Freesurfer

freesurfer@nmr.mgh.harvard.edu

3 participants
30135 discussions

geodesic distance between vertices
by macara.p＠libero.it 15 Apr '16

15 Apr '16

Dear all, I need to compute the geodesic distance (i.,e. the distance on the cortical surface) between a series of vertices. Specifically, I want to compute the distance between all the vertices in the right hemisphere (>100000) and a series of vertices of interest (around 20000), always located in the right hemisphere. Ideally, my output should be a matrix right_verticesXvertices_of_interest where in each entry there is the geodesic distance between the pair of vertices. And I need to do this for hundreds of subjects! I tried to use mris_pmake: for i = right_vertices; for j=vertices_of_interest; command1 = ['/Applications/freesurfer_v5.3/bin/mris_pmake --subject 001 --hemi rh --surface0 white --curv0 sulc --curv1 sulc --mpmOverlay euclidean --mpmProg pathFind --mpmArgs startVertex:'num2str(i) ',' 'endVertex:' num2str(j)] T(i)=evalc('system([command1])') endend But the problem is that it is way too much time consuming and it is difficult to store the results in a matrix. Is there a way to adapt this command for such high computations? I Are there other ways to obtain the geodesic distance between vertices, either than mris_pmake (for example a matlab function)? Thank you in advance,Marica

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LME for functional data, 3 factors
by Laura Rueda Delgado 15 Apr '16

15 Apr '16

Dear FreeSurfers and LME experts, I've just started using the LME toolbox by Bernal-Rusiel et al (2012, 2013) in Matlab, apart from FreeSurfer. My experimental design includes one between-subjects factor (group with two levels, 24 vs 22 subjects), and two within-subjects (WS) factors (day with two levels, and condition with three levels). As far as I understand, the LME toolbox can be used for longitudinal data and for investigating modulations of neural activity with behavioral measures. However, it's been difficult for me to set up both the design matrix and the input of the LME functions given three fixed factors in my design (I haven't included behavior yet). So I have a few questions that I hope you can help me answer. I follow these steps: 1) Following the wiki, I've created a pre-design matrix, M, with: First column: Day factor coded as 0 (first day) and 7 (7 days later, as during acquisition). Second column: Group factor binary coded. Third column: Condition factor coded with dummy variables 1 to 3 (three conditions in total). I don't know if this is correct; I have failed to find in the mailing list any reference to an additional repeated measure besides time in LME models. >From here, I've created the design matrix X adding a column of 1's for the intercept, adding the pre design matrix M, and adding columns for every possible interaction by multiplying element-wise the columns of M (including two-way and three-way interactions). 2) I then use lme_mass_fit_vw, but I have doubts about some inputs Zcols: column 1 in X ni: created a vector with 6 for all subjects (in total there are 6 repeated measures, 2 of day and 3 of condition) I kept the others as default. 3) lme_mass_F for statistics of fixed effects. I check main effects and interactions with contrasts based on the design matrix X: placing a 1 in the column position corresponding to each effect of interest. 4) lme_mass_FDR2 for correcting the F stats for multiple comparisons. I would like to compare the results using AlphaSim's Monte-Carlo simulations, however I'm not sure what image to use to estimate the smoothness. Would you have a suggestion? Could you please let me know if this procedure is sound? Also, if I'm interested in specific comparisons or post-hoc tests, e.g. Group 1$Day 1$Condition 3 vs. Group 1$Day 2$Condition 3, would I need a separate model for that? And lastly, for the analysis including the behavioral measures, should I just include them as a forth column in the pre-design matrix M and add the relevant effects in X? I hope I haven't overwhelmed you with so many questions. I would greatly appreciate any suggestion you can provide. Best regards, Laura Rueda

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Extract random effects (LME) for correlation
by Lim Kai Wei, Joseph 15 Apr '16

