Freesurfer

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Re: [Freesurfer] [External] Re: mri_deface fails on pediatric brain
by Soichi Hayashi 06 Aug '19

06 Aug '19

External Email - Use Caution All, I've tried the dev version with FS_HISTO_STD_THRESH set to 20, but I am seeing the same error message. hayashis@xps15-9560:~/test/deface/app-deface(master*) $ export FS_HISTO_STD_THRESH=20 hayashis@xps15-9560:~/test/deface/app-deface(master*) $ ./mri_deface ../t1.nii.gz $FREESURFER_HOME/average/talairach_mixed_with_skull.gca $FREESURFER_HOME/average/face.gca t1.nii.gz logging results to t1.nii.log reading '/home/hayashis/app/freesurfer-dev/average/talairach_mixed_with_skull.gca'... reading '../t1.nii.gz'... changing type of input volume to 8 bits/voxel... MRIchangeType: Building histogram 0 255 1000, flo=0, fhi=0.999, dest_type=0 bounding unknown intensity as < 10.3 or > 1461.6 total sample mean = 111.8 (0 zeros) spacing=8, using 2055 sample points, tol=1.00e-03... resetting wm mean[0]: 143 --> 144 resetting gm mean[0]: 89 --> 89 input volume #1 is the most T1-like using real data threshold=11.0 skull bounding box = (49, 43, 9) --> (207, 213, 219) finding center of left hemi white matter using (102, 100, 114) as brain centroid of Right_Cerebral_White_Matter... mean wm in atlas = 144, using box (83,79,88) --> (121, 120,139) to find MRI wm before smoothing, mri peak at 77 robust fit to distribution - 78 +- 5.8 distribution too broad for accurate scaling - disabling WARNING2: gca.c::GCAhistoScaleImageIntensities: h_mri->nbins=113, mri_peak=144 after smoothing, mri peak at 0, scaling input intensities by inf GCAhistoScaleImageIntensities: could not find wm peak Numerical result out of range I've checked the binary but it doesn't look like this binary contains any reference to FS_HISTO_STD_THRESH parameter. hayashis@xps15-9560:~/Downloads $ strings mri_deface | grep FS_HISTO hayashis@xps15-9560:~/Downloads 1 Did I download the right binary? Soichi On Mon, Aug 5, 2019 at 3:04 PM Bruce Fischl <fischl(a)nmr.mgh.harvard.edu> wrote: > no, the env variable won't change that. You can try changing the radius > with: > > -radius <n> > > where n=7 by default. Smaller will make it more aggressive. > > cheers > Bruce > On Mon, 5 Aug > 2019, Brian Biekman wrote: > > > External Email - Use Caution > > > > I am also using pediatric brains and am also interesting in using your > defacing algorithm. I downloaded your updated script and changed the > environment variable but it leaves a lot of the jaw and chin. Is this > something that can be fixed by changing the environment variable to some > custom value? > > > > -- > > Brian Biekman > > Graduate Student, University of Houston > > Clinical Psychology, Neuropsychology Concentration > > Laboratory of Early Experience and Development (LEED) > > bdbiekman(a)uh.edu > > Brian.Biekman(a)times.uh.edu > > > > ________________________________________ > > From: freesurfer-bounces(a)nmr.mgh.harvard.edu [ > freesurfer-bounces(a)nmr.mgh.harvard.edu] on behalf of Hoopes, Andrew [ > AHOOPES(a)mgh.harvard.edu] > > Sent: Monday, August 05, 2019 12:25 PM > > To: Freesurfer support list > > Subject: Re: [Freesurfer] [External] Re: mri_deface fails on pediatric > brain > > > > Hi Soichi, you can update mri_deface with: > > > > curl > https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/dev_binaries/centos7… > -o $FREESURFER_HOME/bin/mri_deface > > chmod +x $FREESURFER_HOME/bin/mri_deface > > > > Then, before running mri_deface, set the env variable: > > > > export FS_HISTO_STD_THRESH=20 > > > > Then, it should print out something like: > > > > mean wm in atlas = 144, using box (79,77,96) --> (120, 132,144) to find > MRI wm before smoothing, mri peak at 76 > > FS_HISTO_STD_THRESH found in the environment resetting from 10 to 20.0 > > robust fit to distribution - 76 +- 6.5 > > after smoothing, mri peak at 76, scaling input intensities by 1.895 > > mean wm in atlas = 144, using box (79,77,96) --> (120, 132,144) to find > MRI wm before smoothing, mri peak at 76 > > FS_HISTO_STD_THRESH found in the environment resetting from 10 to 20.0 > > robust fit to distribution - 76 +- 6.5 > > after smoothing, mri peak