Hi Alfonso,

I'm sorry for posting this twice, I realized it may have gotten lost where I posted it initially (in another thread). I'm hoping you can give me some advice. I have some task-based data that I'd like to analyse, both for functional connectivity (PPI) and activation. Since the data is already preprocessed in conn19.b, I'd like to keep it in conn to analyse the activation data, if possible.

My question is this: I have hypotheses about specific ROIs--if I follow the steps below (from another thread*), but keep the ROIs that I'm interested in as sources in the first-level analysis, will this give me activation, limited to my ROIs? Alternatively, would it be better/more accurate to take my data out of conn, and analyze it using e.g., MarsBaR software?



*Steps listed in previous activation question (https://www.nitrc.org/forum/message.php?...)

1) in Setup.Denoising remove all of the effects from the 'confounding effects' list, and enter in 'band-pass filter' the same filter that you used in FSL (e.g. a high-pass filter only)

2) in first-level analysis select as 'sources' all of the 'effect of task' effects plus your 'realignment' covariates (and any other covariates you may have entered into your first-level models in FSL; do not select there any of the ROI sources), then select the 'multivariate regression' measure, set the 'weighting' to 'none', press 'Done' and select there only the 'rest' condition before pressing 'Ok'
3) in second-level analysis select in the 'sources' list your task effect(s) and enter the desired contrast

Thanks,
Tiffany

Hi Tiffany

You are right, the procedure describe in that thread would be just the same whether you want to analyze ROI-level data or voxel-level data (you would use either ROI-to-ROI analyses or seed-to-voxel analyses, respectively, entering as "seeds" all of your task + motion/noise effects and selecting "multivariate regression" as measures of interest in order to have all of those effects estimated jointly in a single first-level GLM analysis)

Best
Alfonso
Originally posted by tiffanyk:

Thanks so much Alfonso!

Thanks,
Tiffany