15 Apr '16

Dear FS experts, I used the spatiotemporal LME MATLAB toolbox to fit to my longitudinal data and performed some ad-hoc tests using the FDR2 correction. I obtained some significant vertices on the fixed effect of slope (rate of change of cortical thickness - i.e. beta coefficient on time was significant). I would like to proceed to show if individual variability in rate of change of cortical thickness - i.e. beta + random coefficient on time is correlated with any clinical and cognition measures. May I know how can this be done? I do not seem to be able to do so from the output of lme_mass_fit_Rgw. Thank you, Joseph ________________________________ Important: This email is confidential and may be privileged. If you are not the intended recipient, please delete it and notify us immediately; you should not copy or use it for any purpose, nor disclose its contents to any other person. Thank you.

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Re: [Freesurfer] axial or sagittal?
by Gamaliel Huerta Urrea 15 Apr '16

15 Apr '16

2016-04-08 12:09 GMT-03:00 Gamaliel Huerta Urrea <gamaliel.huerta(a)uv.cl>: > Hello Freesurfers > > I'm trying to do volumetric reconstruction, i already have done this but. > i wanted to see if for the same subject, with different directions of > slices i have the same result, and obviously i had different results of > volumetric measures. i have used the sequence FSPGR T1 ponderation, with > the same parameters for both data acquisition; with voxel 1 mm3, but the > direction is changed in both studies axial and sagittal. Both studies are > in *.dcm format, and i used the next command for convert dicom to .mgz and > after to apply recon-all process: > > > 1. > > mri_convert firstdicom.dcm 001.mgz > > I would like to know why would i have this problem? > > There is some specific slice direction for this kind of study which is > suitable ? > > I'm interested in cerebellum volumetric information. I attached my results > for volumetry. > > Cheers > > -- > *Gamaliel Huerta* > *Ingeniero Civil Biomédico* > *HealthCare Analytics* > *Centro Interdisciplinario de Innovación en Salud* > *Universidad de Valparaíso* > > -- *Gamaliel Huerta* *Ingeniero Civil Biomédico* *HealthCare Analytics* *Centro Interdisciplinario de Innovación en Salud* *Universidad de Valparaíso*

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Problem in one hemisphere
by Anna Zubik 15 Apr '16

15 Apr '16

Dear Experts, I'm an absolute beginner in using FreeSurfer. After I performed recon-all I had a problem. I used T1 SPGR scans to did the reconstruction. Left hemisphere is done well but right one has some deficits. I attached some print screens bellow. What can I do to solve my problem? Best regards, Anna

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Postdoctoral Position in Infant Neuroimaging Analysis at UNC-Chapel Hill
by gangli 14 Apr '16

14 Apr '16

Postdoctoral research associate positions are available at the Infant Brain Mapping Lab in the Department of Radiology and Biomedical Research Imaging Center at the University of North Carolina at Chapel Hill (UNC-Chapel Hill). Our current focuses are to better understand the dynamic and critical early brain development in both health and disease, via multimodal neuroimaging analysis. The successful candidate will support our efforts either in advancing neuroimaging analysis technologies or in neuroscience applications. We are seeking highly motivated individuals who have extensive research experience in neuroimaging analysis (e.g., structural, diffusion or functional MRI) and demonstrated academic excellence, including publications in first-class journals and conferences. The candidate should have a Ph.D. (or equivalent) in Neuroscience, Computer Science, Applied Mathematics/Statistics, Electrical Engineering, Biomedical Engineering, or related fields. Good command of programming tools including Matlab, C++, Linux, and scripting are necessary to carry out the research work in this group. The successful candidate will be part of a diverse group including neuroscientists, radiologists, psychologists, physicists, biostatistician, and computer scientists, and will build upon the group's extensive foundation on neuroimaging analysis. If interested, please email resume to Dr. Gang Li (gang_li(a)med.unc.edu <https://outlook.unc.edu/owa/redir.aspx?SURL=r-ilRGXKGkv6jOyzGz3FjAPbDG9VNfM…> ). For more information, please visit: http://www.unc.edu/~gangl/contact.html <https://outlook.unc.edu/owa/redir.aspx?SURL=HcVZFV_dUot9RdWseT8XGNDO-UYKJHN…> .