at 76, scaling input intensities by 1.895 > > > > > > ________________________________________ > > From: freesurfer-bounces(a)nmr.mgh.harvard.edu < > freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of Soichi Hayashi < > hayashis(a)iu.edu> > > Sent: Monday, August 5, 2019 11:32 AM > > To: Freesurfer support list > > Subject: Re: [Freesurfer] [External] Re: mri_deface fails on pediatric > brain > > > > External Email - Use Caution > > > > We run on our freesurfer through singularity/docker container, and we > are currently using Centos7 as the base image to build our freesurfer > container, but we can run it on any other Linux based OSes. As far as our > hardware goes, we run on variety of hardware including HPC system, public > and private cloud platforms and a few dedicated desktop machines at our > labs. > > > > Thanks! > > Soichi > > > > On Sun, Aug 4, 2019 at 10:55 PM Bruce Fischl <fischl(a)nmr.mgh.harvard.edu > <mailto:fischl@nmr.mgh.harvard.edu>> wrote: > > Hi Soichi > > > > I have a fix for you. Can you let us know what kind of hardware/software > > env you are running in and we will send it to you? > > > > cheers > > Bruce > > > > > > On Sun, 4 Aug 2019, Soichi Hayashi wrote: > > > >> > >> External Email - Use Caution > >> > >> Bruce, > >> I've uploaded one of the failed subject to surfer.nmr.mgh.harvard.edu< > http://surfer.nmr.mgh.harvard.edu> ftp > >> server (named t1.nii.gz).Thank you for looking into this problem. > >> > >> Soichi > >> > >> On Sun, Aug 4, 2019 at 10:35 AM Bruce Fischl < > fischl(a)nmr.mgh.harvard.edu<mailto:fischl@nmr.mgh.harvard.edu>> > >> wrote: > >> This message was sent from a non-IU address. Please exercise > >> caution when clicking links or opening attachments from > >> external sources. > >> ------- > >> > >> Hi Soichi > >> > >> mri_deface isn't really supported anymore, but if you upload a > >> subject > >> that fails I'll take a look. I would have thought in the ABCD > >> subject age > >> range it would work > >> > >> cheers > >> Bruce > >> On Sat, 3 Aug 2019, Soichi Hayashi wrote: > >> > >> > > >> > External Email - Use Caution > >> > > >> > Hello. > >> > > >> > We are running mri_deface on subjects from ABCD (Adolescent > >> Brain Cognitive > >> > Development) study and many of the subjects are failing on > >> mri_deface. > >> > > >> > $ mri_deface t1.nii.gz > >> > $FREESURFER_HOME/average/talairach_mixed_with_skull.gca > >> > $FREESURFER_HOME/average/face.gca defaced.nii.gz > >> > logging results to defaced.nii.log > >> > reading > >> > > >> '/usr/local/freesurfer/average/talairach_mixed_with_skull.gca'... > >> > reading 't1.nii.gz'... > >> > changing type of input volume to 8 bits/voxel... > >> > MRIchangeType: Building histogram > >> > bounding unknown intensity as < 10.3 or > 1461.6 > >> > total sample mean = 111.8 (0 zeros) > >> > spacing=8, using 2055 sample points, tol=1.00e-03... > >> > resetting wm mean[0]: 143 --> 144 > >> > resetting gm mean[0]: 89 --> 89 > >> > input volume #1 is the most T1-like > >> > using real data threshold=11.0 > >> > skull bounding box = (54, 38, 18) --> (205, 255, 220) > >> > using (104, 110, 119) as brain centroid... > >> > mean wm in atlas = 144, using box (85,83,94) --> (122, > >> 136,143) > >> > to find MRI wm > >> > before smoothing, mri peak at 79 > >> > robust fit to distribution - 80 +- 8.7 > >> > distribution too broad for accurate scaling - disabling > >> > WARNING2: gca.c::GCAhistoScaleImageIntensities: > >> > h_mri->nbins=111, mri_peak=140 > >> > after smoothing, mri peak at 0, scaling input > >> intensities by > >> > inf > >> > GCAhistoScaleImageIntensities: could not find wm peak > >> > Numerical result out of range > >> > > >> > > >> > Should talairach_mixed_with_skull.gca template work with > >> pediatric > >> > subjects? Or is there a different template we must choose? Or > >> maybe any > >> > parameters that we could tweak to get it work better? > >> > > >> > Thanks! > >> > Soichi > >> > > >> > > >> > >> > >> > > > > _______________________________________________ > > 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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Help with MRI segstats
by Daliah Ross 06 Aug '19

