Freesurfer

freesurfer@nmr.mgh.harvard.edu

16 participants
30085 discussions

Covariates in qdec
by Dankner, Nathan (NIH/NIMH) [F] 01 Mar '10

01 Mar '10

Hi all, I've covaried age in a group difference analyses on cortical thickness data both in qdec for vertex-based and in spss for the gyral-based data. For the vertex-based data, covarying age has almost no effect, and if anything intensifies the group differences. However, when run in qdec, covarying age in this dataset wipes out almost any group differences. Is covarying in qdec to be trusted? It seems strange to me that these two methods would result in such extreme differences in results. Thanks, Nathan

3 2

mri_convert dicom conversion
by Satrajit Ghosh 01 Mar '10

01 Mar '10

hi, mri_convert TrioTim/123456-10-1.dcm test.nii.gz will scan all files in the dicom directory to get at series 10. in the above scenario, i know that the files corresponding to the run are 123456-10-*.dcm. is there a way to leverage this information, so that it does not scan the entire directory? cheers, satra

2 1

ICV Error fs 4.3.1
by Jeff Sadino 01 Mar '10

01 Mar '10

Hello, I reconed 149 subjects using freesurfer 4.3.1 automatically using our cluster. When I did asegstats2table, all 149 subjects had the exact same icv: # Measure IntraCranialVol, ICV, Intracranial Volume, 1948106.000000, mm^3 Running mri_segstats manually, here is the beginning of the output: mri_segstats --seg mri/aseg.mgz --sum stats/aseg2.stats --pv mri/norm.mgz --excludeid 0 --brain-vol-from-seg --brainmask mri/brainmask.mgz --in mri/norm.mgz --in-intensity-name norm --in-intensity-units MR --etiv --surf-wm-vol --ctab $FREESURFER_HOME/ASegStatsLUT.txt --subject 040002_S04 atlas_icv (eTIV) = 1948106 mm^3 (det: 1.000000 ) Loading mri/aseg.mgz Getting Cerebral WM volumes from surface lh white matter volume 246101 rh white matter volume 251160 Loading mri/norm.mgz Loading mri/norm.mgz Loading mri/brainmask.mgz # nbrainmaskvoxels 1766623 # brainmaskvolume 1766623.0 # nbrainsegvoxels 1310448 # brainsegvolume 1310448.0 Voxel Volume is 1 mm^3 Generating list of segmentation ids Found 50 segmentations Computing statistics for each segmentation 1 2 Left-Cerebral-White-Matter 247216 247216 Looking at the log files for several other subjects, they all have a determinant of 1. However, all of the other numbers - brainmask, brain seg volume, individual regions - are all unique and correct. It is just the icv. Can anyone point me in the right direction of how to investigate the determinant value? Here is the mri_segstats version: # cvs_version $Id: mri_segstats.c,v 1.33.2.5 2009/02/11 22:38:51 nicks Exp $ Thanks! Jeff Sadino

4 11

ROI extraction based on t-values
by Cindy Eckart 01 Mar '10

01 Mar '10

Dear FreeSurfers, is there an easy way to extract the mean cortical thickness of clusters according to a predefined t-value threshold? I performed a cortical thickness analysis between 3 groups and found some clusters where the groups differed significantly. Now, I would like to extract the mean cortical thickness within those clusters for further statistical analyses (e.g. regression models). Is there a practical way to do this? Thank you very much in advance! Cindy ___________________________________________________________ GRATIS für alle WEB.DE-Nutzer: Die maxdome Movie-FLAT! Jetzt freischalten unter http://movieflat.web.de

