Freesurfer January 2019

freesurfer@nmr.mgh.harvard.edu

119 participants
167 discussions

Problem with checking registration quality using tkregister-sess: if: Expression Syntax.
by Keri Woods 14 Jan '19

14 Jan '19

External Email - Use Caution Hi I’m having a problem that I’m hoping someone could help me with. I’m trying to check the quality of the alignment of structural and functional data, using the command: tkregister-sess -s 20_1 -fsd bold -per-run where 20_1 is the subject ID. Nothing opens, and I get the following error: -------------------------------------------------------------- tkregister-sess logfile is /home/keri/audio2/log/tkregister-sess.log -------------------------------------------------------------- Session: 20_1 ---------------- if: Expression Syntax. I’ve opened the log file, which doesn’t shed much light on the problem. Its contents are: tkregister-sess log file $Id: tkregister-sess,v 1.42 2015/12/10 22:51:31 zkaufman Exp $ setenv SUBJECTS_DIR /home/keri/recon_all/subjects cd /home/keri/audio /usr/local/freesurfer/fsfast/bin/tkregister-sess -s 20_1 -fsd bold -per-run buddha /usr/local/freesurfer/bin/tkregister2 Session: 20_1 ---------------- The SUBJECTS_DIR show here is correct. I’ve been looking through the archives, and someone suggested checking whether tkmedit works when having problems with tkregister. I’ve done this, and it works fine. On a side note, when checking the quality of registration using the command in the format of: tkregister-sess -s sess01 -s sess02 -s sess03 -fsd bold -per-run -bbr-sum I get very poor results for many of the subjects (some of them are even > 1, which I didn’t think was possible). Could this be part of the problem? The subject 20_1 I used as a example did have a fairly good result (eg 0.5090 for one run). Thanks in advance. Best wishes, Keri

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Custom templates and longitudinal processing
by Jason Tourville 11 Jan '19

11 Jan '19

External Email - Use Caution Hi Freesurfers, We are collaborating with a group at Michigan that planss to use Freesurfer's longitudinal pipeline to process T1 data collected from children (see brief project summary below) at multiple time points. Our role is to oversee recon QA and application of a custom cortical parcellation labeling system. As we move to longitudinal processing, we have a couple questions about when/how/if to apply various custom templates that we're using. In preparing the cross-sectional recons, we used a custom tal atlas generated from our sample with help from Avi Snyder. Question 1: When creating the base or running longitudinal processing, should this atlas be specified in some manner? We also generated a custom pediatric surface template and cortical classifier; we co-register each subject to this template and apply the classifier (we actually just use surf2surf to map the labeled template to the individual subject surfaces) to generate cortical labels. Question 2: how should we do this in the longitudinal stream? would we register the subject's base to the pediatric surface template then use the the resulting sphere.reg in the call to mris_ca_label? In the longitudinal work flow, it looks like cortical labeling is done in the longitudinal stream. Question 3: If we notice a labeling error and correct it, is there a means of propagating that correction across all the time points? Question 4: If we've labeled and reviewed all the cross-sectional time-point-one data, is there a way to map those labels to the longitudinal stream time points that doesn't compromise the resulting labels? Question 5: Given the project summary below, are there any other concerns that we should be aware of when applying the longitudinal pipeline? *Brief summary of the longitudinal study of childhood stuttering at University of Michigan (Soo-Eun Chang)* Over the last few years, we have collected yearly structural and functional MRI scans from around 300 children who do and do not stutter. These are children between the ages of 3-10 at initial scan. Scans were acquired each year for up to 4 years (some children do not have all 4 years, but many do). Data from year 1 has been published (Garnett et al., 2018) using a custom labeling system and atlas. We have just finished manual editing of the scans from years 2 – 4 and are preparing to conduct a longitudinal analysis with all 4 years of scans in FreeSurfer. We plan to extract cortical and subcortical measures of thickness, area, volume, LGI, etc. and compare developmental trajectories of these measures in the stuttering group vs. the control group. Also, because of the longitudinal design, we have information on whether children in the stuttering group went on to recover or if their stuttering persisted. All children were considered stuttering at their initial visit, and based on evaluations each year, we are able to retrospectively assign them to the persistent group or the recovered group. In this way, in addition to comparing morphometric measures between children who stutter and controls, we also hope to find neural markers that help predict persistence vs. recovery in childhood stuttering. Many thanks for your input. Jason -- Jason A. Tourville, Ph.D. Research Assistant Professor Department of Speech, Language, and Hearing Sciences Boston University 677 Beacon St. Boston, MA 02215 Phone: (617)353-9484 Fax: (617)353-7755