06 Aug '19

External Email - Use Caution Hi FreeSurfer experts, I have a couple of questions regarding mri_segstats tool for an analysis on vertex-wise cortical thickness data. We are attempting to compare maximum cluster sizes among permutations. We are having trouble figuring out how to get the anatomical label for each cluster using the --slabel argument. We are also wondering if the difference between cluster sizes between mri_surfcluster and mri_segstats is due to partial volumes, and if so, how to correctly use the --pv argument. It suggests in the wiki page that mri/norm.mgz volume is usually used but I believe we need something surface-based since we are using thickness data. Thank you in advance for any help you can provide. Best, Daliah *Daliah Ross* | dross3(a)mail.yu.edu Ph.D. Student, Clinical Psychology with Health Emphasis Ferkauf Graduate School of Psychology Yeshiva University Albert Einstein College of Medicine

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Neuroimaging Data Analyst Position at MGH, Harvard Medical School (Boston, MA)
by Camprodon, Joan A.,M.D.,M.P.H.,Ph.D. 06 Aug '19

06 Aug '19

RESEARCH AND DATA ANALYST LABORATORY FOR NEUROPSYCHIATRY AND NEUROMODULATION MASSACHUSETTS GENERAL HOSPITAL, HARVARD MEDICAL SCHOOL Background: The Laboratory for Neuropsychiatry and Neuromodulation at the Massachusetts General Hospital, Harvard Medical School, is seeking a full time Research and Data Analyst for a series of exciting large-scale projects aimed at understanding circuit dynamics underlying neuropsychiatric disorders and their treatments. The laboratory is a multidisciplinary group conducting clinical, translational and basic human neuroscience research with healthy and neuropsychiatric populations, using device-based neuromodulation interventions (TMS, tDCS, ECT, DBS, etc.), neuroimaging, EEG, behavioral and clinical methods. The program includes the Transcranial Magnetic Stimulation (TMS) clinical service, tightly integrated with our research mission, where we provide individualized treatments for neuropsychiatric patients. Duties and Responsibilities: The Scientific and Data Analyst responsibilities will include, but are not limited to: 1. Implement and support statistical analyses and processing pipelines/scripts for behavioral, EEG and multimodal neuroimaging analysis, including resting‐state and task-based fMRI, DTI and volumetric data. 2. Assist in the creation of databases, query tools for data mining, and documentation on their effective use. Behavioral (RedCap, etc.) and neuroimaging databases (XNAT, etc.) are emphasized. 3. Maintain quality control and integrity of neuroimaging and behavioral data by developing, implementing and overseeing data entry and QC protocols. 4. Implement web-based behavioral questionnaires (e.g. RedCap) and program behavioral tasks with presentation software (e.g. Psychophysics Toolbox, PsychoPy, Presentation). 5. Provide programming and analysis support for lab members. 6. Coordinate general lab computer/network administration (including data management and archiving). 7. Assist with the preparation of scientific reports and publications. Qualifications: 1. B.A., B.S. or equivalent with background in neuroscience, computer science, engineering or related fields. Masters or PhD degree preferred but not required. 2+ years of prior technical experience. 2. Quantitative and extremely proficient with multiple neuroimaging analysis platforms (e.g. HCP Pipeline and Workbench, FreeSurfer, SPM, FSL, AFNI, etc.). 3. Strong programming skills and comfort with diverse computing environments required. 4. Strong background in statistics and statistical software scripting (R preferred). 5. Strong background creating scientific figures and writing technical scientific reports. 6. Strong interpersonal skills and a high level of organization with careful attention to detail are absolutely required. This position requires a high degree of motivation and self‐sufficiency, although extensive training and supervision will be provided. Additional Qualifications: 1. Experience with neuroimaging and behavioral databases. 2. Experience with graph analyses, machine learning and modeling confer a significant advantage. 3. Experience with EEG analysis is preferred. 4. Graphic or Web design skills are considered a plus. Additional Information: Applications must be sent by email to Dr. Joan Camprodon (jcamprodon(a)mgh.harvard.edu<mailto:jcamprodon@mgh.harvard.edu>) CCing Dr. Kristen Ellard (kellard(a)mgh.harvard.edu) and include a CV, cover letter and names of three references. Expected start date: Fall 2019

