Freesurfer December 2012

freesurfer@nmr.mgh.harvard.edu

101 participants
161 discussions

selxavg3 error : NaN in Xtmp.mat file
by Sid Kundu 07 Dec '12

07 Dec '12

Hi Doug, I created a new first level analysis along with new contrasts and event numbers for a group of subjects I had previously done analyses on. All but one of the subjects finished selxavg3 without error, but one subject gave the following error: Saving X matrix to /home/sidkundu/freesurfer/sessions/Heading-TTP/LMV2010_N19_Heading-TTP/bold/TTP-analysis-3/Xtmp.mat Error using svd Input to SVD must not contain NaN or Inf. Error in cond (line 39) s = svd(A); Error in fast_selxavg3 (line 248) XCond = cond(XtX); >> ------------------------------------------ ERROR: fast_selxavg3() failed\n I looked at the generated Xtmp.mat file and it shows the design matrix X has all zeros in 2 columns, resulting in NaNs in the same columns for Xn, and corresponding NaNs in XtX. I'm wondering what would cause this or why it would only occur for only one subject. _________________________________ Sid Kundu Biomedical Engineering Boston University

3 6

Running Tracula: trac-preproc exited with ERRORS
by s0675204 07 Dec '12

07 Dec '12

Hello I am trying to run Tracula on a set of dicoms files. I have run recon-all previously. I am getting a message trac-preproc exited with ERRORS Please can anyone advise me on this? I have listed all the details (command, error/output, config file) below Thank you so much for your help best wishes, Prerona DETAILS ========================================= command used: trac-all -prep -c ./scripts/dmrirc_single_subject ========================================= error message ========================================= canlilab@112-205:~/SBU/data/tracula$ trac-all -prep -c ./scripts/dmrirc_single_subject INFO: SUBJECTS_DIR is /home/canlilab/SBU/data/recon_output INFO: Diffusion root is /home/canlilab/SBU/data/tracula Actual FREESURFER_HOME /home/canlilab/freesurfer trac-preproc -c /home/canlilab/SBU/data/tracula/00075/scripts/dmrirc.local -log /home/canlilab/SBU/data/tracula/00075/scripts/trac-all.log -cmd /home/canlilab/SBU/data/tracula/00075/scripts/trac-all.cmd #------------------------------------- /home/canlilab/freesurfer/bin/trac-preproc #------------------------------------- #@# Image corrections Fri Dec 7 10:42:30 EST 2012 mri_convert /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig.nii.gz mri_convert /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig.nii.gz $Id: mri_convert.c,v 1.179.2.2 2011/05/16 20:53:47 greve Exp $ reading from /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm... Getting Series No INFO: Found 44 files in /home/canlilab/SBU/data/dti_dicoms/00075 INFO: Scanning for Series Number 7 Scanning Directory INFO: found 42 files in series INFO: loading series header info. RunNo = 6 WARNING: Run 1 appears to be truncated Files Found: 42, Files Expected (lRep+1): 2 FileName /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm Identification NumarisVer syngo MR B17 ScannerModel TrioTim PatientName S790_P72_TC Date and time StudyDate 20120502 StudyTime 120741.390000 SeriesTime 125649.953000 AcqTime 125626.150000 Acquisition parameters PulseSeq ep_b0 Protocol DTI PhEncDir COL EchoNo 1 FlipAngle 90 EchoTime 93 InversionTime -1 RepetitionTime 5500 PhEncFOV 220 ReadoutFOV 220 Image information RunNo 6 SeriesNo 7 ImageNo 1 NImageRows 896 NImageCols 896 NFrames 42 SliceArraylSize 40 