I need to finish off my thesis and I think I need some helps in clarifying a few things.

1. I am told to  perform bias field correction for all the preprocessed fMRI data load- and I have done that for fMRI, no problem.

2. I have completed preprocessing procedures for resting state data by employing DPARSFA, that was done but questions are :a) do I need to do bias field correction for this data - of course mine is high chanelled (i think it is 32 channel)

Hi All,

I am Syida , also  a newbie here and very new for neuroimaging datat processing as I need to finish off my thesis and I think I need some helps in clarifying a few things.

1. I am told to  perform bias field correction for all the preprocessed fMRI data load- and I have done that for fMRI, no problem.

2. I have completed preprocessing procedures for resting state data by employing DPARSFA, that was done but questions are :

I'm a rooky and want get help for a basic problem. 

There are many ways to process the data preprocessing, which makes me a bit confused. Please help me check if my process is correct.

For Reho, the sequence is Slice Timing-Realign-reorient Fun-filter-normalize by epi-nuisance covariates regression-reho-smooth reho.

For alff and falff, the sequence is Slice Timing-Realign-reorient Fun-smooth-normalize by epi-nuisance covariates regression-alff+falff.

Thanks!

Hi all,

About the spatial normalization for ReHo analysis, I wonder which one is better, T1 image unified segmentation or EPI template, for the spatial normalization of ReHo? Cause my findings show significant differences between T1 and EPI template. Since the ReHo-KCC algorithm computes the neighborhood voxels, it is intuitively that simple spatial transformation should not have resulted in distinct patterns.

In the same vein, what template (EPI-MNI vs T1-MNI) should be the most optimal one for seed-based and graph-based functional connectivity analyses? Thank you~~