Freesurfer May 2020

freesurfer@nmr.mgh.harvard.edu

111 participants
208 discussions

unable to set everything to version 7
by Safi Ullah . 10 May '20

10 May '20

External Email - Use Caution Dear List, I installed version 7 into a directory named 'freesurfer' where there are already Verion 6 and Version 7(beta) is installed. My OS is 'Ubuntu_FS_600_700b1_TutorialData' "/usr/local/freesurfer/6.0.0-1" "/usr/local/freesurfer/7.0.0-0.1b1" So I installed Version 7 (stable) in the same directory "/usr/local/freesurfer/" but when I export my necessities *$export FREESURFER_HOME=/usr/local/freesurfer* *$export SUBJECTS_DIR=/usr/local/freesurfer/subjects* *$source $FREESURFER_HOME/SetUpFreeSurfer.sh* -------- freesurfer-linux-centos7_x86_64-7.0.0-20200429-3a03ebd -------- Setting up environment for FreeSurfer/FS-FAST (and FSL) FREESURFER_HOME /usr/local/freesurfer FSFAST_HOME /usr/local/freesurfer/6.0.0-1/fsfast FSF_OUTPUT_FORMAT nii.gz SUBJECTS_DIR /usr/local/freesurfer/subjects TUTORIAL_DATA /home/developer/tutorial_data MNI_DIR /usr/local/freesurfer/6.0.0-1/mni FSL_DIR /usr/local/freesurfer/6.0.0-1/fsl_507 Although, I checked the creation dates of files from the mismatching directories in both versions, there is no update in the latest version, so it should not be a problem, however I wonder, why are some variables still pointing toward the old version? any solution,please? Regards, Safi

1 0

mris_fix_topology command does not finish
by Billah, Tashrif 10 May '20

10 May '20

Hi there, The following commands part of `recon-alll -i some_T1w.nii.gz -autorecon2` does not finish for some of our data: #@# Fix Topology lh Sat May 9 14:17:52 EDT 2020 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 sub-7049_ses-01_desc-XcMaN4_T1w lh #@# Fix Topology rh Sat May 9 14:17:52 EDT 2020 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 sub-7049_ses-01_desc-XcMaN4_T1w rh Waiting for PID 57296 of (57296 57299) to complete... Waiting for PID 57299 of (57296 57299) to complete... We kept them running for about 48 hours in multiple attempts, yet no luck. Moreover, mris_fix_topology is a binary file so I am unable to debug through. Any suggestion on where to look at? Best, Tashrif

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[CFP] The 3rd International workshop on Machine Learning in Clinical Neuroimaging (MLCN 2020)
by MLCN Workshop 09 May '20

09 May '20

External Email - Use Caution *Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) on 4 October 2020 at MICCAI 2020 in Lima, Peru. We welcome contributions on novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *30 June 2020*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. For more information, please visit https://mlcnws.com/. Best wishes, The MLCN 2020 committee Christos Davatzikos Andre Marquand Jonas Richiardi Emma Robinson Ahmed Abdulkadir Cher Bass Mohamad Habes Seyed Mostafa Kia Jane Maryam Rondina Chantal Tax Hongzhi Wang Thomas Wolfers International Workshop on Machine Learning in Clinical Neuroimaging 4 October 2020 in Lima, Peru The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/) a satellite event of MICCAI (https://miccai2020.org) calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions. For the machine learning track, we seek contributions with substantial methodological novelty in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Spatio-temporal brain data analysis - Structural data analysis - Graph theory and complex network analysis - Longitudinal data analysis - Model stability and interpretability - Model scalability in large neuroimaging datasets - Multi-source data integration and multi-view learning - Multi-site data analysis, from preprocessing to modeling - Domain adaptation, data harmonization, and transfer learning in neuroimaging - Unsupervised methods for stratifying brain disorders - Deep learning in clinical neuroimaging - Model uncertainty in clinical predictions - ... In the clinical neuroimaging track, we seek contributions that explore how the application of advanced machine learning methods help us to move towards precision medicine for complex brain disorders. Topics of interest include but are not limited to: - Biomarker discovery - Refinement of nosology and diagnostics - Biological validation of clinical syndromes - Treatment outcome prediction - Course prediction - Analysis of wearable sensors - Neurogenetics and brain imaging genetics - Mechanistic modeling - Brain aging - ... Submission Process: The workshop seeks high quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (see https://www.miccai2020.org/en/PAPER-SUBMISSION-GUIDELINE.html#manuscript-fo… for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2020. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2020 conference. Best Paper Award: This year, all MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality and novelty of contributions. The winner will be announced at the end of the workshop and will receive 500 USD honorarium. Important Dates: - Paper submission deadline: June 30th, 2020 - Notification of Acceptance: July 24th, 2020 - Camera-ready Submission: July 31st, 2020 - Workshop Date: 4 October 2020

