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30063 discussions

Extracting values via mri_segstats from middle of the cortex
by Yash Patel 05 Oct '17

05 Oct '17

Hi, I have a MT and T1W volume (processed through recon-all). I would like to extract all MT values that lie in the middle surface of the cortex, and run that through mri_segstats so I get average MT values for each cortical region in the DK-atlas. My workflow is as follows: 1. mri_vol2suf --mov <MT_vol> --hemi lh --projfrac 0.5 -- regheader <id> --reg <MT_reg_to_T1W.dat> --o <vol2surf.output.mgh> --interp nearest 2. mri_surf2vol --surfval <vol2surf.output.mgh> --hemi lh --fillribbon --identity <id> --template <T1W> --o <surf2vol.output.mgh. 3. mri_segstats --seg <aparc+aseg for subid.mgz> --i <surf2vol.output.mgh> --ctab --id <list of regions> --o <output.stats> Is this the correct way of going about it? In essence I extract all the values of MT found in the middle of the cortex (#1), then I fill the cortex ribbon with these values in the subject's T1W vol (#2), and then run mri_segstats on that output so both aparc+aseg and the output from #2 are in the same space. Is there any other (more efficient) method to do this? Thank you, Yash RRI, Baycrest Health Sciences University of Toronto

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Extract pcc.nii.gz stats using aparc parcellations
by Tori Williams 05 Oct '17

05 Oct '17

Hi Doug, I was wondering if anyone could offer some insight into a freesurfer challenge I've encountered. I have run a resting connectivity analysis using the fsfast stream and now have a pcc.nii.gz map for each subject that is in fsaverage space (and loads just fine using tksurfer with fsaverage). Without mapping back to each subject's native space, is there a way to just use the fsaverage aparc to extract the mean pcc value for each subject by parcellation? I initially tried the following: `mris_anatomical_stats -a fsaverage/label/lh.aparc.annot -t fsaverage/surf/<subject pcc map in fsaverage space> -b fsaverage lh' and got an error that fsaverage/mri/wm.mgz could not be found. I followed an old post on the listerv and just put in a random wm.mgz file from a subject, assuming that anatomical stats wouldn't be calculating any distances if I specified a surface file. After I tried the same command again, I got this error: "ERROR: subject fsaverage is an average subject. mris_anatomical_stats cannot currently be used with an average subject. " Is there a better way to do this that I might be missing? I don't know if it complicates things given that the pcc files are saved as .nii.gz, even though they are surface overlays? Any suggestions would be greatly appreciated! Sincerely, Tori -- Victoria J. Williams, PhD Clinical Research Fellow Interdisciplinary Brain Center Massachusetts General Hospital Harvard Medical School Department of Neurology 149 13th Street Charlestown, MA 02129

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Error feeding design matrix into mri_glmfit
by Dowling, Kevin Francis 04 Oct '17