IsMosaic 1 ImgPos 772.3238 773.0634 185.5347 VolRes 1.7188 1.7188 3.0000 VolDim 128 128 40 Vc -0.9983 0.0151 -0.0555 Vr -0.0000 -0.9648 -0.2629 Vs -0.0575 -0.2625 0.9632 VolCenter 0.0000 0.0000 0.0000 TransferSyntaxUID 1.2.840.10008.1.2.1 INFO: sorting. INFO: (128 128 40), nframes = 42, ismosaic=1 Could not parse NUMARIS version string syngo MR B17 found in dicom tag 18,1020 (len = 3 != 6) Repetition Time = 5500, TR = 5500 ms PE Dir COL COL AutoAlign matrix detected AutoAlign Matrix --------------------- 1.000 0.000 0.000 0.000; 0.000 1.000 0.000 0.000; 0.000 0.000 1.000 0.000; 0.000 0.000 0.000 1.000; FileName /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm Identification NumarisVer syngo MR B17 ScannerModel TrioTim PatientName S790_P72_TC Date and time StudyDate 20120502 StudyTime 120741.390000 SeriesTime 125649.953000 AcqTime 125626.150000 Acquisition parameters PulseSeq ep_b0 Protocol DTI PhEncDir COL EchoNo 1 FlipAngle 90 EchoTime 93 InversionTime -1 RepetitionTime 5500 PhEncFOV 220 ReadoutFOV 220 Image information RunNo 6 SeriesNo 7 ImageNo 1 NImageRows 896 NImageCols 896 NFrames 42 SliceArraylSize 40 IsMosaic 1 ImgPos 113.4156 146.2544 -24.5854 VolRes 1.7188 1.7188 3.0000 VolDim 128 128 40 Vc -0.9983 0.0151 -0.0555 Vr -0.0000 -0.9648 -0.2629 Vs -0.0575 -0.2625 0.9632 VolCenter 0.1478 26.0388 -1.8116 TransferSyntaxUID 1.2.840.10008.1.2.1 sagrev = 0, correv =0, trarev = 0 Vs = -0.0574949 -0.262456 0.963229 INFO: no Siemens slice order reversal detected (good!). TR=5500.00, TE=93.00, TI=-1.00, flip angle=90.00 i_ras = (-0.998346, 0.0151149, -0.0554726) j_ras = (-3.31902e-08, -0.964826, -0.262891) k_ras = (-0.0574949, -0.262456, 0.963229) writing to /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig.nii.gz... mri_probedicom --i /home/canlilab/SBU/data/dti_dicoms/00075/data_1.dcm > /home/canlilab/SBU/data/tracula/00075/dmri/dcminfo.dat flip4fsl /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig.nii.gz /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig_flip.nii.gz INFO: input image orientation is LPS INFO: input image determinant is 8.86231 fslswapdim /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig.nii.gz x -y z /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig_flip.nii.gz INFO: left-right orientation was flipped by fslswapdim fslorient -forceradiological /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig_flip.nii.gz mv -f /home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig_flip.mghdti.bvecs /home/canlilab/SBU/data/tracula/00075/dmri/bvecs mv: cannot stat `/home/canlilab/SBU/data/tracula/00075/dmri/dwi_orig_flip.mghdti.bvecs': No such file or directory Linux 112-205.psy.sunysb.edu 2.6.32-279.5.2.el6.x86_64 #1 SMP Fri Aug 24 01:07:11 UTC 2012 x86_64 x86_64 x86_64 GNU/Linux trac-preproc exited with ERRORS at Fri Dec 7 10:42:51 EST 2012 ============================================== config file: ============================================== # # dmrirc.example # # This file contains commands that will be run by trac-all before an analysis. # It is used to set all parameters needed for the analysis. # # Remove a parameter from your dmrirc file if you want use the default value. # Parameters that don't have default values must be specified. # # Any other commands that you might want to run before an analysis can be added # to this file. # # Original Author: Anastasia Yendiki # CVS Revision Info: # $Author: ayendiki $ # $Date: 2011/05/24 