2 1

design matrix problem in qdec
by "Brühl＠nmr.mgh.harvard.edu, Hannah" 01 Mar '10

01 Mar '10

Hi to All, I have come across a problem using qdec, specified below. What I am trying to do is to look for an interaction between two discrete variables (diagnostik and PTSD). It is my understanding that this appears to be a common problem. However, is there a way around it, such as conceptualizing the design matrix differently? And how could that be done? Thanks, Hannah [LOE5145MAC:~/Borderline_2/subjects] bruehh01% lh-Avg-Intercept-thickness ----------------------- Does the average thickness differ from zero? 1.000 1.000 1.000 1.000; lh-Diff-NonPTSD-PTSD-Intercept-thickness ----------------------- Does the average thickness, accounting for diagnostik, differ between NonPTSD and PTSD? 1.000 -1.000 1.000 -1.000; lh-Diff-BPD-KG-Intercept-thickness ----------------------- Does the average thickness, accounting for PTSD, differ between BPD and KG? 1.000 1.000 -1.000 -1.000; lh-X-PTSD-diagnostik-Intercept-thickness ----------------------- Is there a PTSD--diagnostik interaction in the mean thickness? 1.000 -1.000 -1.000 1.000; ninputs = 49 Checking inputs nframestot = 49 Allocing output nframes = 49 Writing to /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/y.mgh gdfReadHeader: reading /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/qdec.fsgd WARNING: gdfReadV1: class PTSDPTSD-diagnostikKG is defined but not used. INFO: demeaning continous variables Continuous Variable Means (all subjects) Class Means of each Continuous Variable 1 PTSDNonPTSD-diagnostikBPD 2 PTSDPTSD-diagnostikBPD 3 PTSDNonPTSD-diagnostikKG 4 PTSDPTSD-diagnostikKG INFO: gd2mtx_method is dods Reading source surface /Users/bruehh01/Borderline_2/subjects/fsaverage/surf/lh.white reading group avg surface area 822 cm^2 from file Reading in average area /Users/bruehh01/Borderline_2/subjects/fsaverage/surf/lh.white.avg.area.mgh Number of vertices 163842 Number of faces 327680 Total area 65416.648438 AvgVtxArea 0.399267 AvgVtxDist 0.721953 StdVtxDist 0.195470 $Id: mri_glmfit.c,v 1.138.2.15 2009/07/27 17:43:10 greve Exp $ cwd /Users/bruehh01/Borderline_2/subjects cmdline mri_glmfit --y /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/y.mgh --fsgd /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/qdec.fsgd dods --glmdir /Users/bruehh01/Borderline_2/subjects/qdec/Untitled --surf fsaverage lh --label /Users/bruehh01/Borderline_2/subjects/fsaverage/label/lh.cortex.label --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Avg-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Diff-NonPTSD-PTSD-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Diff-BPD-KG-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-X-PTSD-diagnostik-Intercept-thickness.mat sysname Darwin hostname LOE5145MAC.campus.fu-berlin.de machine i386 user bruehh01 FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 0 y /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/y.mgh logyflag 0 usedti 0 FSGD /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/qdec.fsgd labelmask /Users/bruehh01/Borderline_2/subjects/fsaverage/label/lh.cortex.label maskinv 0 glmdir /Users/bruehh01/Borderline_2/subjects/qdec/Untitled IllCondOK 0 DoFFx 0 Creating output directory /Users/bruehh01/Borderline_2/subjects/qdec/Untitled Loading y from /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/y.mgh INFO: gd2mtx_method is dods Saving design matrix to /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/Xg.dat Matrix condition is 1e+08 ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 1.000 0.000; Error in Analyze: command failed: mri_glmfit --y /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/y.mgh --fsgd /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/qdec.fsgd dods --glmdir /Users/bruehh01/Borderline_2/subjects/qdec/Untitled --surf fsaverage lh --label /Users/bruehh01/Borderline_2/subjects/fsaverage/label/lh.cortex.label --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Avg-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Diff-NonPTSD-PTSD-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-Diff-BPD-KG-Intercept-thickness.mat --C /Users/bruehh01/Borderline_2/subjects/qdec/Untitled/contrasts/lh-X-PTSD-diagnostik-Intercept-thickness.mat

2 1

conitnue longitudinal processing...
by Gonzalo Rojas Costa 01 Mar '10

01 Mar '10

Hi: I was processing a longitudinal processing (previous the 8.8 earthquake) using a command line like: recon-all -base <templateid> -tp <tp1id> -tp <tp2id> ... -all but it couldn't finished because the power cut... How can I continue the processing without starting again from the beginning ?... Sincerely, Gonzalo Rojas Costa Department of Radiology Las Condes Clinic Santiago Chile

1 0

Destrieux boundaries Insular cortex
by Lena Palaniyappan 28 Feb '10

28 Feb '10

Is there any reference available for tracking the boundaries of 5 parcellations within insula as given by Destrieux a2009s? Cheers Lena _______________________________________________________ Lena Palaniyappan Clinical Lecturer & Honorary StR | Division of Psychiatry ( University of Nottingham) South Block 'A' floor| Queens Medical Centre | Nottingham | NG7 2UH This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.