04 Oct '17

Dear Freesurfer experts, I'm trying to run a between groups analysis for two groups with a matrix set up for DOSS regressing out 5 covariates (contrast is 1 -1 0 0 0 0 0). I am trying to run this with DOSS as one of the covariates is an interaction term that would lead to colinearity issues in DODS. However, I am having some difficulty reading in my matrix. When I send in a standard matlab matrix (attached as a .mat) I get the following, "MatrixReadTXT: could not scan value [1][1] \ ERROR:in uncompressing," which I assume means that my matrix isn't being read properly (full output below under Output 1). I noticed a similar issue was discussed on the forum previously (https://mail.nmr.mgh.harvard.edu/pipermail//freesurfer/2013-November/034582…) and using an ASCII file seemed to resolve the issue. When I convert my matlab file into an ASCII text file (using dlmwrite in matlab, also attached as a .txt file) I get the following error (full output below under Output 2), "MatrixReadTxt: could not scan value [2][1] \ Invalid argument \ WARNING:matlab elment type is 758132281, which is not a matrix." I'm running Freesurfer version 5.3 on a CentOs 7 system. I suspect this is probably a simple mistake on my part, but I would greatly appreciate any guidance or further troubleshooting suggestions you might have! ~~~ Output 1: [dauntless:RPDR_Manual_Edits] (nmr-stable53-env) mri_glmfit --y /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh --X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix.mat --C /cluster/roffman/users/fsgd/RPDR_083115/Test_TwoGroups_RegressOutFiveCovariates_OddSyntax.mtx --glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS --surf fsaverage rh --fwhm 22 --no-prune Reading source surface /cluster/roffman2/users/RPDR_Manual_Edits//fsaverage/surf/rh.white FWHM = 22.000000 Number of vertices 163842 Number of faces 327680 Total area 65020.765625 AvgVtxArea 0.396850 AvgVtxDist 0.717994 StdVtxDist 0.193566 Surface smoothing by fwhm=22.000000, niters=356.000000 $Id: mri_glmfit.c,v 1.196.2.8 2012/11/01 18:51:41 greve Exp $ cwd /autofs/cluster/roffman2/users/RPDR_Manual_Edits cmdline mri_glmfit --y /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh --X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix.mat --C /cluster/roffman/users/fsgd/RPDR_083115/Test_TwoGroups_RegressOutFiveCovariates_OddSyntax.mtx --glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS --surf fsaverage rh --fwhm 22 --no-prune sysname Linux hostname dauntless.nmr.mgh.harvard.edu machine x86_64 user kfd11 FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 fwhm 22.000000 niters 356.000000 OneSampleGroupMean 0 y /autofs/cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh logyflag 0 X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix.mat usedti 0 labelmask /cluster/roffman2/users/RPDR_Manual_Edits//fsaverage/label/rh.cortex.label maskinv 0 glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS IllCondOK 0 ReScaleX 1 DoFFx 0 Creating output directory /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS Loading y from /autofs/cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh MatrixReadTxT: could not scan value [1][1] Invalid argument ERROR: in uncompressing not enough memory in buffer Cannot allocate memory Segmentation fault (core dumped) ~~~ Output 2: [dauntless:RPDR_Manual_Edits] (nmr-stable53-env) mri_glmfit --y /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh --X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix_ASCII.txt --C /cluster/roffman/users/fsgd/RPDR_083115/Test_TwoGroups_RegressOutFiveCovariates_OddSyntax.mtx --glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS --surf fsaverage rh --fwhm 22 --no-prune Reading source surface /cluster/roffman2/users/RPDR_Manual_Edits//fsaverage/surf/rh.white FWHM = 22.000000 Number of vertices 163842 Number of faces 327680 Total area 65020.765625 AvgVtxArea 0.396850 AvgVtxDist 0.717994 StdVtxDist 0.193566 Surface smoothing by fwhm=22.000000, niters=356.000000 $Id: mri_glmfit.c,v 1.196.2.8 2012/11/01 18:51:41 greve Exp $ cwd /autofs/cluster/roffman2/users/RPDR_Manual_Edits cmdline mri_glmfit --y /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh --X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix_ASCII.txt --C /cluster/roffman/users/fsgd/RPDR_083115/Test_TwoGroups_RegressOutFiveCovariates_OddSyntax --glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS --surf fsaverage rh --fwhm 22 --no-prune sysname Linux hostname dauntless.nmr.mgh.harvard.edu machine x86_64 user kfd11 FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 fwhm 22.000000 niters 356.000000 OneSampleGroupMean 0 y /autofs/cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh logyflag 0 X /cluster/roffman/users/fsgd/matrix/RPDR/99PostFortification_Minus_100PreFortification_DOSS_Nuisance_ZAgeGST_ZAgeXGrp_Matrix_ASCII.txt usedti 0 labelmask /cluster/roffman2/users/RPDR_Manual_Edits//fsaverage/label/rh.cortex.label maskinv 0 glmdir /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS IllCondOK 0 ReScaleX 1 DoFFx 0 Creating output directory /cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_fwhm22/FS53Test_TopMinusBottomRegressoutZAgeGST_ZAgeXGrp_DOSS Loading y from /autofs/cluster/roffman2/users/RPDR_Manual_Edits/Analyses_Thickness/199_PrePostFort/RH/rh_thickness_map_usingfsgd.mgh MatrixReadTxT: could not scan value [2][1] Invalid argument WARNING: matlab elment type is 758132281, which is not a matrix. Kind regards, Kevin Kevin F. Dowling Clinical Research Coordinator Brain Genomics Laboratory Division of Psychiatric Neuroimaging Martinos Center for Biomedical Imaging Massachusetts General Hospital 149 13th Street Charlestown, MA, 02129 (p) 617.643.2479