06:47:12 $ # $Revision: 1.3.2.3 $ # # Copyright © 2011 The General Hospital Corporation (Boston MA) "MGH" # # Terms and conditions for use reproduction distribution and contribution # are found in the 'FreeSurfer Software License Agreement' contained # in the file 'LICENSE' found in the FreeSurfer distribution and here: # # https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense # # Reporting: freesurfer(a)nmr.mgh.harvard.edu # # # FreeSurfer SUBJECTS_DIR # T1 images and FreeSurfer segmentations are expected to be found here # setenv SUBJECTS_DIR /path/to/recons/of/ducks # Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = $pre_data/tracula # Subject IDs # set subjlist = (00075 00104 00122 00200 00308 00369 00387 00400 00422 00452 00518 00568 00587 00624 00636 00686 00698 00727 00909 00926 00933 00943 00983 00992 01049 01121 01329 01450 01509 01531 01678 01735 01746 01809 01843 01917 10039 10056 10089 10165 10171 10227 10238 10263 10314 10320 10326 10349 10407 10431 10435 10458 10477 10499 10500 10636 10750 11209 12210 12357 12472 12645 13493 13958 14302 14481 14530 15329 15345 15864 16654 17267 17478 18337 18422 18530 19259 19377 19725 20071 20383 21413 23846 24115 24531 24781 25962 26314 26475 28822 30510 30571 30927 31034 31049 31237 31437 31471 31531 32131 32162 32222) # In case you want to analyze only Huey and Louie # Default: Run analysis on all subjects # set runlist = (1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102) # Input diffusion DICOMs (file names relative to dcmroot) # If original DICOMs don't exist these can be in other image format # but then bvecfile bvalfile and nb0 must be specified (see below) # set dcmroot = $pre_data/dti_dicoms set dcmlist = (00075/data_1.dcm \ 00104/data_1.dcm \ 00122/data_1.dcm \ 00200/data_1.dcm \ 00308/data_1.dcm \ 00369/data_1.dcm \ 00387/data_1.dcm \ 00400/data_1.dcm \ 00422/data_1.dcm \ 00452/data_1.dcm \ 00518/data_1.dcm \ 00568/data_1.dcm \ 00587/data_1.dcm \ 00624/data_1.dcm \ 00636/data_1.dcm \ 00686/data_1.dcm \ 00698/data_1.dcm \ 00727/data_1.dcm \ 00909/data_1.dcm \ 00926/data_1.dcm \ 00933/data_1.dcm \ 00943/data_1.dcm \ 00983/data_1.dcm \ 00992/data_1.dcm \ 01049/data_1.dcm \ 01121/data_1.dcm \ 01329/data_1.dcm \ 01450/data_1.dcm \ 01509/data_1.dcm \ 01531/data_1.dcm \ 01678/data_1.dcm \ 01735/data_1.dcm \ 01746/data_1.dcm \ 01809/data_1.dcm \ 01843/data_1.dcm \ 01917/data_1.dcm \ 10039/data_1.dcm \ 10056/data_1.dcm \ 10089/data_1.dcm \ 10165/data_1.dcm \ 10171/data_1.dcm \ 10227/data_1.dcm \ 10238/data_1.dcm \ 10263/data_1.dcm \ 10314/data_1.dcm \ 10320/data_1.dcm \ 10326/data_1.dcm \ 10349/data_1.dcm \ 10407/data_1.dcm \ 10431/data_1.dcm \ 10435/data_1.dcm \ 10458/data_1.dcm \ 10477/data_1.dcm \ 10499/data_1.dcm \ 10500/data_1.dcm \ 10636/data_1.dcm \ 10750/data_1.dcm \ 11209/data_1.dcm \ 12210/data_1.dcm \ 12357/data_1.dcm \ 12472/data_1.dcm \ 12645/data_1.dcm \ 13493/data_1.dcm \ 13958/data_1.dcm \ 14302/data_1.dcm \ 14481/data_1.dcm \ 14530/data_1.dcm \ 15329/data_1.dcm \ 15345/data_1.dcm \ 15864/data_1.dcm \ 16654/data_1.dcm \ 17267/data_1.dcm \ 17478/data_1.dcm \ 18337/data_1.dcm \ 18422/data_1.dcm \ 18530/data_1.dcm \ 19259/data_1.dcm \ 19377/data_1.dcm \ 19725/data_1.dcm \ 20071/data_1.dcm \ 20383/data_1.dcm \ 21413/data_1.dcm \ 23846/data_1.dcm \ 24115/data_1.dcm \ 24531/data_1.dcm \ 24781/data_1.dcm \ 25962/data_1.dcm \ 26314/data_1.dcm \ 26475/data_1.dcm \ 28822/data_1.dcm \ 30510/data_1.dcm \ 