External Email - Use Caution Perfect, thanks so much for the quick response!
Mark ____________________ Mark Wagshul, PhD Associate Professor Gruss Magnetic Resonance Research Center Albert Einstein College of Medicine Bronx, NY 10461 Ph: 718-430-4011 FAX: 718-430-3399 Email: mark.wags...@einsteinmed.org<mailto:mark.wags...@einsteinmed.org> [einstein-logo-rgb] This email message and any accompanying attachments may contain privileged information intended only for the named recipient(s). If you are not the intended recipient(s), you are hereby notified that the dissemination, distribution, and or copying of this message is strictly prohibited. If you receive this message in error, or are not the named recipient(s), please notify the sender at the email address above, delete this email from your computer, and destroy any copies in any form immediately. From: freesurfer-boun...@nmr.mgh.harvard.edu <freesurfer-boun...@nmr.mgh.harvard.edu> On Behalf Of Douglas N. Greve Sent: Thursday, August 20, 2020 9:53 AM To: freesurfer@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] Extracting LME results from label regions On 8/19/2020 10:24 PM, Mark Wagshul wrote: External Email - Use Caution Dear Freesurfer experts, I have what seems like a simple problem, and I'm sure there is a method for doing this, hoping someone can give me some guidance. We have results from an LME model run on surface data, and then run through PALM to calculate FWE-corrected p-values from the LME. We now would like to extract the mean beta values from each anatomical label region, within regions that were significant at p-corrected < 0.05. The beta values (as well as the PALM p-values) are contained in an mgz surface file. So, what I think is needed is: 1) Create mask images for each individual label index, e.g. lh.caudalmiddlefrontal You can do this in one of two ways: A. Use mri_annotation2label to create a "segmentation", then use mri_binarize with --match to pull out each label as a mask B. Use mri_annotation2label to break the annotation into separate labels, then use mri_label2label to create a binary mask 2) Create mask image from PALM results, for p < 0.05 Not sure about this, but you might be able to use mri_binarize with --max .05 3) Multiply mask * label * beta, and calculate mean value in that region fscalc mask.mgz mul labelmask.mgz mul beta.mgz -o result.mgz So, my simple question is what are the appropriate FS commands for performing this (or alternatively Matlab commands), or is there a simpler way or extracting these values? Thanks for any advice you can give us on this. Mark ____________________ Mark Wagshul, PhD Associate Professor Gruss Magnetic Resonance Research Center Albert Einstein College of Medicine Bronx, NY 10461 Ph: 718-430-4011 FAX: 718-430-3399 Email: mark.wags...@einsteinmed.org<mailto:mark.wags...@einsteinmed.org> [einstein-logo-rgb] This email message and any accompanying attachments may contain privileged information intended only for the named recipient(s). If you are not the intended recipient(s), you are hereby notified that the dissemination, distribution, and or copying of this message is strictly prohibited. If you receive this message in error, or are not the named recipient(s), please notify the sender at the email address above, delete this email from your computer, and destroy any copies in any form immediately. _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.nmr.mgh.harvard.edu%2Fmailman%2Flistinfo%2Ffreesurfer&data=02%7C01%7CMark.wagshul%40einsteinmed.org%7Ccb155f1722e04a77950408d845107a18%7C9c01f0fd65e040c089a82dfd51e62025%7C0%7C0%7C637335284366752150&sdata=5Z6yUk3ce8GBN7m0zAmGxVENyHv86s0sVIHffgEhwRc%3D&reserved=0>
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