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PCA and v.gmh
by Florence Campana 04 Oct '17

04 Oct '17

Dear freesurfers, I contact you since I ran a PCA with mri_glmfit. I do not manage to load the output v.mgh as an overlay as suggested in the documentation, which makes me wonder what is wrong. The output file or me loading it? For clarification, I ran the command: mri_glmfit --surf fsaverage lh --y fmcpr.siemens.sm4.fsaverage.lh.nii.gz --pca --osgm --glmdir /Users/Florence/Documents/test_glm_pca to run the PCA in surface. v.mgh is of size 1*163842(nb of vertices)*1*300 (number of components). On the one hand, the output seems to make sense (its size, and the values are centered around 0), on the other hand I do not understand why I do not see anything when I load it as an overlay in tksurfer, despite modifying the thresholds and color parameters etc. Or should I then save separately each spatial component contained in v.mgh, and load each of them as one overlay? I would also like to specify that my input data (fmcpr.siemens.sm4.fsaverage.lh.nii.gz) are of size 1*163842(nb of vertices)*1*300 (time points). This is the data from a single subject for now (before getting group data via mris_preproc) and I am not sure of whether it has the proper format. Indeed, it is specified that —y is the “path to input file with each frame being a separate input”. I do not understand what it means: does frame refer to one time point, and should I then have 300 input files (which seems a lot!)? Thank you very much, Florence

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Test-retest reliability in longitudinal data
by Worker, Amanda 04 Oct '17

04 Oct '17

Hi all, I have a longitudinal dataset that I'd like to calculate test-retest reliability for. However, I am not sure whether to calculate this for the cross-sectionally processed data or longitudinal data? It would seem to make sense to use the cross-sectional data, as the time points are independent, but then it means that the test-retest results would not be applicable in a dataset processed longitudinally. On the other hand, calculating reliability metrics for data processed in the exact same way as will be used in further studies seems to make sense also, but would calculating test-retest on the longitudinally processed data bias the results, as the data points are not fully independent? Does anyone have any idea of the best way forward? Thanks, A

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Cortical parcellation
by Fereshte 04 Oct '17

04 Oct '17

Dear Experts, How to figure out if a cortical parcellation is Considered as a part of gray matter or white matter ? Thank you!

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Brodmann areas - exvivo labels
by Trisanna Sprung-Much 03 Oct '17

03 Oct '17

Hi there If I wanted to compare some spatial probability maps of some sulci of interest with the Juelich Data provided by FreeSurfer, would it make more sense to use the unthresholded Brodmann areas or the .thresh ones? Based on my understanding of how the .thresh was created (by creating a surface area of the average BA label that best resembled the average surface area of the label from each individual hemisphere) in order to avoid including vertices that had a probability of 10%, for instance, I think using .thresh would be fine. However, I thought I would verify. In input would be greatly appreciated. Best Trisanna -- Ph.D. Candidate McGill University Integrated Program in Neuroscience Psychology

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New Post-doc and Research Associate positions on the MIDUS study at The Center for Healthy Minds
by STACEY M SCHAEFER 03 Oct '17