30571/data_1.dcm \ 30927/data_1.dcm \ 31034/data_1.dcm \ 31049/data_1.dcm \ 31237/data_1.dcm \ 31437/data_1.dcm \ 31471/data_1.dcm \ 31531/data_1.dcm \ 32131/data_1.dcm \ 32162/data_1.dcm \ 32222/data_1.dcm) # Diffusion gradient table # Must be specified if inputs are not MGH DICOMs # Three-column format one row for each volume in the diffusion data set # Default: Read from DICOM header # #set bvecfile = $pre_data/DTI_Analysis/00075/bvecs.txt # Diffusion b-value table # Must be specified if inputs are not MGH DICOMs # Single-column format one value for each volume in the diffusion data set # Default: Read from DICOM header # #set bvalfile = $pre_data/DTI_Analysis/00075/bvals.txt # Number of low-b images # Must be specified if inputs are not DICOM # Default: Read from DICOM header # set nb0 = 1 # Perform registration-based B0-inhomogeneity compensation? # Default: 0 (no) # set dob0 = 0 # Input B0 field map magnitude DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # #set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm) # Input B0 field map phase DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # #set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm) # Echo spacing for field mapping sequence (from sequence printout) # Only used if dob0 = 1 # Default: None # #set echospacing = 0.7 # Perform registration-based eddy-current compensation? # Default: 1 (yes) # set doeddy = 1 # Rotate diffusion gradient vectors to match eddy-current compensation? # Only used if doeddy = 1 # Default: 1 (yes) # set dorotbvecs = 1 # Fractional intensity threshold for BET mask extraction from low-b images # This mask is used only if usemaskanat = 0 # Default: 0.3 # set thrbet = 0.5 # Perform diffusion-to-T1 registration by flirt? # Default: 1 (yes) # set doregflt = 1 # Perform diffusion-to-T1 registration by bbregister? # Default: 0 (no) # set doregbbr = 0 # MNI template (the only option for inter-subject registration in this version) # Default: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # set mnitemp = $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # Use brain mask extracted from T1 image instead of low-b diffusion image? # Has no effect if there is no T1 data # Default: 1 (yes) # set usemaskanat = 1 # Paths to reconstruct # Default: All paths in the atlas # set pathlist = ( lh.cst_AS rh.cst_AS \ lh.unc_AS rh.unc_AS \ lh.ilf_AS rh.ilf_AS \ fmajor_PP fminor_PP \ lh.atr_PP rh.atr_PP \ lh.ccg_PP rh.ccg_PP \ lh.cab_PP rh.cab_PP \ lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) # Number of path control points # Default: 5 # set ncpts = 5 # List of training subjects # This text file lists the locations of training subject directories # Default: $FREESURFER_HOME/trctrain/trainlist.txt # set trainfile = $FREESURFER_HOME/trctrain/trainlist.txt # Use long (more descriptive) directory hierarchy for saving path distributions? # By default paths distributions are saved directly under $subjectname/dpath # Default: 0 (no) # set dopathsubdirs = 0 # Number of MCMC burn-in iterations # (Path samples drawn initially by MCMC algorithm and discarded) # Default: 200 # set nburnin = 200 # Number of MCMC iterations # (Path samples drawn by MCMC algorithm and used to estimate path distribution) # Default: 5000 # set nsample = 5000 # Frequency with which MCMC path samples are retained for path distribution # Default: 5 (keep every 5th sample) # set nkeep = 5 ==============================================