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DKT vs. DK Freesurfer atlases
by Gabor Perlaki 11 Jan '19

11 Jan '19

Dear all, I've found an article comparing DK and DKT atlas based automatic segmentations to manual labels (Klein and Tourville, Front Neurosci. 2012; 6:171, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3514540/) It seems that using DKT40 works better than DK in most of the cortical regions (based on Dice). Does anybody know that the DKTatlas related results created by Freesurfer (e.g. aparc.DKTatlas+aseg.mgz, lh.aparc.DKTatlas.stats) are based on the same DKT40 atlas used in the article and whether the DKT40 atlas related results were also created by Freesurfer in the article by Klein and Tourville? Are the Freesurfer created results in lh.aparc.DKTatlas.stats generally more reliable than the results in lh.aparc.stats? -- Gabor Perlaki research associate Diagnostic Center of Pécs H-7623 Pécs, Rét str. 2. Tel.: 0036-30-2084367 E-mail: petzinger.gabor(a)gmail.com

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Position Available in Neuroimaging of PTSD at VA Boston
by Emma Brown 11 Jan '19

11 Jan '19

External Email - Use Caution The Behavioral Science Division (Boston) of the National Center for Postraumatic Stress Disorder is strongly tied to the Neuroimaging Research for Veterans (NeRVe) Center at VA Boston. Imaging candidates with interest in military-associated traumatic stress are strongly encouraged to apply for this position with duties beginning in 2019. The National Center for PTSD is a multi-site consortium within the Department of Veteran Affairs. The Behavioral Science Division is nationally recognized for developing model research and clinical programs in stress and trauma. The Division has outstanding resources for research, training, and clinical activities. Exceptional opportunities exist for professional collaboration with a wide array of researchers locally and at six other National Center divisions, as well as with investigators in other academic, VA, and military settings. A major focus of National Center research is adjustment following military service, but candidates who have background in related areas or with other types of trauma are welcomed. Requirements for a position are a doctoral degree plus demonstrated skills in research, scientific publication, and grant writing. Clinical psychologists must have graduate training and clinical internship from APA-accredited programs, as well as existing professional licensure or eligibility for licensure. Division investigators participate in the training and supervision of postdoctoral fellows and predoctoral interns. Faculty appointment at the Boston University School of Medicine is anticipated. Salary and benefits are competitive. Please send a curriculum vitae with a cover letter, reprints, and the names of anticipated professional references to Brian Marx, Ph.D., National Center for PTSD (116B-4), VA Boston Healthcare System, 150 S. Huntington Ave., Boston, MA 02130-4817 or via email to brian.marx(a)va.gov. Materials will be reviewed on a first-come basis until available positions are filled. Formal position posting and the application process will follow federal procedures via the USAjobs.gov website. U.S. citizenship is required. The Department of Veteran Affairs is an Equal Opportunity Employer.

