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Re: [Freesurfer] MRI protocol for hippocampal subfield segmentation
by Gabor Perlaki 27 Feb '18

27 Feb '18

According to Bruce's request I repost the rest of thread: We set up our our hippocampal subfield T2 protocol based on the ADNI protocol (with some minor differences) as suggested by Iglesias. However, we see an artefact similar to the image found in ADNI2 training manual (page 28): http://adni.loni.usc.edu/wp-content/uploads/2012/10/ ADNI2MRITrainingManual_2014.pdf Please see the hyperintense artefact in the anterior part of the sagittal and axial images. Do you also see this artefact in your images? Do you have any idea about the cause of the artefact or do you have any suggestion on how to get rid of it?

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KWWidgets
by Melvin Robinson 27 Feb '18

27 Feb '18

A FreeSurfer build from source requires KWWidgets, but it has been deprecated for 10 years. What does Freesurfer use this code for?

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aseg and aparcstats2table on longitudinal data
by Lisa Delalande 26 Feb '18

26 Feb '18

Hi FreeSurfer experts, I would like to use the asegstats2table and the aparcstats2table commands on my longitudinal data, that have been processing following the longitudinal pipeline (recon-all -cross, - base and finally -long). And I have two questions : 1) Am I obliged to do the long_stats_slopes --qdec ./qdec/longi_presentacio.qdec.table.dat --stats aseg.stats --meas volume --do-avg --do-rate --do-pc1 --time months command before using the aseg/aparc commands ? 2) And I would like to know which subjectsID I have to choose for the command line, example : asegstats2table --subjects *001_tp1 002_tp1*...etc. (cross)? OR *template_001 template_002*...etc (base)? OR *001_tp1.long.template_001 002_tp1.long.template_002*...etc (long)? Knowing that it's for a comparison test-retest (microlongitudinal : 1 month). Thanks you for your precious help, Best regards, Lisa

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questions about the segamentation volume
by Ting Li 26 Feb '18

26 Feb '18

Hi FreeSurfer Expert, I followed the Anatomical ROI analysis tutorial and make a aseg.vol.table for the 40 subjects. However, the segment volume can various a lot in different subjects, such as Left-Lateral-Ventricle, Left-Inf-Lat-Vent. I attached a screenshot. Thanks for your response. Best wishes, Ting

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FSGD and Contrast to determine the effect of age on performance
by Martin Juneja 26 Feb '18

26 Feb '18

Hi experts, I am not sure if this has already been addressed in FS discussion. If my fsgd file looks like following: Class Male Class Female Variables Performance Age Input S1 Male 181 25 Input S2 Female 167 23 .... .... I understand that the contrast matrix *[**0 0 1 -1 0 0]* represents whether there is a difference between the group (male/female)-performance slopes regressing out the effect of age. In other words, this matrix calculates whether two slopes i.e. one for males: thickness-performance slope and second for females: thickness-performance slope - are significantly different after regressing out the effect of age. My questions are following: (1) Is my above interpretation of contrast matrix *[**0 0 1 -1 0 0]* correct? (2) If my interpretation is correct, then I was wondering what will be the contrast matrix if *I want to check whether there is **significant effect of age on e.g. thickness-performance slopes after regressing out the effect of gender.* And what will be the corresponding fsgd file for that, any changes in that? Thanks.

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Unknown Label in following postprocessing with mne-python
by Daniel van de Velden 26 Feb '18

26 Feb '18

Dear fellow colleagues, in my study I use volumetric source spaces. In the process of creating them, I decided to identify all available volumelabels FreeSurfer "recognized" in the given mri image. Every single subject inherits, despite all other volumelabels, a volumelabel called "Unknown". In an 3D representation of those "unknown" volumelabel coordinates I opine those points could also be part of other volumelabels like cerebral cortex or white matter. Would you please give me a detailed explanation of the origin of this 'Unknown' label? Specifically in respect to the fsaverage "subject". Best regards, Daniel van de Velden

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Extraction of cortical thickness in native space
by Albrecht, Daniel S. 26 Feb '18

26 Feb '18

Hello, We are attempting to extract cortical thickness values from a pre-defined ROI in MNI space. Originally, we had followed the instructions detailed here: https://surfer.nmr.mgh.harvard.edu/fswiki/VolumeRoiCorticalThickness for extraction in MNI space, but now wanted to extract thickness from individual subject T1 images in native space. We attempted to use the same pipeline (e.g. register native space T1 to fsaverage) but were unable to get a good registration with several different tools. Is there a better way to do this, either by improving the registration to fsaverage, or by transforming the ROI to native space (which we have done) and projecting that to a surface/label? Any suggestions are welcome, and we appreciate the help! Cheers, Dan VolumeRoiCorticalThickness - Free Surfer Wiki<https://surfer.nmr.mgh.harvard.edu/fswiki/VolumeRoiCorticalThickness> surfer.nmr.mgh.harvard.edu Cortical Thickness of a Volume-defined ROI. This page describes the workflow to extract freesurfer cortical thickness values for a region-of-interest (ROI) defined in ...

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local gyrification index - contrast and doss vs. dods
by C.P.E. Rollins 26 Feb '18

26 Feb '18

Dear Freesurfer Developers, I'm following the very well-documented steps proposed by Marie Schaer for measuring local gyrification index in the paper "How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index" (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3369773/) I have 2 questions I was wondering whether you might be able to answer: 1) Based on the contrast and FSDG files described in the paper (patients vs. controls, controlling for age and gender), would significant regions in blue refer to regions with reduced gyrification in patients relative to controls (patient LGI < control LGI) or vice versa? 2) When I try running the analysis in QDEC as opposed to command line with mri_glmfit, I get very different results - I think because QDEC is generating different contrast files than the one specified in the paper (1 1 -1 -1 0). Basically, this seems to be due to the difference in modelling LGI with doss (command line) or dods (default QDEC). I was wondering whether there were any reason doss was chosen for LGI instead of dods (see Step 5 in Section 4 Statistical Group Comparisons)? Thank you in advance for your help. Kindest regards, Colleen -- Colleen Rollins, PhD Candidate Department of Psychiatry University of Cambridge Herchel Smith Building, Robinson Way, Cambridge CB2 0SZ

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Surface Errors
by Emily Schwartz 26 Feb '18

26 Feb '18

Hi FreeSurfer community, I am working on elderly subjects and have had this kind of surface error quite often in different locations. Usually, it's over about 3-4 slices (in one view) and can be fixed, but I am finding multiple in almost every subject and therefore taking a lot of time to edit (and then once reprocessed new ones sometimes come up in different locations). I was wondering if these are normal to have, minor surface errors that do not necessarily need to be fixed. I have added images of an example below. Here are some of the values I get from aparc.stats for caudal middle frontal lh after manually editing: Surface area: Original - 2500 mm^2 Edited - 2532 mm^2 Grey volume: Original - 1710 mm^3 Edited - 1727 mm^3 Thickness avg Original - 4455 mm std: 2.371 Edited - 4445 mm std: 2.365 Although the thickness values only differ by 10 mm, we are a little concerned because the difference is not within the standard deviation. Please let me know if all of these errors should be edited or if it may be okay to keep the smaller segmentation/topological errors as they are. Thank you, Emily

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Re: [Freesurfer] MRI protocol for hippocampal subfield segmentation
by Gabor Perlaki 26 Feb '18

26 Feb '18

Does anybody else have a comment on the experienced artefact?

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