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30132 discussions

Per-run registration in monkey fmri
by SHAHIN NASR 26 Jan '12

26 Jan '12

Hi In monkey fmri, we usually have to do the functional-anatomical registration manually since bbregister doesn't work very well. It was easy in version 4.5 since we usually used just one run for registration. In version 5.1 we can use per-run option to improve registration but I am wondering how can we use it in monkey fmri? Should we register each run separately? Then how can we combine those individual registration files to generate the final registration file.

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vertex extraction
by Alexander Lebedev 26 Jan '12

26 Jan '12

Dear Freesurfer users and developers! I am trying to extract vertices by the script suggested by Pedro Paulo de M. Oliveira Jr. for i in `seq 1 $numsubject` do mri_convert --frame $i y.mgh $FSAVERAGE_DIR/surf/lh.$i.mgh mris_convert -c $i.mgh $FSAVERAGE_DIR/surf/lh.inflated lh.$i.asc done It works fine, but I'm just wondering if there is an easy opportunity to combine the vertices from each subject into one big N-by-V matrix (N - subject № , V- vertex №). Thank you very much for your time! -- Regards, Alexander Lebedev Stavanger University Hospital, Norway

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qdec analysis
by Cat Chong 26 Jan '12

26 Jan '12

Hello Experts: In qdec, I would like to analyze if the thickness- entorhinal volume correlation differs between APOE positive and negative subjects accounting for age. I have chosen age as a nuisance variable and entorhinal volume as a covartiate. ( hope this is correct) Results show me several clusters of group differences following FDR correction. Here is my question: I would like to know what the mean value differences are between the groups for various clusters. And I would also like to know which group shows less of a correlation for certain clusters. Could you tell me where I would find those values? Best Regards, Catherine

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qdec and analysis results
by Cat Chong 26 Jan '12

26 Jan '12

Hello Experts, I am analyzing cortical thickness of two groups (APOE 4carries vs. noncarriers) in freesurfer with age as a co-variate. I am uncertain about the statistical differences behind: 1)Does the correlation between thickness and age accounting for APOE, differ from 0? in contrast to: 2)Does the thickness-age correlation differ between 1 and 0? I get significant results after FDR correction for the first but not for the second analysis. thank you for your help, catherine

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cwp threshold
by Antonella Kis 26 Jan '12

26 Jan '12

Dear all, I would like to know if while doing a group analysis for my FA data and I do the correction by multiple comparisons for the clusters: mri_glmfit-sim \ --glmdir Group_Analysis.glmdir \ --grf 3 neg \ --cwpvalthresh .05 \ --overwrite what exactly is defined the cwpvalthresh. I mean I know that this is a cluster-wise p-value threshold but is this calculated from or based on the FA intensity values? Thank you very much for your clarification. Antonella

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mri_glm with grf over CVS space
by Antonella Kis 26 Jan '12

