Freesurfer July 2018

freesurfer@nmr.mgh.harvard.edu

112 participants
165 discussions

Transform using talairach.m3z for submillimeter data
by Ren, Jianxun 26 Jul '18

26 Jul '18

Hi FS experts, I reconstructed several 0.75mm structural images using recon-all v6 with highres flag successfully and everything looks fine. Now, I want to transform structural images (norm.mgz, 0.75mm3, 320^3) to the common space (MNI305, 1mm3, 255^3 for freesurfer) using mri_vol2vol with mri/transforms/talairach.m3z, however the result looks wired with distorted shape and wrong centroid. I also attempt to run mri_convert with "-at" flag. The output looks fine and the resolution keeps the same with input, i.e. 0.75mm3. So I suppose the problem of using mri_vol2vol is the wrong "targ" reference, which should be a high resolution MNI305, But I can't find it. Could you tell me if my understanding is right or not? If it's right, where can I find the high resolution MNI305 template? There are the commands I used: mri_vol2vol --mov norm.mgz --targ $FREESURFER_HOME/average/mni305.cor.mgz --m3z transforms/talairach.m3z --noDefM3zPath --o norm_mni305.nii.gz --interp trilin mri_convert -at transforms/talairach.m3z norm.mgz norm_mni305.nii.gz -oc 0 0 0 Best, Jianxun

1 0

26 Jul '18

External Email - Use Caution Dear Freesurfer experts, I have mask in MNI152 space. I would like to register this mask to fsaverage. How can I do this registration. I am thinking of "mri_vol2surf" as follows: mri_vol2surf --mov mask.nii --mni152reg --regheader fsaverage --o mask_fsaverage.nii Is this command correct? Thank you for your valuable guidnace John

2 4

Data sets with enlarged ventricles
by Khoi Mai 26 Jul '18

26 Jul '18

External Email - Use Caution Hello, We are trying to run two subjects through recon-all. The kicker is that these two have very large ventricles and we have been stuck at mris_fix_topology (the script reports about 100-ish defects for each). We use HPC to run the analyses but even with a walltime of 96 hours each, both subjects were unable to be finished within that time frame. My question is that is there any way to improve mris_fix_topology runtime? Subject 1: Subject 2: Thank you for your help. Best, Khoi

2 2

mri_glmfit
by Piero Chiacchiaretta 26 Jul '18

26 Jul '18

External Email - Use Caution Dear FS group, I would like to restrict mri_glmfit analysis, on FS-FAST outputs, to the cerebellum. Thus, I’m using —mask, adding the mask of cerebellum. To this aim I can obtain it using mri_binarize. Do you agree? mri_binarize --i ... --match 8 --o Left_Cerebellum.nii.gz mri_binarize --i ... --match 45 --o Right_Cerebellum.nii.gz Which is the correct input? aparc+aseg in fsaverage? In which dimension? mri.2mm? mri? Thanks Piero

2 1

brain parenchymal fraction
by Barletta, Valeria 26 Jul '18

26 Jul '18

Dear Freesurfers, I need to extract the brain parenchymal fraction to check brain atrophy in my patients. The BPF is defined as "the ratio of brain parenchymal tissue volume to the total volume contained within the brain surface contour". In the text file aseg.stats, I found this info: # Measure BrainSeg, BrainSegVol, Brain Segmentation Volume, 1052408.000000, mm^3 # Measure BrainSegNotVent, BrainSegVolNotVent, Brain Segmentation Volume Without Ventricles, 1032104.000000, mm^3 # Measure BrainSegNotVentSurf, BrainSegVolNotVentSurf, Brain Segmentation Volume Without Ventricles from Surf, 1030608.836503, mm^3 Can I extract the BPF from those, or is any of them the BPF? Thank you, Valeria

2 2

Yeo-2011 individual ROIs
by Ehsan Tadayon 26 Jul '18

26 Jul '18

External Email - Use Caution Hi, Is there an easy way to sub-parcellate each network in Yeo-2011 (for instance network 7) into several clusters (ROIs) for an individual subject? thanks Ehsan.

