Thanks Doug and Bruce, Fscalc is great because I can do maths with multiple inputs, like a mean. I’m noticing, however, that when I do a mean with fscalc I get different values than when I do the mean in Matlab, any idea why? In some regions the differences are as much as 5-10% so I don’t think it’s just a rounding/precision issue.
fscalc a.mgh add b.mgh add c.mgh add d.mgh add e.mgh div 5 --odt float --o mean.mgh Hard to imagine it’s an order of operations issue, but I am a little confused about how fscalc handles order of operations from the help page. I’m using v5.3.0 and Matlab 2014b Thanks, Jared From: <freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Douglas Greve <gr...@nmr.mgh.harvard.edu> Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu> Date: Friday, February 16, 2018 at 11:31 AM To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu> Subject: Re: [Freesurfer] Freesurfer equivalent to fslmaths? Or fscalc On 2/16/18 11:22 AM, Bruce Fischl wrote: Hi Jared I think mris_calc does at least some of what you want. cheers Bruce On Fri, 16 Feb 2018, Zimmerman, Jared wrote: Hi all, Is there an equivalent of fslmaths in Freesurfer? I would like to add two scalar value images (.mgh files) that are registered to the fsaverage6 surface but I’m not seeing an obvious way to do it. Right now I’m reading the images into Matlab to add them, but this is a bit inconvenient because what I would like to do is smooth an image by a small amount, add the original image back to it, then smooth again marginally and iterate until I get to a target fwhm. Since I can’t smooth inside Matlab this necessitates writing out a temp image for each smoothing step then reading it back into Matlab for the adding. Obviously this is a solvable problem, but as someone only marginally proficient in Matlab it’s something I’d like to avoid, plus it seems like a lot of I/O for this task. A little more detail on my data and what I’m trying to do: The scalar images I’m working with are network confidence maps, basically like the spatial maps from an ICA dual-regression. I want to combine the confidence maps together into a hard partition and write it to an annot file, but I want to smooth them first. I’m concerned, however, that smoothing is going to bias the parcellation against small network parcels and in favor of large network parcels because in each confidence map the small parcels will be surrounded by lots of zeros (does this make sense?). To correct for this, my idea was to iteratively smooth by small amounts and to add the original confidence values (or some fraction of them) back to the smoothed map after each iteration so that regions of high confidence with a small/narrow spatial spread do not become diluted by the smoothing and don’t get taken over by larger high confidence regions in nearby networks. One final question would be how to smooth on a surface without resampling. Right now I’m using mri_surf2surf and smoothing when I resample to the native mesh, but if I take the above approach I would not want to resample at each smoothing step. Could I just use mri_surf2surf with –srcsubject and –trgsubject pointing to the same subject? Thanks, Jared ____________________________ Jared P. Zimmerman jar...@pennmedicine.upenn.edu<mailto:jar...@pennmedicine.upenn.edu> Neuroscience Graduate Student Oathes Lab University of Pennsylvania _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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