2 1

regarding longitudinal segmentations
by Prasanna M 07 Dec '12

07 Dec '12

Hi, Do I work with aseg.fused.mgz or do I work with aparc files to get the most accurate segmentation of brain structures? I tried using freeview to look at segmentations. Is there a way to isolate mgz files of very specific structures. I mean, is there an inbuilt tool which can do that for me, or would I have to go image by image, and threshold to get, say the hippocampus? Thanks, Prasanna -- -- Let there be peace on earth... And let it begin with me.

2 1

long_mris_slopes error
by Alexandru Hanganu 07 Dec '12

07 Dec '12

Dear Freesurfer Experts, While performing the long_mris_slopes cmd, we receive an error: ------------------------------------------- SUBJECT pls_pd_base00 Running Within-Subject GLM Writing ./tmp-pls_pd_base00_lh_thickness_Rx7HLs/X-long.mat ... mri_glmfit --y /Applications/freesurfer/subjects/pls_pd_base00/surf/lh.long.thickness-stack.mgh --X ./tmp-pls_pd_base00_lh_thickness_Rx7HLs/X-long.mat --allow-zero-dof --no-contrasts-ok --surf pls_pd_base00 lh --label /Applications/freesurfer/subjects/pls_pd_base00/label/lh.long.cortex.label --glmdir ./tmp-pls_pd_base00_lh_thickness_Rx7HLs/glm Traceback (most recent call last): File "/Applications/freesurfer/bin/long_mris_slopes", line 732, in <module> run_cmd(cmd,'mri_glmfit did not work?') File "/Applications/freesurfer/bin/long_mris_slopes", line 422, in run_cmd retcode = subprocess.call(args) File "/System/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/subprocess.py", line 443, in call return Popen(*popenargs, **kwargs).wait() File "/System/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/subprocess.py", line 593, in __init__ errread, errwrite) File "/System/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/subprocess.py", line 1079, in _execute_child raise child_exception OSError: [Errno 8] Exec format error ------------------------------------------- we changed the mris_calc and mri_glmfit files with the new ones (8.2MB / 15.8MB). Can you please help with this ? Thank you in advance, Sincerely, Alex.

4 29

fixed version of mri_glmfit and mri_glmfit-sim
by Knut J Bjuland 07 Dec '12

07 Dec '12

Hi I saw an updated version of mri_glmfit in ftp://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/misc/linux-centos4_x86…. Should I update this file in my freesurfer installation. What do this fix? Is is not in the release notes? Along with mri_glmfir-sim from Doug Greve ftp site. Knut J