3 2

Longitudinal Processing - Retaining Manual Edits
by KALEY E ELLIS 26 Jul '18

26 Jul '18

External Email - Use Caution Dear FreeSurfer Developers, We are using your longitudinal processing pipeline (version 6.0) for our study (n=140, 3 timepoints). Our scanner was upgraded a year ago and the a filter was used on the raw images to adjust for hyperintensities in the images due to using a multi-channel coil. At this point, our bases have already been processed and consist of three timepoints with a mix of filters: T1 no filter, T2 no filter, T3 with filter. Since we have invested many resources into the manual editing process, we want to try to retain all the manual edits that have been done to the base. We still have the original scan and the filtered scan for each subject. We have some ideas on how we might be able to retain the edits, but would you be able to provide input regarding whether this seems reasonable? Your feedback is greatly appreciated! Plan A: 1) Create a new T3 cross-sectional with the correct nifti 2) Run recon-all on the existing base Q: Will this retain the previous manual edits AND incorporate the new cross-sectional image? Plan B: 1) Create a new T3 cross-sectional with the correct nifti 2) Create a new base Q: Is it possible to transfer data from old manual edits to a new base? If so, how? Thank you! Kaley Associate Research Specialist Center for Healthy Minds Waisman Center for Brain Imaging and Behavior University of Wisconsin-Madison

1 0

longitudinal qdec contrasts
by Courtney Haswell 26 Jul '18

26 Jul '18

External Email - Use Caution We are working in Matlab to analyze longitudinal data using the mass-univariate approach to Linear Mixed Effects (LME) Modeling. Step 1: Below is the design matrix (first few rows) we created in an effort to compare subjects with low, moderate, and high drinking behavior with respect to time (3 groups). 1 0 1 0 0 0 0 0 1 3.0 1 3.0 0 0 0 0 1 0 1 0 0 0 0 0 1 1.2 0 0 1 1.2 0 0 1 2.3 0 0 0 0 1 2.3 1 3.5 0 0 0 0 1 3.5 1 0 1 0 0 0 0 0 Column 1 - intercept ones; 2 - time (years since baseline scan); 3 - low drinking group; 4 - low drinking x time; 5 - mod drinking group; 6 - mod drinking x time; 7 - high drinking group; 8 - high drinking x time Step 2: To compare low/moderate drinking, mod/high drinking, and low/high drinking, we created the contrast matrix below: CM=[0 0 0 1 0 -1 0 0; 0 0 0 0 0 1 0 -1; 0 0 0 1 0 0 0 -1] Row 1 - low/mod drinking; Row 2 - mod/high drinking; Row 3 - low/high drinking Please let us know if the contrast matrix is appropriate for our design matrix and the comparisons we would like to make. This seems incorrect since there is only one output from "lme_mass_F(lhstats,CM);" Thank you for your help.

2 1

ICV as covariate for controls vs. patients
by Martin Juneja 26 Jul '18

26 Jul '18

External Email - Use Caution Hello experts, I am interested in identifying regions of interest by comparing cortical volume (CV) between controls and patients. After including age and sex as my covariates, I identified regions X1 and X2, which showed significantly lower CV for patients (as compared to controls). But after I include ICV as another covariate, my results show that for none of the areas there is any significant difference in CV, i.e. my results vanish. When I checked subjectwise ICV for each group, I found that there is almost significant difference (two-sampled t-test, p = 0.067) in ICV between two groups, but interestingly mean group ICV for patients group was larger compared than mean ICV for controls. But as I said earlier, regions X1 and X2 had significantly lower CV for patients (as compared to controls), when I didn't include ICV as covariate. Could you please help me in interpreting these results? Is there any advice regarding inclusion of ICV as covariate? Or my results are purely because of differences in ICV between groups, and there is no real findings regarding the regions identified (X1 and X2)? Thanks a lot !

3 11

questions about Tracula and doeddy=1 or 0
by Daumail, Loic Jean 26 Jul '18

26 Jul '18

Dear FreeSurfer experts, I am analyzing a dataset (Q-Ball) of subjects with TRACULA on FreeSurfer 6.0. b1=0, b2=3000 s/mm2 and diff directions = 90. Surprisingly the trac-all reconstruction with doeddy =1 was not as good as with doeddy = 0 on average on all the subjects, as in our dataset there were more tracts missing with doeddy=1 among all the subjects, which is a very curious result, shouldn't we get the reverse? ==> should I rely more on the doeddy=0 result then? ==> or should I modify the configuration parameters, such as adding more control points , ... and maybe others? Secondly, we are using the head motion parameters (computed with doeddy=1) to exclude subjects who moved too much. Is that appropriate? In other words, can we trust the motion parameters and exclude subjects on that basis? Thank you for your time and consideration, Sincerely, Loic

1 1

← Newer
1
2
3
4
5
6
7
8
...
17
Older →

Jump to page:

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Freesurfer July 2018