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~~
严老师好，最近在处理VMHC的过程中遇到了几个问题，希望您能解惑：
1.是不是因为要制作组内T1对称的模板，所以必须所有数据一起处理？如果因为内存问题，我能不能分批次处理，那么要从具体功能核结构的哪个文件夹一起保留呢？同时还要保留哪些文件夹？
2.因为要用到对称T1模板，所以健康人和患者的数据是不是要分开处理？然后组间分析的时候制作mask是要用健康和患者的对称T1mask一起取并集么？