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WM surface including too much gray matter
by Brian Biekman 05 Aug '19

05 Aug '19

External Email - Use Caution To whom it may concern: I'm dealing with an issue in my subjects' recon-all output where the white matter surface extends into the gray matter, especially in the parietal and posterior temporal regions. This is caused by the wm.mgz including too much gray matter. This issue occurs to some degree in every subject's scans. This can be fixed with extensive wm volume edits but since this appears to be a systematic problem, I wanted to find a solution that could be applied to every subject, possibly in the recon-all pipeline. I've tried using an expert parameters 2 different ways to no avail. I've changed the -wlo values in mri_segment but it removes wm voxels in the temporal lobe from the wm.mgz surface. I've tried adding the -prune and -gentle flags to mri_normalize but that appears to make the problem worse. Is there a better approach to try? If necessary, I can send a subject's recon-all output. -- Brian Biekman Graduate Student, University of Houston Clinical Psychology, Neuropsychology Concentration Laboratory of Early Experience and Development (LEED) bdbiekman(a)uh.edu Brian.Biekman(a)times.uh.edu

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Re: [Freesurfer] [External] Re: mri_deface fails on pediatric brain
by Soichi Hayashi 05 Aug '19

05 Aug '19

External Email - Use Caution We run on our freesurfer through singularity/docker container, and we are currently using Centos7 as the base image to build our freesurfer container, but we can run it on any other Linux based OSes. As far as our hardware goes, we run on variety of hardware including HPC system, public and private cloud platforms and a few dedicated desktop machines at our labs. Thanks! Soichi On Sun, Aug 4, 2019 at 10:55 PM Bruce Fischl <fischl(a)nmr.mgh.harvard.edu> wrote: > Hi Soichi > > I have a fix for you. Can you let us know what kind of hardware/software > env you are running in and we will send it to you? > > cheers > Bruce > > > On Sun, 4 Aug 2019, Soichi Hayashi wrote: > > > > > External Email - Use Caution > > > > Bruce, > > I've uploaded one of the failed subject to surfer.nmr.mgh.harvard.edu > ftp > > server (named t1.nii.gz).Thank you for looking into this problem. > > > > Soichi > > > > On Sun, Aug 4, 2019 at 10:35 AM Bruce Fischl <fischl(a)nmr.mgh.harvard.edu > > > > wrote: > > This message was sent from a non-IU address. Please exercise > > caution when clicking links or opening attachments from > > external sources. > > ------- > > > > Hi Soichi > > > > mri_deface isn't really supported anymore, but if you upload a > > subject > > that fails I'll take a look. I would have thought in the ABCD > > subject age > > range it would work > > > > cheers > > Bruce > > On Sat, 3 Aug 2019, Soichi Hayashi wrote: > > > > > > > > External Email - Use Caution > > > > > > Hello. > > > > > > We are running mri_deface on subjects from ABCD (Adolescent > > Brain Cognitive > > > Development) study and many of the subjects are failing on > > mri_deface. > > > > > > $ mri_deface t1.nii.gz > > > $FREESURFER_HOME/average/talairach_mixed_with_skull.gca > > > $FREESURFER_HOME/average/face.gca defaced.nii.gz > > > logging results to defaced.nii.log > > > reading > > > > > '/usr/local/freesurfer/average/talairach_mixed_with_skull.gca'... > > > reading 't1.nii.gz'... > > > changing type of input volume to 8 bits/voxel... > > > MRIchangeType: Building histogram > > > bounding unknown intensity as < 10.3 or > 1461.6 > > > total sample mean = 111.8 (0 zeros) > > > spacing=8, using 2055 sample points, tol=1.00e-03... > > > resetting wm mean[0]: 143 --> 144 > > > resetting gm mean[0]: 89 --> 89 > > > input volume #1 is the most T1-like > > > using real data threshold=11.0 > > > skull bounding box = (54, 38, 18) --> (205, 255, 220) > > > using (104, 110, 119) as brain centroid... > > > mean wm in atlas = 144, using box (85,83,94) --> (122, > > 136,143) > > > to find MRI wm > > > before smoothing, mri peak at 79 > > > robust fit to distribution - 80 +- 8.7 > > > distribution too broad for accurate scaling - disabling > > > WARNING2: gca.c::GCAhistoScaleImageIntensities: > > > h_mri->nbins=111, mri_peak=140 > > > after smoothing, mri peak at 0, scaling input > > intensities by > > > inf > > > GCAhistoScaleImageIntensities: could not find wm peak > > > Numerical result out of range > > > > > > > > > Should talairach_mixed_with_skull.gca template work with > > pediatric > > > subjects? Or is there a different template we must choose? Or > > maybe any > > > parameters that we could tweak to get it work better? > > > > > > Thanks! > > > Soichi > > > > > > > > > > > >