26 Jan '12

Hi, I would like to do a Group Analysis for my FA data. For all my subjects (controls + patients) I have all the fa-masked.ANAT+CVS-to-avg35.mgz (the resampled registered image to CVS space). I am not sure if my steps for the Group Analysis are the right one: 1) Concatenate FA from individuals into one file mri_concat */fa-masked.ANAT+CVS-to-avg35.mgz --o GroupAnalysis-fa-masked.ANAT+CVS-to-avg35.mgz 2) Make sure the order agrees with the fsgd: ls -l */fa-masked.ANAT+CVS-to-avg35.mgz 3) Create average of the input images for visualization: mri_average */fa-masked.ANAT+CVS-to-avg35.mgz GroupAnalysis.Average-fa-masked.ANAT+CVS-to-avg35.mgz 4) mri_glmfit --y GroupAnalysis-fa-masked.ANAT+CVS-to-avg35.mgz \ --fsgd Group_Analysis.fsgd dods --C contrast.mtx --glmdir Group_Analysis.glmdir To view uncorrected results: tkmedit fsaverage orig.mgz -aparc+aseg -overlay /media/1Tra/Antonella_2011/DTI_FS/dtrecon/Group_Analysis.glmdir/contrast/sig.mgh -fminmax 2 3 where -fminmax 2 3 : Voxel-wise threshold of 2 (p<.01), color saturation at 3 (p<.001) To correct for multiple comparisons run: 5) mri_glmfit-sim \ --glmdir Group_Analysis.glmdir \ --grf 3 neg \ --cwpvalthresh .0166 \ --overwrite Is 3 (p<.001) neg as well as .0166 (.05/3) the best voxel-wise as well as cluster wise values to be tried? To view corrected results: tkmedit fsaverage orig.mgz \ -ov /media/1Tra/Antonella_2011/DTI_FS/dtrecon/group_analysis.glmdir/contrast/grf.th3.pos.sig.cluster.mgh \ -seg /media/1Tra/Antonella_2011/DTI_FS/dtrecon/group_analysis.glmdir/contrast/grf.th3.pos.sig.ocn.anat.mgh \ /media/1Tra/Antonella_2011/DTI_FS/dtrecon/group_analysis.glmdir/contrast/grf.th3.pos.sig.ocn.lut \ -fminmax 1.3 5 with only positive values * p-values are p-values for cluster * Set threshold to 1.3 (p<.05) Are this steps right? When I run the registration over the cvs space which file is the equivalent file of the fa-tal.nii.reg from the TAL space? MANY THANKS! Antonella

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building a pipeline using mri_robust_register and other FS functions
by Mehul Sampat 25 Jan '12

25 Jan '12

Hi Folks, We have subjects with high lesion load which changes significantly over time. I want to use FS functions to build a pipeline for comparing lesion changes in two time-points of the same-subject. I am thinking of using the following steps; 1) Use mri_normalize to normalize the two time-points. 2) Use mri_robust_register to register two time-points of the same subject to half-way space. 3) Use mri_skull_strip 4) Use subtraction imaging or some other techniques to look for lesion changes. My questions are: 1) I think I need mri_normalize since the output from mri_robust_register is not intensity normalized ? 2) Instead of the first three steps, I could also do the following: Run autorecon1 for both timepoints and then run mri_robust_register on the skull stripped images Does it matter if we run mri_robust_register before or after skull stripping ? Thanks Mehul

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One Position for Medical Imaging Specialist in MRI at Barrow Neurological Institute, Phoenix (Masters Degree in Computer Engineering or Equivalent)
by Jie Shi 25 Jan '12

25 Jan '12

*Overview:* The Barrow Neurological Institute (BNI), in Phoenix, has a world-class imaging research team that produces next-generation methods for Magnetic Resonance Imaging (MRI). We primarily develop new algorithms for collecting MRI data and reconstructing images, using signal processing and other mathematics along with knowledge of physics and medicine. We are working to change the standard for routine clinical MRI, making it better for every patient and more cost effective for global healthcare. We are looking for a motivated individual who will work with our researchers to perform a variety of computer-engineering tasks, including algorithm implementation, program optimization, GPU programming, creation of a visual programming environment for MR research, and IT administration. *Position Information:* The individual will help the faculty to develop algorithms and write efficient code (in C/C++), and port computationally intensive algorithms to a GPU. Over time, they will re-engineer a visual programming environment for our team, to facilitate the development environment and product (final code) creation. They will be responsible for all of the (primarily Linux) computers in the center and related administrative functions, which should average no more than 10% of the job. The individual will have the opportunity to travel each year to the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), which will be in Melbourne, Australia, in May 2012. The person in this position will report to Jim Pipe, Ph.D., Director of Neuroimaging Research at the BNI. Requirements include: *•* a Masters degree in computer science, computer engineering, or related fields • high-level experience with C/C++ programming, memory management, etc. • training/experience with GPU programming • training/experience with GUI programming • Linux administrator skills • some training in signal processing and linear algebra • strong interpersonal and communication skills • creativity and a desire to learn • ability to write well and present thoughts clearly *Facilities and Environment:* Work will be performed in the Keller Center for Imaging Innovation at the BNI in Phoenix, Arizona. The Keller Center was built to bring together clinical, academic, and industry expertise to produce robust clinical imaging technology and bring it to market. Towards that end, it houses BNI faculty and staff, Arizona State University (ASU) engineering faculty and students, and (soon) a Philips clinical scientist. This Keller Center has a 3 Tesla research MRI scanner dedicated to this effort. The BNI performs more neurosurgeries under one roof than anywhere in the world, and is located within St. Joseph’s Hospital, the largest hospital in Arizona. It has the largest neuroradiology fellowship program and the largest neurosurgery residency program in the United States. Its academic partner for imaging research, ASU, has a vibrant and growing program in MR imaging. In addition to the research 3T MRI, the BNI has 7 diagnostic MRI scanners as well as the first 3T intra-op MRI installed in a hospital. The BNI also houses a preclinical imaging facility with a 7T Bruker Biospec MR scanner, which is run jointly with ASU. *Application Information:* Interested candidates should send their CV to Jim Pipe at jim.pipe(a)thebni.org.