03 Oct '17

Postdoctoral Fellow in Affective, Cognitive, and Aging Neuroscience Degree and area of specialization: Ph.D. in Neuroscience or Psychology Experience in neuroimaging research in emotion, cognition, and/or aging preferred. Minimum number of years and type of relevant work experience: * Demonstrated experience in neuroimaging. * Experience with multi-modal imaging including functional, resting state, diffusion weighted, and structural preferred. * Statistical acumen in analyzing neuroimaging data and programming expertise. * Demonstrated experience in scientific communication including speaking and publishing. * Excellent organizational, project management, interpersonal, writing and communication skills. * Adaptable; self-motivated. * Team player who possesses a passion for CHM mission/core values. Preferred: * Some prior post-doctoral experience. * Experience with psychophysiological data including heart rate variability, skin conductance, pupilometry, and/or eye tracking. Principal duties: The Center for Healthy Minds (CHM) was founded and is led by neuroscientist Richard J. Davidson. CHM’s mission is to cultivate well-being and relieve suffering through a scientific understanding of the mind. CHM is a leader in rigorous, interdisciplinary basic and translational research aimed at understanding the mind and how to nurture well-being in ourselves and others. CHM communicates these findings to the world, inspires scientists to conduct further research, and influences a shift in culture to embrace well-being. The successful candidate will play a key role analyzing both extant and new neuroimaging data in relation to sociodemographic, behavioral, cognitive, and biomarker data from the Midlife in the US (MIDUS; http://midus.wisc.edu/) national longitudinal study. MIDUS - Midlife in the United States, A National ...<http://midus.wisc.edu/> midus.wisc.edu A National Study of Health and Well-Being Additional information: Expected position duration is two years with potential for renewal based on funding and performance. UW-Madison is an equal opportunity/affirmative action employer. We promote excellence through diversity and encourage all qualified individuals to apply. To apply please send cover letter and CV to: Mike Kelly, mkelly1(a)wisc.edu<mailto:mkelly1@wisc.edu>, (608) 890-1115 ---------------------------------------------------------------------------------------------------------------------------------------------------- The Center for Healthy Minds, at the University of Wisconsin-Madison, is also recruiting for a Research Specialist! The successful candidate will be responsible for scientific integrity of data collection, data processing, and participant screening and scheduling for the longitudinal Midlife in the United States (MIDUS) study and a comparative study collecting similar measures in a cohort from the Wisconsin Registry for Alzheimer's Prevention (WRAP). MIDUS is a national longitudinal study of health and well-being. The primary purpose of this component of the MIDUS study is to examine how individual differences in the neural bases of different emotional response styles relate to health and well-being throughout the lifespan. The main goal of the study in the WRAP sample is to understand how different emotional response styles may increase a person's risk of developing Alzheimer's disease. Minimum $31,113 ANNUAL (12 months) depending on qualifications. Learn more and apply<http://jobs.hr.wisc.edu/cw/en-us/job/496193/research-specialist>.<http://jobs.hr.wisc.edu/cw/en-us/job/496193/research-specialist> UW-Madison is an affirmative action/equal employment employer and we encourage women, minorities, veterans, and people with disabilities to apply. Application deadline is 11:55pm October 11, 2017. Stacey M. Schaefer, Ph.D. Center for Healthy Minds University of Wisconsin-Madison 625 W Washington Ave Madison WI 53703 608-263-9321

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convert asc to fsaverage
by Alexandra Apple 03 Oct '17

03 Oct '17

Hi, I was wondering if there is a way to convert an asc file to fsaverage. I have a list of vertex values and I want to convert them to a thickness format. I know how to use mri_convert to go form thickness to ascii and I was hoping there is a way to go in the other direction. Thanks! Ali

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R: Re: R: Re: R: Re: R: Re: Map of covariance
by stdp82＠virgilio.it 03 Oct '17