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Re: [Freesurfer] mri_surfcluster issues
by Antonin Skoch 14 Apr '16

14 Apr '16

Dear Doug, thank you for the clarification. So to make sure, do you use this group_avg_area for each vertex also for building null distribution of maximal cluster size? The area values in .csd files are values of group_avg_area of cluster with maximal group_avg_area in each simulation step? Looking to the source codes it seems to me that the "actual area" of vertices (named "area" in source codes) in fsaverage surfaces is by default not used for anything. This would be reason why in cluster reports from p-map surface overlays originally generated by PALM but clustered by mri_surfcluster the p-values of clusters were not monotonous with respect to cluster area. There was cluster with larger area, but larger p, than other cluster, which had smaller area, but also smaller p. Which was one of the reasons why I digged onto this issue. Now I am at least getting correspondence between PALM and mri_surfcluster cluster sizes. I "detached" lh.white from lh.white.avg.area.mgh (this file is implicitly read by mri_surfcluster), then I got values differing only by global scaling factor corresponding to the ratio of group_avg_surf_area/total_area. PALM does not do this correction. Apparently there is quite large region-specific variability in scaling factor between area and group_avg_area of vertices due to the fact that some regions of brain are less spatially variable (less small individual-specific folds), therefore their average surface does not lose too much folding. Is this effect also accounted for in smoothing on average surface, i.e. are the group average distance between vertices used for smoothing instead of actual positions of the vertices? This would require storing this info somewhere which does not seem to me. If the real positions and distances of vertices in average surfaces are used for smoothing, but group_avg_area is used for cluster extent inference, then this effect would in fact introduce variance of spatial smoothness of data, which could have consequences to statistical inference, wouldn't it? How this is handled? I have several related questions: Reading source code I came across term "ripped" faces/vertices. It seems to me that these faces/vertices are effectively excluded from computations. What does this term mean? Are there any "ripped" vertices in fsaverage subject surfaces? Do you also generate file corresponding to avg.area.mgh in make_average_surfaces? From the code it seems to me that there is only global scaling realized by setting total group_avg_area average surface file. Which values are stored in lh.area of fsaverage? It seems to me that these values do not correspond to area of real lh.white vertices. Thank you for your kind support. Regards, Antonin Skoch It is quite confusing and took us some time to understand ourselves. When we create an average subject like fsaverage, we have to create a triangular mesh just like we do with any subject. This means that we have to assign an xyz to each coordinate. When trying to represent a group, this is not straight forward. The way we do it is to compute the xyz coordinate in MNI305 space for each vertex for each subject, then map that through the spherical registration into the common surface-space for each subject. We then average the MNI305 xyzs across subject to arrive at the xyz for that vertex in the common surface-space. This gives the pial surface of fsaverage its brain-like shape. The problem is that it is much smoother than an individual cortex because all the little folds have been averaged out. This means that the surface has much less surface area than any of the individuals used to make the average (from a surface area standpoint, it is not an average at all!). To be able to report average surface area, we computed the area of each vertex of each subject and mapped this into the common surface space and averaged it across subjects. When we want to report an area for a given region, we compute the sum of the vertex average areas rather than the area of the vertices themselves.On 04/13/2016 11:17 AM, Antonin Skoch wrote: > Dear Doug, > > thank you very much for the support. > > I am quite confused by this info about correcting of area of fsaverage > clusters. Could you please clarify to me the following: > > How exactly the values of lh.area, lh.white.avg.area.mgh of fsaverage were > computed? > > How the cluster area inside mri_surfcluster (and also mri_glmfit and > mri_mcsim) is computed? > > How the correction for average subject is applied? > > I assumed that area belonging to particular vertex is computed by summing > areas of faces originating in particular vertex divided by 3. > The face areas are computed from vertex coordinates by the method (based on > cross-product of vectors) > > http://stackoverflow.com/questions/26312570/calculate-surface-area-of-a-3d-… > > This is how the vertex area from surface file is computed inside PALM. Is the > same method used in freeSurfer? I checked the values of