2 1

R: Re: R: Re: R: Re: different MRI size
by Andrea Tateo 07 Dec '12

07 Dec '12

This is my dataset.I don't know another Tenx ----Messaggio originale---- Da: fischl(a)nmr.mgh.harvard.edu Data: 7-dic-2012 17.09 A: "Andrea Tateo"<andrea.tateo(a)virgilio.it> Ogg: Re: R: Re: R: Re: [Freesurfer] different MRI size why do you have different matrix sizes? And I can't tell resolution without knowing the FOV. Ideally no voxel dimension is bigger than about 1.3-1.4mm. cheers Bruce p.s. please cc the list so that others can answer! On Fri, 7 Dec 2012, Andrea Tateo wrote: > > > My data set has two types of MR-images: > first 256, 124, 256 > > second 181, 145, 181 > > > > > > > ----Messaggio originale---- > Da: fischl(a)nmr.mgh.harvard.edu > Data: 7-dic-2012 15.56 > A: "Andrea Tateo"<andrea.tateo(a)virgilio.it> > Ogg: Re: R: Re: [Freesurfer] different MRI size > > Sure. What are the voxel sizes? > > On Dec 7, 2012, at 9:52 AM, Andrea Tateo <andrea.tateo(a)virgilio.it> > wrote: > > Thank you Bruce for answering. > > > My images have size: 256, 124, 256 and 181, 145, 181 > I would like to have FS-output with same input's size to evaluate > DICE index. > > (The manual segmentation of comparison are images with sizes 256, > 124, 256 and 181, 145, 181) > > Thank you for your time > > > > > > > ----Messaggio originale---- > Da: fischl(a)nmr.mgh.harvard.edu > Data: 7-dic-2012 14.20 > A: "Andrea Tateo"<andrea.tateo(a)virgilio.it> > Cc: <Freesurfer(a)nmr.mgh.harvard.edu> > Ogg: Re: [Freesurfer] different MRI size > > Hi Andrea > > sure, recon-all will handle whatever you give it and resample to > 256^3, > but the accuracy will degrade as the resolution worsens. What > resolution > are your images? > > cheers > Bruce > On Fri, 7 Dec > 2012, Andrea Tateo wrote: > > > Hello FS experts, > > I would like to study MRI, by using FS, to extract binary images > of > > hippocampi. > > > > My data images have sizes different from 256,256,256. > > > > Is there any FS procedure to generate images 256, 256, 256 from > my data > > set? > > > > Does FS work only on images 256, 256, 256 ? > > > > Thank you very much > > > > Andrea > > > > > > > The information in this e-mail is intended only for the person to > whom it is > addressed. If you believe this e-mail was sent to you in error and > the e-mail > contains patient information, please contact the Partners > Compliance HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to > you in error > but does not contain patient information, please contact the sender > and properly > dispose of the e-mail. > > > > > >

1 0

different MRI size
by Andrea Tateo 07 Dec '12

07 Dec '12

Hello FS experts, I would like to study MRI, by using FS, to extract binary images of hippocampi. My data images have sizes different from 256,256,256. Is there any FS procedure to generate images 256, 256, 256 from my data set? Does FS work only on images 256, 256, 256 ? Thank you very much Andrea

3 2

How can I get 300 dpi resolution or about when saving tiff files in tksurfer?
by Knut J Bjuland 07 Dec '12

07 Dec '12

How can I get 300 dpi resolution or about when saving tiff files in tksurfer?

4 5

De-trending resting state data
by Chris McNorgan 06 Dec '12

06 Dec '12

I'm working with raw timeseries waveform data from a resting state dataset. There's one run per participant, so no between-run normalization is required. With Doug's help, I have mastered extracting the timeseries information from individual ROIs in surface space. The timeseries data is the product of the preproc-sess script, to which I additionally added the -inorm parameter to extract the mean timeseries for the entire volume. One concern I had was that correlations between timeseries from different ROIs might be artificially inflated by scanner drift. For example, if the overall signal increases or decreases as a function of time over the course of the run or fluctuates periodically, then for any two regions, their signals will go up or down together as a result. This should in turn make the timeseries from these two regions more correlated. I have no strong evidence that scanner drift is a particular problem in my dataset, but it seems likely enough in a 6 minute run that I want to mitigate the problem. I can't really put these data through selxavg, as I do not have a model to fit for resting state data, and in any case would like to work with the timeseries itself, rather than residuals or any other by-product of an HRF model. I was wondering if simply subtracting the global mean signal waveform from each ROI waveform would be a reasonable strategy for removing drift, or if there are any standalone commands that are called by some of the super-scripts that I can use without carrying out a first level analysis. Thanks, Chris

1 0

update mri_glmfit
by Knut J Bjuland 06 Dec '12

06 Dec '12

1 0

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Freesurfer December 2012