03 Oct '17

Thank you very much. Where I should put 0 0 0 0 .25 .25 .25 .25? Which are the contrast reported below that I should modify? C1) gr1-gr2_intercept 1 -1 0 0 0 0 0 0 C2 )gr1-gr2_slope 0 0 0 0 1 -1 0 0 C3) gr1-gr3_intercept 1 0 -1 0 0 0 0 0 C4) gr1-gr3_slope 0 0 0 0 1 0 -1 0 C5) gr1-gr4_intercept 1 0 0 -1 0 0 0 0 C6) gr1-gr4_slope 0 0 0 0 1 0 0 -1 C7) gr2-gr3_intercept 0 1 -1 0 0 0 0 0 C8) gr2-gr3_slope 0 0 0 0 0 1 -1 0 C9) gr2-gr4_intercept 0 1 0 -1 0 0 0 0 C10) gr2-gr4_slope 0 0 0 0 0 1 0 -1 C11) gr3-gr4_intercept 0 0 1 -1 0 0 0 0 C12) gr3-gr4_slope 0 0 0 0 0 0 1 -1 C13) gr1+gr2-vs-gr3+gr4.intercept 0.5 0.5 -0.5 -0.5 0 0 0 0 C14) gr1+gr2-vs-gr3+gr4.slope 0 0 0 0 0.5 0.5 -0.5 -0.5 C15) group.effect.intercept 1 -1 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 1 0 0 -1 0 0 0 0 C16) group.effect.slope 0 0 0 0 1 -1 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 1 0 0 -1 Stefano >----Messaggio originale---- >Da: "Douglas Greve" <greve(a)nmr.mgh.harvard.edu> >Data: 3-ott-2017 16.19 >A: <freesurfer(a)nmr.mgh.harvard.edu> >Ogg: Re: [Freesurfer] R: Re: R: Re: R: Re: Map of covariance > >probably you want 0 0 0 0 .25 .25 .25 .25 > > > >On 10/3/17 10:10 AM, stdp82(a)virgilio.it wrote: >> Many thank for your response. >> Claryfing my question on point 3, >> When I look in group.effect.slope, the map show the group difference removing >> the effect of the covariate. >> Conversely, I would to obtain the map reporting only the effect of covariate >> on dependent variable (functional connectivity). >> Is possible to look on it? >> Regards >> Stefano >> >> >>> ----Messaggio originale---- >>> Da: "Douglas N Greve" <greve(a)nmr.mgh.harvard.edu> >>> Data: 2-ott-2017 16.58 >>> A: <freesurfer(a)nmr.mgh.harvard.edu> >>> Ogg: Re: [Freesurfer] R: Re: R: Re: Map of covariance >>> >>> >>> >>> On 10/01/2017 05:08 PM, stdp82(a)virgilio.it wrote: >>>> Hi, >>>> on 4 groups, 1 covariate (4GV1) >>>> >>>> I run mri_glmfit using the following .mtx >>>> >>>> C1) gr1-gr2_intercept 1 -1 0 0 0 0 0 0 >>>> C2 )gr1-gr2_slope 0 0 0 0 1 -1 0 0 >>>> C3) gr1-gr3_intercept 1 0 -1 0 0 0 0 0 >>>> C4) gr1-gr3_slope 0 0 0 0 1 0 -1 0 >>>> C5) gr1-gr4_intercept 1 0 0 -1 0 0 0 0 >>>> C6) gr1-gr4_slope 0 0 0 0 1 0 -1 0 >>> should be >>> >>> 0 0 0 0 1 0 0 -1 >>> >>>> C7) gr2-gr3_intercept 0 1 -1 0 0 0 0 0 >>>> C8) gr2-gr3_slope 0 0 0 0 0 1 -1 0 >>>> C9) gr2-gr4_intercept 0 1 0 -1 0 0 0 0 >>>> C10) gr2-gr4_slope 0 0 0 0 0 1 0 -1 >>>> C11) gr3-gr4_intercept 0 0 1 -1 0 0 0 0 >>>> C12) gr3-gr4_slope 0 0 0 0 0 0 1 -1 >>>> C13) gr1+gr2-vs-gr3+gr4.intercept 0.5 0.5 -0.5 -0.5 0 0 0 0 >>>> C14) gr1+gr2-vs-gr3+gr4.slope 0 0 0 0 0.5 0.5 -0.5 -0.5 >>>> C15) group.effect.intercept >>>> 1 -1 0 0 0 0 0 0 >>>> 1 0 -1 0 0 0 0 0 >>>> 1 0 0 -1 0 0 0 0 >>>> C16) group.effect.slope >>>> 0 0 0 0 1 -1 0 0 >>>> 0 0 0 0 1 0 -1 0 >>>> 0 0 0 0 1 0 0 -1 >>>> >>>> Please: >>>> 1) Could you check whether the all .mtx are complete and corrected, in >>>> particular the gr1+gr2-vs-gr3+gr4.intercept and the gr1+gr2-vs-gr3+gr4. >> slope? >>> they look correct, except for the one i noted >>>> 2) Could you explain the meaning of: >>>> gr1+gr2-vs-gr3+gr4.slope - does the average slope of grp1 and grp2 differ >> from that of grp3 and 4 >>>> gr1+gr2-vs-gr3+gr4.intercept - same >>>> group.effect.slope - interaction between group and covariate >>>> group.effect.intercept - is there an effect of group on the inercept >>>> 3) Which is the contrast identifying the effect of covariate on group >>>> differences? >>> not sure what you mean >>>> Thanks >>>> >>>> Best regards >>>> >>>> >>>> Stefano >>>> >>>> >>>> >>>>> ----Messaggio originale---- >>>>> Da: "Douglas N Greve" <greve(a)nmr.mgh.harvard.edu> >>>>> Data: 5-set-2017 22.59 >>>>> A: <freesurfer(a)nmr.mgh.harvard.edu> >>>>> Ogg: Re: [Freesurfer] R: Re: Map of covariance >>>>> >>>>> you would need to create a contrast to look for the interaction. If you >>>>> have four gruops and 1 covariate, then