lh.white vertex area > computed in PALM and they do NOT correspond neither to lh.area, nor > lh.white.area.mgh values. > I am listing areas of 3 representative vertices of fsaverage white surface > got from lh.white.avg.area, lh.area and computed from lh.white in PALM: > > vtx 50: lh.white.avg.area 0.4952, lh.area 0.589, PALM 0.5967 > vtx 110: lh.white.avg.area 0.5533, lh.area 0.5198, PALM 0.5421 > vtx 1000: lh.white.avg.area 0.6193, lh.area 0.3349, PALM 0.4305 > > Any clues why there is a difference? > > In cluster summary file, there are flags FixVertexAreaFlag 1 and > FixSurfClusterArea 1. What is the significance of these flags? I tried to set > --no-fix-vertex-area. The FixVertexAreaFlag got value 0, but the vertex areas > produced by mri_surfcluster were the same. > Is it possible to turn off also FixSurfClusterArea? > > I also found flags FixVertexAreaFlag and FixGroupSubjectArea in .csd files. > Are there similar corrections applied to cluster size values in .csd files? > > Thank you in advance for help. > > Regards, > > Antonin Skoch > > > Dear FreeSurfer experts, > > > > I am using mri_surfcluster to compare results from PALM and FreeSurfer > > cluster extent inference. > > > > I came across several issues in mri_surfcluster I cannot cope with. > > > > 1. > > I observed that --mask option in mri_surfcluster does not prevent > > cluster to growing outside the mask. > > > > When I run > > > > mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum > > summary.txt --subject fsaverage --surf white --hemi lh --thmin 0 --ocn > > cluster_summary.mgz --mask ./mask.mgh > > > > the cluster is growing outside mask and covers all surface including > > non-cortical (masked) regions. > > > > Is this intended behavior? This behavior (allowing clusters leak > > outside mask) seems strange to me since it could possibly influence > > results of cluster-extent inference in case of mask-constrained analysis. > This is happening because you are setting the threshold to 0. > > > > 2. > > How behaves mri_glmfit-sim in permutation-based building > > cluster-extent null distribution with mask? Is in this case also > > allowed to clusters to leak outside mask? > No > > > > 3. > > Another issue is concerning reported cluster area. I tested > > mri_surfcluster on data where overlay values > > (lh.thickness.fsaverage.mgh) were set to 0 outside cortex mask (they > > were non-zero also in some portion of non-cortical vertices). When I > > set thmin to non-zero value in mri_surfcluster > > > > mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum > > summary.txt --subject fsaverage --surf white --hemi lh --thmin 0.00001 > > --ocn cluster_summary.mgz --mask ./mask.mgh > > > > I get cluster comprising (almost) all cortex, not leaking to > > non-cortical areas. But what is strange, the reported cluster size is > > far larger than area of the whole cortical surface. > > > > number of voxels in search space = 149953 > > Done loading source values (nvtxs = 163842) > > overall max = 5 at vertex 817 > > overall min = 0 at vertex 8 > > surface nvertices 163842 > > surface area 65417.097656 > > surface area 65416.648438 > > NOT Adjusting threshold for 1-tailed test > > Searching for Clusters ... > > thmin=0.000100 (0.000100), thmax=-1.000000 (-1), thsignid=0, > > minarea=0.000000 > > Found 2 clusters > > Max cluster size 76431.562500 > > Saving cluster numbers to pok.mgz > > > > > > Here is output of mri_surfcluster: > > # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ NVtxs > > 1 5.000 817 76431.56 -25.8 4.6 -37.5 149874 > > 2 2.670 144205 0.53 -15.8 -40.4 -3.8 1 > > > > How it is possible? > The surface area of 65416.648438 has not been corrected for the fact > that fsaverage is an average subject. The area in the summary table has > been corrected. > > > > 4. > > Does the fsaverage/surf/lh.area correspond to area of particular > > vertices on fsaverage/surf/lh.white? I expect yes. > The lh.area file does correspond. However, the area measures on the raw > fsaverage surface are not accurate because the average surface has much > less surface area due to averaging out the individual folding patterns. > > > > 5. > > How it is possible to calculate area of specified vertex of surface? I > > tried to do that by creating .label file with particular vertex number > > and running > > > > mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum > > summary.txt --subject fsaverage --surf white --hemi lh --thmin 0.00001 > > --clabel myVertex.label > > > > I indeed got report of 1 cluster with 1 vertex, with VtxMax value > > identical to vertex index in myVertex.label > > but the area value in summary.txt is different that value in > > fsaverage/surf/lh.area. > You can extract it out of ?h.white.avg.area.mgh using mri_segstats with > the --slabel function. > > > > Thank you very much in advance for clarification, > > > > Antonin Skoch > > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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glmfit and glmfit-sim after built monte-carlo simulation (superiortemporal label)
by Pedro Rosa 14 Apr '16