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Re: [Freesurfer] Using Freesurfer Commands for Neonate
by Mahshid Fouladivanda 05 Aug '19

05 Aug '19

External Email - Use Caution Dear Lilla, Thanks for your help. I have used the same command in the recon-all.log file (seen bellow), but I have a doubt for the highlighted part in the command: "mri_aparc2aseg --s SBJ1 --volmask --aseg aseg.presurf.hypos --relabel mri/norm.mgz mri/transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gcamri/aseg.auto_noCCseg.label_intensities.txt" which used a predefined file from freesurfer folder. Actually, I used the mri_aparc2aseg command with some changes, seen bellow: mri_aparc2aseg --s mysubj --annot aparc --o $SUBJECTS_DIR/mysubj/mri/parc.mgz so, I am wondered if it reads the ' /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gca' automatically. I would appreciate it of you could help on how to correct this part. Regards, Mahshid

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FreeSurfer Import File
by Jiajun Luo 05 Aug '19

05 Aug '19

External Email - Use Caution Hi, I am beginner for FreeSurfer... I want to run recon-all on my MRI T1 data. I have a series of dcm files for an individual and these dcm files are archived in a tgz file. What should I import to FreeSurfer for recon-all? A single dcm file, or some selected dcm files, or all dcm files, or the archived tgz file? Do I need to convert these dcm files to certain types of files? Thank you! Jiajun

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Gray Matter Intensity at each vertex
by Axelson, Eric D 05 Aug '19

05 Aug '19

External Email - Use Caution Is there a way to extract the gray matter intensity value at each vertex of a surface file (lh/rh.curv/pial/etc)? ________________________________ Notice: This UI Health Care e-mail (including attachments) is covered by the Electronic Communications Privacy Act, 18 U.S.C. 2510-2521 and is intended only for the use of the individual or entity to which it is addressed, and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If you are not the intended recipient, any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please notify the sender immediately and delete or destroy all copies of the original message and attachments thereto. Email sent to or from UI Health Care may be retained as required by law or regulation. Thank you. ________________________________

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Re: [Freesurfer] Estimation of surface based smoothing
by Greve, Douglas N.,Ph.D. 05 Aug '19

05 Aug '19

There is no set value for surface or volume. Typically we apply 5-10mm. Surface-based smoothing is more gentle than volume-based, so you can apply larger kernels. On 8/5/2019 2:59 AM, Matthieu VANHOUTTE wrote: External Email - Use Caution Hi Douglas, I was talking about the FWHM smoothing to apply before statistical analysis. I know the FWHM to apply to a volume-based study but wonder what should be apply regarding the surface-based study? Best, Matthieu On 04/08/2019 22:53, Greve, Douglas N.,Ph.D. wrote: can you be more specific? What kind of effects? On 8/4/2019 4:29 PM, Matthieu Vanhoutte wrote: External Email - Use Caution Dear experts, Knowing the size of the effects on volume-based data, does anyone would know a way to estimate the size of the effects on surface data? Best, Matthieu _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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subcortical structure accuracy
by Zeng,Victor (BIDMC - Psychiatry) 05 Aug '19

05 Aug '19

?Hi all, For the sake of time, for one dataset we have that we want to extract hippocampal/amygdala/thalamus subfields from, we ran it without manually editing any portions of the brain. We're going to visually QC it, but do you anticipate any trouble with the accuracy of the segmentation considering we didn't remove any portions of the brainmask for dura/blood vessels and etc. Obviously, if we detect that a blood vessel is running through the amgydala, we would remove that and rerun the subfield extraction. Victor Zeng Beth Israel Deaconess Medical Center Keshavan Lab ph: 617 754 1237 -- ________________________________ This message is intended for the use of the person(s) to whom it may be addressed. It may contain information that is privileged, confidential, or otherwise protected from disclosure under applicable law. If you are not the intended recipient, any dissemination, distribution, copying, or use of this information is prohibited. If you have received this message in error, please permanently delete it and immediately notify the sender. Thank you.

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