3 2

inconsistencies in surface segmentation and aseg.mgz
by Joonkoo Park 27 Feb '10

27 Feb '10

Dear surfers, I was visually inspecting the results of the auto-recon process using tkmedit (e.g. tkmedit subject brainmask.mgz lh.white -aux T1.mgz -aux-surface rh.white -segmentation aseg.mgz /usr/local/freesurfer/FreeSurferColorLUT.txt) I noticed that often times the segmentation results from the surface files (pial, main and original surface represented by red, yellow, and green, respectively) are not consistent with the results from the aseg.mgz files. For instance, a piece of gray matter in the anterior temporal pole is colored pink and indicates Left-Cerebral-Cortex as the label, but this area is marked to be outside the red line (pial surface). Likewise, yellow line often cuts through the area indicated as white matter by aseg.mgz. >From a previous email thread, I noted that Bruce mentioned that "We usually trust the surfaces more than the aseg for cortical morphometry." Does this mean that aseg.mgz is not used in subsequent processing stream? What shall I do if I (after visually inspecting this) want to correct the surfaces more similar to the results given be aseg.mgz? Thanks for your help in advance. Joon -- Joonkoo Park Ph.D. Candidate, Cognition & Cognitive Neuroscience Department of Psychology, University of Michigan

2 1

Surface very inaccurate after reprocessing for control points
by Laluz, Victor 26 Feb '10

26 Feb '10

Hello, Throughout most of Freesurfer's version history, I have noticed this problem pop up every 50 or so cases I do: I will have a pretty well-processed surface but with some inaccuracies in the WM surface, I will put control points down to correct it, and reprocess with autorecon2-cp as usual. The surface comes back with the areas I put CPs for accurate, but the rest of the cortex in a localized area will have a massive amount of the surface missing. An example is attached as a .png image. Now, if I go back and put down control points throughout the part of the surface that is missing after the first reprocessing and repeat autorecon2-cp, the end result at that point is usually a perfectly accurate surface. However, I was wondering if the massive loss in surface after the first autorecon2-cp is attributable to some kind of bug that I might be able to avoid. Any thoughts would be great, thanks! Victor Victor Laluz Imaging Coordinator UCSF Memory and Aging Center 350 Parnassus Avenue, Suite 905 Box 1207 San Francisco, CA 94143-1207 Phone: (415) 476.1680 Fax: (415) 476.1816 Email: vlaluz(a)memory.ucsf.edu<mailto:vlaluz@memory.ucsf.edu> memory.ucsf.edu<http://www.memory.ucsf.edu/> www.youtube.com/ucsfmemoryandaging<http://www.youtube.com/ucsfmemoryandaging>

2 1

mri_glmfit reference
by Georg Homola 26 Feb '10

26 Feb '10

Hi, following these refs I've found on the list and the mri_glmfit help, I assume the cluster-wise probability of the permutation simulation is based on cluster mass inference, is that right? Thanks, Georg Statistical Science 2004, Vol. 19, No. 4, 676-685 Institute of Mathematical Statistics, 2004 Permutation Methods: A Basis for Exact Inference Michael D. Ernst Human Brain Mapping 15:1-25(2001) Nonparametric Permutation Tests For Functional Neuroimaging: A Primer with Examples Thomas E. Nichols and Andrew P. Holmes Validating cluster size inference: random field and permutation methods Satoru Hayasaka and Thomas E. Nichols, NI, 20 (2003) 2343-2356. Hagler and Sereno, NI, 2006, vol 33 1093-1103. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

2 1

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