DPARSFA processing sequence

Submitted by tomchanglai on Mon, 01/11/2016 - 06:44

To whom it may concern,

We are testing the effects of different processing options on ReHo reliability and we would appreciate if one could help us clarify a few things:

1. Does it make a difference whether we perform 'Nuisance Covariates Regression' or 'Normalize' on our data set first? Also, is there a perferred way/reason why one should be perfroemd before the other?

2. In the GUI, there are two places where we can perform 'Nuisance Coavariates Regression' and 'Filter'. Can we assume that the same processing option will be run, just the order by which it is run is different?

Thank you.

 

Forums:

YAN Chao-Gan

Mon, 01/11/2016 - 09:00

Hi Tom,
1. I prefer to removing the impact of nuisance covariates as early as possible, thus I put nuisance covariates regression before spatial normalization. You can test the difference between the two strategies.

2. They are the same, but runs in the different orders. Just keep in mind, nuisance regression before spatial normalization means extracting nuisance signals in the native space. Nuisance regression after spatial normalization means extracting nuisance signals at the MNI space.

Best,

Chao-Gan


tomchanglai

Tue, 01/12/2016 - 06:50

Dear Dr. Yan,

Thank you very much for the quick response. We finished the ReHo calculation and are now running the Test-Retest Reliability: ICC. We were able to complete the ANOVA method under 1 minute; however, when we ran the ReML Model, MATLAB reads the image files successfully, but then no further text appears on the command line. It appears to be running (i.e. we are unable to exit Matlab), however we have left the analysis like this for an hour or two without any further progress. We are unsure if the ICC calculation is stuck, or whether it requires a long processing time. Any help would be greatly appreciated.

Thank you.

YAN Chao-Gan

Wed, 01/13/2016 - 09:05

Hi,

The ReML comsumes very long time. You can give it a try overnight.

Best,

Chao-Gan

于可可

Wed, 01/20/2016 - 07:20