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group wrfx error: MRInormWeights: value less than or eq to 0.
by Finnegan Calabro 25 Jan '12

25 Jan '12

Hi Doug et al, We're running into a problem doing a wrfx group analysis using FreeSurfer 4.5. Looks like the glmfit is having trouble with the weights. We tried running it for different subsets of our subjects, and get the same thing, so it's not one subject contaminating the analysis. Any guess where we should look for tracking this down? Presumably the cesvar files are screwed up somehow, which would imply something went wrong in the individual selxavg3's. But any ideas about what could lead to this? The individual overlays all seem to look ok. [sidkundu@brodmann all-vs-null]$ mri_glmfit --y tal.ces.nii --osgm -- glmdir tal.wrfx.osgm --nii --mask ../tal.mask.nii --wls tal.cesvar.nii $Id: mri_glmfit.c,v 1.138.2.15 2009/07/27 17:43:10 greve Exp $ cwd /home/sidkundu/freesurfer/sessions/Heading-TTP/1-24-12-TTP/ TTP_analysis/all-vs-null cmdline mri_glmfit --y tal.ces.nii --osgm --glmdir tal.wrfx.osgm --nii --mask ../tal.mask.nii --wls tal.cesvar.nii sysname Linux hostname brodmann.bu.edu machine x86_64 user sidkundu FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 1 y /home/sidkundu/freesurfer/sessions/Heading-TTP/1-24-12-TTP/ TTP_analysis/all-vs-null/tal.ces.nii logyflag 0 usedti 0 mask ../tal.mask.nii maskinv 0 glmdir tal.wrfx.osgm IllCondOK 0 DoFFx 0 wFile tal.cesvar.nii weightinv 1 weightsqrt 1 Creating output directory tal.wrfx.osgm Loading y from /home/sidkundu/freesurfer/sessions/Heading-TTP/1-24-12- TTP/TTP_analysis/all-vs-null/tal.ces.nii Saving design matrix to tal.wrfx.osgm/Xg.dat Matrix condition is 1 Pruning voxels by thr: 0.000000 Found 260736 voxels in mask Saving mask to tal.wrfx.osgm/mask.nii search space = 2.08589e+06 ERROR: MRInormWeights: value less than or eq to 0. c=34, r=2, s=33, v=0

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hippocampal subfield segmentation
by Joana Braga Pereira 25 Jan '12

25 Jan '12

Dear Van Leemput & Freesurfers, I've been working with the GEMS tool for hippocampal subfields segmentation and have a question I would like to ask you. I've noticed that the CA1 sector appears to be quite small compared to other subfields (CA2-3 seems to be the largest), while previous studies suggest that it should be the largest subfield of the hippocampus. Why? On the other hand, I have read the paper by Van Leemput et al. [2009] and how the subfields were initially delineated and was wondering what atlas was the manual tracing based on? Many thanks! Joana

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