14 Apr '16

Dear list, I want to limit my vertex-wise group analysis to a smaller spatial region, then I ran my own Monte-Carlo simulation on fsaverage for the superior temporal gyrus: mri_mcsim --o lh/superiortemporal --base mc-z --surface /fsaverage lh --nreps 10000 --label superiortemporal After 36 hours, it finished and I pasted the resulting into $FREESURFER_HOME/average/mult-comp-cor/fsaverage, and ran glmfit: mri_glmfit --glmdir glmdir --y lh.thickness.5.mgh --fsgd table.fsgd --C contrast.mtx --surface fsaverage lh --label /$FREESURFER_HOME/subjects/fsaverage/label/lh.superiortemporal.label It seems it ran only on the label, as expected. However I am not able touse this pre-cached simulation in mri_glmfit-sim. I tried: mri_glmfit-sim --glmdir glmdir --sim-sign abs --cache-dir $FREESURFER_HOME/average/mult-comp-cor/fsaverage/lh/superiortemporal --no-sim mc-z.abs.th13 --cache-label /$FREESURFER_HOME/subjects/fsaverage/label/lh.superiortemporal.label ...but I get an error although the csd files are in /average/mult-comp-cor/fsaverage... path : ERROR: cannot find any csd files I tried several combinations of the text specified after --no-sim, but all I got is the error above. I though I should refer to my own simulation using --cache-dir, but it requires the --no-sim flag. Can anyone help me? Best, Pedro Rosa.

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DLPFC seed
by Raney, Talia L. 14 Apr '16

14 Apr '16

Hi all, I'm trying to do FC connectivity analysis using the dorsalateral prefrontal cortex as a seed. I checked the text file $FREESURFER_HOME/average/colortable_desikan_killiany.txt and it looks like DLPFC is not listed as a possible structure to do the FC analysis. I attempted to use lh-prefrontal and rh-prefrontal but selxavg3-sess fails because there's no volume/label in fsaverage that is associated with it. Any suggestions on what seeds I could use? Would I have to combine or parcellate other existing seeds? Thank you! Best, Talia

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Re: [Freesurfer] Viewing eroded structure after -seg-erode 1
by Elijah Mak 14 Apr '16

14 Apr '16

Thanks Doug. Do you mean loading the structure (i.e. LeftHippocampus2diff.mgz) on the orig.mgz as an overlay? I am able to do that but I can't seem to find an option for thresholding. Sorry if I misunderstood your original email. Best Wishes, Elijah

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