Back to PLS HelpPosted on 12/02/15 08:24:06
Number of posts: 14
Hello,
I have resting state fMRI data (200 volumes for each subject) and am interested in correlating it to EEG data (200 variables corresponding to 200 volumes of fMRI data for each subject).
Is it possible to perform this analyses using the blocked or E.R fMRI options provided in PLS software? While creating the datamat files, it is mandetory to enter the event onsets. But there are no events in resting state analyses. Is there a work around for this situation?
Best Wishes
Jay
Replies:
Posted on 12/02/15 08:38:28
Number of posts: 394
can you give us more info on the EEG data? Is it spectral power? and what aspect of the fMRI signal do you want to correlate with the EEG data?
Posted on 12/03/15 10:09:41
Number of posts: 14
Posted on 12/03/15 11:12:17
Number of posts: 394
Hi Jay - the for the resting state you can use the same approach that Omar Grigg did in:
Grigg O, Grady CL (2010) Task-Related Effects on the Temporal and Spatial Dynamics of Resting-State Functional Connectivity in the Default
Network. PLoS ONE 5(10): e13311. doi:10.1371/journal.pone.00133
using the block design module of PLS
Posted on 12/15/15 06:06:26
Number of posts: 1
Hi Randy, the paper by Grigg et al is very interesting, but the method doesn't seem quite intuitive. In particular, if all the blocks (consisting of 5 volumes condensed to one) go into the permutation and bootstrapping, won't they be treated as individual units? It seems that what we really want is for each full subject to be treated individually?
Really, the situation with continuous regressors for resting state data seems to be very similar to any regular fMRI situation. E.g. in event-related fMRI the onsets and durations are only a way to define the continuous regressors corresponding to each condition.
So, if you already have such continuous regressors, is there a way to 'intercept' the fMRI pipeline and enter the regressors directly into the design matrix? This would seem more intuitive, and would ensure that subjects are treated individually, just as in the other types of fMRI analysis.
Best
Egill
Posted on 12/16/15 04:01:49
Number of posts: 394
Hi Randy, the paper by Grigg et al is very interesting, but the method doesn't seem quite intuitive. In particular, if all the blocks (consisting of 5 volumes condensed to one) go into the permutation and bootstrapping, won't they be treated as individual units? It seems that what we really want is for each full subject to be treated individually?
Really, the situation with continuous regressors for resting state data seems to be very similar to any regular fMRI situation. E.g. in event-related fMRI the onsets and durations are only a way to define the continuous regressors corresponding to each condition.
So, if you already have such continuous regressors, is there a way to 'intercept' the fMRI pipeline and enter the regressors directly into the design matrix? This would seem more intuitive, and would ensure that subjects are treated individually, just as in the other types of fMRI analysis.
Best
Egill
Hi Egill
At this point, there is no easy way to do what you want withouth having to write a custom script. As you can imagine, there a plethora of questions that relate to resting-state networks and we have not yet updated the PLS code to accommodate all of these. If you are adept in MATLAB you can create a script or modify ours create the datamats you need and then use the command line PLS to run the stats. This is probably the easiest and quickest solution.
cheers
Randy
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