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Parcellation of files containing within subject summary measures
by Martin Gell 08 May '20

08 May '20

External Email - Use Caution Dear experts, I calculated vertex-wise standard deviation across multiple thickness (lh.thickness.fwhm10.fsaverage.mgh) and volume files (lh.volume.fwhm10.fsaverage.mgh) of one individual, calculated from scans at different time points. I used the freesurfer library for R to calculate the sd in thickness and volume between scans and saved the sd in each vertex as a new mgh file (one for thickness and one for volume). I now need to parcellate (calculate the mean value in each parcel) my new mgh files containing the vertex-wise sd using the Desikan-Killiany Atlas and Destrieux Atlas as definitions for the parcels. What is the best way to approach this? Thank you! Martin <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai…> Virus-free. www.avast.com <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai…> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

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tracula stats summary
by Zeng, Qi 08 May '20

08 May '20

External Email - Use Caution Hi, Is it possible to collect all 18 tracts stats into a single file? Has tried: "tractstats2table --load-pathstats-from-file paths.txt --overall --tablefile tractAll.txt" Path.txt contains all the subjects' 18 paths' full path directory Error message: "all stats files need to have the same pathwayname. try verbose option These are the pathwaynames found: ['fmajor', 'fminor', 'lh.atr', 'lh.cab', 'lh.ccg', 'lh.cst', 'lh.ilf', 'lh.slfp', 'lh.slft', 'lh.unc', 'rh.atr', 'rh.cab', 'rh.ccg', 'rh.cst', 'rh.ilf', 'rh.slfp', 'rh.slft', 'rh.unc']" Best, Qi -- Ph.D. candidate Icahn School of Medicine at Mount Sinai

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Hippocampal subfield tool for 6.0.0 data
by O'Shea,Andrew 08 May '20

08 May '20

External Email - Use Caution Hello, Would you be able to point me to the right version of the hippocampal subfield tool as described here: https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfieldsAndNucleiOfAm… The data have been processed in 6.0.0, but the stable pub version we have doesn't seem to have the segmentHA_T1.sh within it. I see 7.0.0 is out with these hippocampal tools but I know mixing versions is cautioned against. Where would I find the appropriate hippocampal tools for 6.0.0 data? Thanks! Andrew O'Shea, MSc. Research Coordinator III Woods Lab Center for Cognitive Aging and Memory McKnight Brain Institute Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida Phone (Office): (352) 294-5827 Phone (Cell): (586) 899-2771 Email: aoshea(a)phhp.ufl.edu

2 1

recon-all-rms error < 2.00
by jiahui 07 May '20

07 May '20

External Email - Use Caution Hello FreeSurfer Developers, I run the recon-all -all command for aboout 10 hours.1. I want to accelerate, is there any way, such as how to achieve multi-core parallelism? Or is there another way to speed things up? 2. What are the quick ways to check image quality after running results? I've had a lot of subjects and it's not realistic to look at them manually one by one. 1) FreeSurfer version: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c

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Re: [Freesurfer] Compare individual to a group
by Xiaojiang Yang 07 May '20