it would be >>>>> >>>>> 0 0 0 0 1 -1 0 0 >>>>> >>>>> 0 0 0 0 1 0 -1 0 >>>>> >>>>> 0 0 0 0 1 0 0 -1 >>>>> >>>>> >>>>> >>>>> On 09/05/2017 04:32 PM, stdp82(a)virgilio.it wrote: >>>>>> Hi, >>>>>> e.g., by considering two or more groups, I would like to map the clusters >>>>>> reporting the covariance between the functional connectivity or cortical >>>>>> thickness (dependent variable) and age, tacking in account the group >>>>>> differences. >>>>>> Instead, I'm not interested to map the group differences, taking in >> account >>>>>> the nuisance factors. >>>>>> Thanks, >>>>>> Stefano >>>>>> >>>>>>> ----Messaggio originale---- >>>>>>> Da: "Douglas N Greve" <greve(a)nmr.mgh.harvard.edu> >>>>>>> Data: 5-set-2017 18.12 >>>>>>> A: <freesurfer(a)nmr.mgh.harvard.edu> >>>>>>> Ogg: Re: [Freesurfer] Map of covariance >>>>>>> >>>>>>> I don't understand what you are asking. can you elaborate? >>>>>>> >>>>>>> >>>>>>> On 08/30/2017 12:50 PM, stdp82(a)virgilio.it wrote: >>>>>>>> Hi list, >>>>>>>> by applying a design e.g. 4GV1 in fsgd I could assess the dependence >>>>>>>> of group differences taking in account the effect of covariate. >>>>>>>> Anyway, where I should look if I want assess the the map showing the >>>>>>>> clusters in which the dependent variable is associated to covariate, >>>>>>>> tacking in account the group difference? Please could you suggest my >>>>>>>> the path? >>>>>>>> Thanks >>>>>>>> >>>>>>>> Stefano >>>>>>>> >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> Freesurfer mailing list >>>>>>>> Freesurfer(a)nmr.mgh.harvard.edu >>>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>>>>> -- >>>>>>> Douglas N. Greve, Ph.D. >>>>>>> MGH-NMR Center >>>>>>> greve(a)nmr.mgh.harvard.edu >>>>>>> Phone Number: 617-724-2358 >>>>>>> Fax: 617-726-7422 >>>>>>> >>>>>>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting >>>>>>> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 >>>>>>> www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>>>>>> Outgoing: ftp://surfer.nmr.mgh.harvard. edu/transfer/outgoing/flat/greve/ >>>>>>> >>>>>>> _______________________________________________ >>>>>>> Freesurfer mailing list >>>>>>> Freesurfer(a)nmr.mgh.harvard.edu >>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>>>>> >>>>>>> >>>>>>> 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. >>>>>>> >>>>>>> >>>>>> _______________________________________________ >>>>>> Freesurfer mailing list >>>>>> Freesurfer(a)nmr.mgh.harvard.edu >>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>>>> >>>>>> >>>>> -- >>>>> Douglas N. Greve, Ph.D. >>>>> MGH-NMR Center >>>>> greve(a)nmr.mgh.harvard.edu >>>>> Phone Number: 617-724-2358 >>>>> Fax: 617-726-7422 >>>>> >>>>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting >>>>> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 >>>>> www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>>>> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ >>>>> >>>>> _______________________________________________ >>>>> Freesurfer mailing list >>>>> Freesurfer(a)nmr.mgh.harvard.edu >>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer(a)nmr.mgh.harvard.edu >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>> >>>> >>> -- >>> Douglas N. Greve, Ph.D. >>> MGH-NMR Center >>> greve(a)nmr.mgh.harvard.edu >>> Phone Number: 617-724-2358 >>> Fax: 617-726-7422 >>> >>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting >>> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 >>> www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ >>> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer(a)nmr.mgh.harvard.edu >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer(a)nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> > >_______________________________________________ >Freesurfer mailing list >Freesurfer(a)nmr.mgh.harvard.edu >https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >

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