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How to make grey matter edits
by Rosalia Dacosta Aguayo 11 Jan '19

11 Jan '19

External Email - Use Caution Dear FreeSufer team, I have been working with MS patients using T1 lesion filled images. I corrected pial surface and I did control points for white matter. However, I have not found how to edit gray matter segmentation. I would much appreciate if you tell me where I could find a TUTORIAL fir this issue. Best regards, Rosalia

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Questions About Effect Sizes in Vertexwise GLM Anlaysis
by Max Owens 11 Jan '19

11 Jan '19

External Email - Use Caution Hi Freesurfer Experts, I have two related questions about the measures of effect size that can be derived from a vertexwise GLM analyses conducted in Freesurfer: 1. What is the unit of effect size for the gamma.mgh file created by vertexwise GLM Analysis using mri_glmfit? 2. Based on two previous posts ( https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg52144.html and https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg57316.html) I tried to calculate Cohen’s d in all significant clusters resulting from clusterwise multiple comparison correction of a vertexwise GLM using mri_glmfit. I did this by dividing the gamma.mgh file by rstd.mgh to create a cohensd.mgh file (fscalc gamma.mgh div ../rstd.mgh -o cohensd.mgh). Then from the output of that operation I extracted the mean value in each significant cluster from my vertexwise analysis using mri_segstats (mri_segstats --i cohensd.mgh --seg cluster.sig.ocn.mgh --exclude 0 --o sum.dat). My understanding was that this would yield the average Cohen’s d of each cluster. However, if it is indeed Cohen’s d the effect sizes yielded (between .01 and .02) are so small that they should be undetectable given my sample size (N=1104). This leads me to believe that the outputs are not in fact the average Cohen’s d of the cluster. Can anyone provide any clarification of what the output file of this process means? Specifically, what is the unit of effect size? Thanks in advance for your help! Max

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Re: [Freesurfer] Running TRACULA
by Yendiki, Anastasia 11 Jan '19

11 Jan '19

Hi Ainara - What do you mean by default freesurfer files? Can you describe how you created each subject's recon-all style directory under SUBJECTS_DIR? TRACULA looks for $SUBJECTS_DIR/$subjectid/mri/aparc+aseg.mgz for each subject ID. a.y On Jan 11, 2019 8:30 AM, Ainara Gomez <ainara.ggastiasoro(a)deusto.es> wrote: External Email - Use Caution Dear Freesurfer experts, I am trying to run TRACULA as a complementary analysis on a patients and healthy controls sample. I have read in the tutorial that it is assumed that FreeSurfer has already been run on all the subjects' T1-weighted data because TRACULA will use the aparc+aseg.mgz from each subject's FreeSurfer reconstruction. However, T1s of my subjects are not available for this study and I only have the diffusion images. I have run the three steps of the trac-all separately (using the default freesurfer files: aseg+aparc.mgz...) and all the processes have ended without errors. Is this correct? Is it possible to use the default freesurfer files instead of each subject's recon-all files in order to run trac-all? Thank you very much in advance, Ainara -- Ainara Gómez Gastiasoro Dpt of Methods and Experimental Psychology Neuropsychology of Severe Medical Conditions research team Faculty of Psychology and Education University of Deusto neurolab.deusto.es<http://neurolab.deusto.es/> https://twitter.com/deustoneurolab Avda. de las Universidades 24, 48007, Bilbao (Spain) Phone: +34 944139000 (ext. 3268) <tel:%2B34%20944139000>

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Running TRACULA
by Ainara Gomez 11 Jan '19

11 Jan '19

External Email - Use Caution Dear Freesurfer experts, I am trying to run TRACULA as a complementary analysis on a patients and healthy controls sample. I have read in the tutorial that it is assumed that FreeSurfer has already been run on all the subjects' T1-weighted data because TRACULA will use the aparc+aseg.mgz from each subject's FreeSurfer reconstruction. However, T1s of my subjects are not available for this study and I only have the diffusion images. I have run the three steps of the trac-all separately (using the default freesurfer files: aseg+aparc.mgz...) and all the processes have ended without errors. Is this correct? Is it possible to use the default freesurfer files instead of each subject's recon-all files in order to run trac-all? Thank you very much in advance, Ainara -- Ainara Gómez Gastiasoro Dpt of Methods and Experimental Psychology Neuropsychology of Severe Medical Conditions research team Faculty of Psychology and Education University of Deusto neurolab.deusto.es https://twitter.com/deustoneurolab Avda. de las Universidades 24, 48007, Bilbao (Spain) Phone: +34 944139000 (ext. 3268) <%2B34%20944139000>