07 May '20

External Email - Use Caution Hi Douglas, Sorry I didn't find an easy way to include previous email chain. Now I just copied all of them below, so that it's easy to follow. Thanks, Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 14:56:08 -0700 sorry, please include the previous email chain so that I know what you are referring to On 5/7/2020 5:33 PM, Xiaojiang Yang wrote: External Email - Use Caution Douglas, Just as I mentioned in the initial email, subjects in the healthy group have different ages. Because average cortical thickness is believed to be declined as the age gets older, I want to "normalize" the group by multiplying each subject's thickness with an age-dependent scalar (pre-calculated constant). Suppose I take age 30 as the "standard" age, then subjects with ages greater than 30 will have scalars greater than 1 (depend on age); subjects with ages smaller than 30 will have scalars less than 1. In this way, all subjects in the group looks like to be at the same age (30). Of course, I also suspect that there is no need to do the above mentioned normalization if I use mri_glmfit. But my initial intention was using non-linear scalars, and mri_glmft can only use linear fit. Please give me your opinion. Thank you! Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 14:06:27 -0700 I don't know what weight is. Can you elaborate? On 5/7/2020 4:54 PM, Xiaojiang Yang wrote: External Email - Use Caution Hi Douglas, Thank you very much! Could you please help me with more detailed information? 1)Should I use "Variables age" or "Variables age weight" in the fsgd file? If I can use either of them, which one is better? I don't know what weight is. Can you elaborate? 2)For the contrast file, the content "1 -1 0" you mentioned, it corresponds to the "Variables age" fsgd file, correct? Yes 3)If I use the "Variables age weight" fsgd file, what will be the content of the contrast file? Is it "1 -1 0 0"? Yes Thank you again! Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 11:55:27 -0700 I think it will work properly if you use the raw data as input and create an FSGD file with two classes: (1) the individual subject and (2) healthy subjects. Also include age as a covariate. Use DOSS instead of DODS. Then set your contrast to be 1 -1 0 and run mri_glmfit. I think that should properly account for age. On 5/7/2020 2:44 PM, Xiaojiang Yang wrote: External Email - Use Caution Dear FS experts, I have a group of healthy subjects. Given a new individual subject, I want to compare its cortical thickness to the healthy group so that I can find where the thickness is abnormal (thickening or thinning). I use fsaverage as the template subject to calculate mean and std of the healthy group. Since subjects in the healthy group have different ages, I do a "normalization" process to all the subjects in the group BEFORE calculating the mean and std. Thus the normalized healthy group can be regarded as all subjects having the same "standard" age. The normalization process is just multiplying some pre-obtained scale (varied by age, but not linearly ) to the file *?h.thickness.fwhm0.fsaverage.mgh* of each subject that are already calculated by "recon-all --qcache" command. So the mean and std of the group are actually weighted in the sense of Freesurfer's recon-all results. My questions are: 1)If I do vertex-wise t-tests by simply comparing individual subject's thickness to the group using mris_calc but WITHOUT using mri_glmfit, can I still use mri_glmfit-sim to do multiple comparisons correction? If yes, how? 2)If the answer is no for the above question, how should I use mris_glmft to implement my above thoughts so that I can use use mri_glmfit-sim? Specifically, how can I use the weighted mean for the group? 3)Take a step back, if I consider the weight is linearly correlated with the age, how to design the fsdg and contrast file? Thank you very much! Xiao

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Re: [Freesurfer] Compare individual to a group
by Xiaojiang Yang 07 May '20

07 May '20

External Email - Use Caution Douglas, Just as I mentioned in the initial email, subjects in the healthy group have different ages. Because average cortical thickness is believed to be declined as the age gets older, I want to "normalize" the group by multiplying each subject's thickness with an age-dependent scalar (pre-calculated constant). Suppose I take age 30 as the "standard" age, then subjects with ages greater than 30 will have scalars greater than 1 (depend on age); subjects with ages smaller than 30 will have scalars less than 1. In this way, all subjects in the group looks like to be at the same age (30). Of course, I also suspect that there is no need to do the above mentioned normalization if I use mri_glmfit. But my initial intention was using non-linear scalars, and mri_glmft can only use linear fit. Please give me your opinion. Thank you! Xiao

2 1

Re: [Freesurfer] Compare individual to a group
by Xiaojiang Yang 07 May '20

07 May '20

External Email - Use Caution Hi Douglas, Thank you very much! Could you please help me with more detailed information? 1) Should I use "Variables age" or "Variables age weight" in the fsgd file? If I can use either of them, which one is better? 2) For the contrast file, the content "1 -1 0" you mentioned, it corresponds to the "Variables age" fsgd file, correct? 3) If I use the "Variables age weight" fsgd file, what will be the content of the contrast file? Is it "1 -1 0 0"? Thank you again! Xiao

2 1

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Freesurfer May 2020