Dear Conn toolbox experts,

I'm interested in task-related connectivity, and would like to test the connectivity associated with a parametric modulator in my design matrix. However, when I set up my analysis in the Conn toolbox, I see my conditions (onset regressors) but not the parametric modulator associated with each condition. Is it possible to include the parametric modulator as a condition? Does it even make sense within the context of the Conn analysis?

Ta,

Rich

Dear Rich

Unfortunately the toolbox does not allow you to explore task-related connectivity when the task is characterized by a continuous variable (parametric modulators). I would suggest to discretize if possible your modulator variable (convert it to a fixed set of levels) and then enter those as different conditions (conn will allow you then to explore task-related connectivity across these different 'conditions' / levels of your parametric modulator). Alternatively perhaps you might also want to consider using SPM's PPI analyses to explore this form of continuous task interactions. 

Best
Alfonso
Originally posted by Richard Morris:

Dear Alfonso,

I am uncertain if this comment also applies to v15.a of conn.
I have a 1st level design in SPM with a time modulator (ordinal values 1-n of trials) and a modulator by distance to target (continuous values 0-1).
Unfortunately, they don't show when I load my SPMs to conn. Under covariate, conn recognizes only the motion parameters which it calls "SPM covariates".
Am I doing something wrong?

Thank you for your help.
Best,
E

Dear Ewa,

Yes, in the latest releases of CONN you may actually perform those PPI-style analyses in CONN directly. To do that you would need to create a series of first-level covariate files (.txt or .mat files) containing your modulator variables and enter those as additional first-level covariates in Setup.CovariatesFirstLevel. Then, in the first-level anlaysis tab you may select the analysis type 'other temporal modulation' effects, and select there each of these covariates, and that will perform a PPI analyses looking at the connectivity modulation associated with your time or distance-to-target variables (i.e. those areas where functional connectivity strength covaries with time or distance-to-target). Unfortunately the 'import' functionality will not automatically read the information of your modulatory variables from your SPM.mat files so you would instead need to manually/progammatically create those first-level covariate files for each subject/session (I will try to add the functionality to automatically import into CONN that info from SPM-defined task-modulation variables to the next release).

Best
Alfonso
 
Originally posted by Ewa Miendlarzewska:

what are the format restrictions for the text/.mat file?  can there be as many time points as there are trials summed across conditions?

Hi Shady,

Those timeseries are expected to contain as many time-points as samples/acquisitions for each subject/session (e.g. just the same format as the rp_*.txt realignment parameter files, or the art_regression_outliers*.mat motion/outliers files)

Hope this helps
Alfonso
Originally posted by Shady El Damaty:

Hi Alfonso, many thanks for this info. I had a follow up question about the parametric modulator analysis. In my spm.mat file I have two event-related conditions A and B, each of which has a trialwise parametric modulator that codes continuous ratings of confidence. For each condition I extracted the estimated parametric regressor column from the SPM.xX.X and entered it as a first-level covariate, resulting in two additional covariates (param_conf_A, param_conf_B) along with the standard 'SPM covariates'. If the goal is to look at connectivity differences between the confidence-modulated A and B conditions (and if the setup sounds OK up to this point), I was wondering 1) does it make sense to omit the parametric modulator covariates from the Confounds list in the denoising step, or should they be kept in? 2) is it necessary to run the first-level analysis twice with a different modulator selected under 'other temporal-modulation effects' for each. It seems like only one interaction factor can be selected. I was also trying to determine which option under first-level 'Analysis options' makes most sense. Thanks,

erik

Hi Erik,

If you already have those parametric-modulation timeseries as first-level covariates "param_conf_A" and "param_conf_B" (and I guess you probably also have the original conditions onsets/durations entered as conditions "A" and "B"), what I would suggest is the following:

1) create two conditions associated with these covariates. E.g. create a new condition "conf_A", enter 0/inf in the onset/duration fields in all sessions where the condition is present, then select in the 'optional fields. task modulation factor' field the option "condition blocks * covariate param_conf_A". The same for the new condition "conf_B" (now select task modulation factor param_conf_B)

2) create a new first-level analysis with the 'analysis type' field set to gPPI, and select when prompted the conditions "A", "B", "conf_A", and "conf_B"

3) in the second-level results window select all four conditions and enter the contrasts:
    [0 0 1 0] to look at the association between connectivity and confidence scores in condition A
    [0 0 0 1] to look at the association between connectivity and confidence scores in condition B
    [0 0 1 -1] to look at the difference in association between connectivity and confidence scores between the two conditions
    [1 0 0 0] to look at the relative connectivity during condition A (average connectivity compared to baseline at the zero-level of the confidence scores covariate)
    [0 1 0 0] to look at the relative connectivity during condition B (average connectivity compared to baseline at the zero-level of the confidence scores covariate)
    [1 -1 0 0] to look at the average connectivity differences between the two conditions (both estimated at the zero-level of the confidence scores covariate)

Last, regarding your question (1), yes, in almost all cases you want to include the "effect of *" covariates as confounding effects during Denoising (this controls for the direct association between the BOLD signal and the covariate, it does not remove or control for the association between the covariate and connectivity values which is what one typically cares about in connectivity analyses). In this case, since you are going to be doing PPI analyses (which explicitly control for the same regressors -the "main psychological term"- in the PPI equation) it should not make a difference whether you enter them or not in the Denoising step (but I would still recommend doing so just for consistency across analyses).

Hope this helps
Alfonso

Originally posted by erik wing:

Hi Alfonso, great, thanks so much for the detailed response--seems like it's working.

erik

Hi All,

A follow-up question regarding the output metric in CONN and interpretation of parametric modulator analyses.

Can we assume that the sign of the effect size (positive or negative), provides information about the direction of the relationship between the parametric modulator and connectivity cortical areas?

Thanks,

Michael Jacob
Post-Doctoral Fellow
San Francisco VA Med Ctr

Hi Alfonso,

I am trying to run a gPPI analysis in CONN (ROI-to-ROI) using parametric modulators and I have followed the instructions you provided below about entering condition-specific temporal modulators. Everything seems to be running fine but I would like to check the gPPI first level design matrices to make sure that my regressors look as expected, but I can't seem to fine this information anywhere? 

The parametric modulators I have are continuous, trial-specific measures of memory performance. I extracted them from my first level SPM.mat files, SPM.xX.X, which contains the mean-centered parametric modulators to be used in my standard univariate first-level models. I specifically want to check that:
1) my parametric modulators (added as first-level covariates in CONN) have been correctly used to weight by my 'conditions' (I just added these as blocks with 0 onset and inf duration, as suggested below), and ...
2) my covariates are being including as regressors in the gPPI analysis (to ensure that both my psychological and physiological variables are controlled for when calculating each ppi). 

Any help would be much appreciated!

Best,
Rose



Originally posted by Alfonso Nieto-Castanon:

Hi Alfonso!

I hope you have been doing well (and staying healthy!). I am running a parametric modulation analysis in conn, using the recommendations in this thread. I have one question: it seems that in order to enter the first-level covariate files, other users have pulled the SPM.xX.X vector, which I believe is the hrf-convolved covariate. I was wondering whether putting an hrf convolved time series in the set-up is acceptable, or whether this results in two rounds of convolution that might skew the data?

I appreciate your help!

Best,
Katherine Aboud

Originally posted by Alfonso Nieto-Castanon:

Dear Alfonso,

I'm now also doing gPPI analysis with parametric modulator. According to your advice, we need to set the trial-by-trial parameter as another covariate in first-level analysis and then do GPPI analysis, right? but im wondering that interaction effects did not involve the actual task-related neural response and not sure the feasibility of this kind of analysis