Hi,
I donšt think there is a simple, single answer to this and opinions might
vary. My personal opinion is that using an average means that you will
have a stronger signal related to your task but could reduce other signals
that might be of interest for correlations with other areas, particularly
if your region spans different resting-state networks (which would not be
obvious from the task results alone). Also, averaging across slices can be
an issue with respect to slice timing changes not being accounted for. So
I suspect that the peak voxel approach is more common for these reasons,
and you might have less trouble with reviewers if you stuck to that, but
other people might have different opinions.
All the best,
Mark
On 08/01/2015 07:50, "Dennis van der Meer" <[log in to unmask]> wrote:
>Hi there,
>I'm hoping to get feedback on my method for seed region selection for a
>PPI analysis. I first ran a straightforward fMRI analysis for a working
>memory task, and found two significant clusters (thresholded at Z=2.6,
>p=.001). As I wanted to get one homogeneous cluster with strong signal, I
>thresholded the data again at Z=3.7, leaving me with one cluster of 61
>voxels for which I extracted the mean timeseries (in every subjects
>native space) and used this timeseries for the PPI analysis. I chose this
>approach because I thought this might better capture signal/ be more
>robust than basing my seed on a peak voxel, even though the latter is
>suggested in several online tutorials. Can someone explain the pro's and
>cons of either approach?
>Thanks! Kind regards, Dennis
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