Dear DPABI team,
first, I would like to thank you for your work and effort – I enjoy working with DPABI. I hope I didn't miss or overread information in this forum about the questions I'll ask in the following, if so I would like to apologize beforehand.
I'm working on resting-state data and I'm wondering if I'm entering a 0 in the TR box and have the TRInfo.tsv file (in that a TR of one participant is different to the TR in this persons' nifti header - I changed it to the correct one in TRInfo.tsv) in my data folder with all correct TR information, do I understand correctly from the DPARSFA_run script that DPARSFA is only reading the TR information from the TRInfo.tsv file, or, for example for fALFF calculation, does it take the information explicitely from the nifti header? So if there is an existing TRInfo.tsv file with correct information (that might be different from the information in the header, because after creating the file adapted), does DPARSFA read the information only from this TRInfo.tsv file?
Furthermore, after reading in this forum as well as the literature (I hope I did not miss important information), I am wondering if calculating a within- subject annual percentage signal change (from a ROI between two timepoints) (fALFF, ReHo and DC using DPARSFA) is most suitable on raw maps compared to standardized maps (m or z) – after my knowledge the standardization will change the interpretation and standardization has its advantages, but at the same time the values can become very big after standardization and that might be a problem. Maybe you have an advice or further important points regarding relative signal change and used maps?
Thank you very much in advance!
I wish you a nice day!
Best,
Marthe
1. If there is TRInfo.tsv,
1. If there is TRInfo.tsv, then DPARSF will ignore the TR info in the NIFTI header.
2. I will use raw. However, if you think the global mean ALFF need be controlled for the two times, then zALFF is also an option.
Thanks a lot for your very
Thanks a lot for your very helpful answer!
Thanks a lot for your very
Thanks a lot for your very helpful answer!