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NeuroHackademy 2019
by Ariel Rokem 10 Jan '19

10 Jan '19

External Email - Use Caution We are happy to announce a call for applications to participate in Neurohackademy 2019! This two-week hands-on workshop held at the University of Washington eScience Institute in Seattle, July 29th - August 9th, 2019, focuses on tools and techniques used to analyze human neuroscience data, on methods used to extract information from large datasets of publicly available data (such as the Human Connectome Project, OpenfMRI, etc.), and on tools for making human neuroscience research open and reproducible. Neurohackademy sessions in the first week will include lectures and tutorials on data science, machine learning, data visualization, and data resources. The second week will be devoted to participant-directed activities: guided work on team projects, hackathon sessions, and breakout sessions on topics of interest. *For more details and a preliminary list of instructors, see: **https://neurohackademy.org/ <https://neurohackademy.org/>* We are now accepting applications to participate at https://neurohackademy.org/apply/ Ideally, applicants should have some prior experience with programming and with neuroscience data analysis, but we welcome applications from participants with a variety of relevant backgrounds. Accepted applicants will be asked to pay a fee of $200 upon final registration. This fee will include participation in the course, accommodation in the UW dorms, and two meals a day (breakfast and lunch), for the duration of the course. A limited number of fee waivers and travel grants will be available. We encourage students with financial need and students from groups that are underrepresented in neuroimaging and data science to apply for these grants (see application form for details). *Important dates:* *February 18th: Application deadline* *March 15th: Notification of acceptance* *April 1st: Final registration deadline*

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LME MATLAB Tools
by Tefera, Getaneh B 10 Jan '19

10 Jan '19

External Email - Use Caution Dear Freesurfer Experts, I have a question about how to interpret cortical thickness difference between groups using LME MATLAB tools. I have three groups(g0, g1, and g2) and five time points(t= 0,0.5, 1,2,3). I am trying to use LME model with random effects y-int and time from the base line. Based on the LME tutorial and the questions and answers from Freesurfer support list: Y=B1+B2*t+B3*g1+B4*g1*t+B5*g2+B6*g2*t+ .... To see the difference between group g0 and group g1, I constructed the following contrast matrix using the tutorial provided in LME MATLAB tools C = [zeros(1,3) [1 0 0] zeros(1,4)]; CM.C=[zeros(1,3) [1 0 0] zeros(1,4)]; Based on the tutorial I run the following functions. rhstats = lme_mass_fit_Rgw(X,[1 2],rhY,ni,rhTh0,rhRgs,rhsphere); F_rhstats = lme_mass_F(rhstats,CM); fs_write_fstats(F_rhstats,rhmri,'/backup/cross/rh_AB_cross_sig.mgh','sig'); nv=length(rhstats); Beta2 = zeros(1,nv); for i=1:nv if ~isempty(rhstats(i).Bhat) Beta2(i) = rhstats(i).Bhat(2); end; end; rhmri1 = rhmri; rhmri1.volsz(4) = 1; fs_write_Y(Beta2,rhmri1,'rhBeta2.mgh'); [detvtx,sided_pval,pth] = lme_mass_FDR2(F_rhstats.pval,F_rhstats.sgn,rhcortex,0.05,0); fs_write_Y(sided_pval,rhmri1,'spval.mgh'); When I view sig.mgh using tksurfer I see regions with red and blue color. 1) Does the result shows the oveall change for all the five time points? 2) How do I know the difference between the two groups for each 5 time points? 3) Does red means thickness of group A is larger than thickness of group B? 4) what does rhBeta2.mgh and spval.mgh tell us? How can I extract the information from the file? With regards Getaneh

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Freesurfer January 2019