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30088 discussions

recon-all skull stripping
by Inglese, Marianna 16 Oct '19

16 Oct '19

External Email - Use Caution To whom it may concern, I am using recon-all to segment many T1s. As I can see, one of the automatic step performed by recon-all is the skull stripping. Would it be possible to know the threshold used for the stripping? I guess it is the same threshold everytime. I would like to apply the skull stripping only on other T1s and need to get the same result. Thank you very much for the help. Yours faithfully, Marianna Inglese Research fellow, Imperial College London Department of Surgery and Cancer

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Postdoctoral Positions in Pediatric Neuroimaging at the Waisman Center, University of Wisconsin Madison
by Doug Dean III 16 Oct '19

16 Oct '19

External Email - Use Caution Postdoctoral Research Associate Waisman Center, University of Wisconsin–Madison, Madison WI The Waisman Laboratory for Brain Imaging & Behavior at the Waisman Center, University of Wisconsin–Madison, is seeking highly motivated postdoctoral researchers to work on neuroimaging research studies of young children and individuals with intellectual and developmental disorders (IDDs). Areas of research include the development and evaluation of image acquisition and analyses for brain imaging studies of infants, young children, and individuals with autism, Down syndrome and fragile X syndrome. The Alexander and Dean labs at the Waisman Laboratory for Brain Imaging & Behavior have multiple projects using MRI and functional near infrared spectroscopy (fNIRS). Work is ongoing to develop rapid, quiet and motion-correction imaging methods to improve studies in these challenging populations. Studies are also under way to characterize brain structure, microstructure and function change during early brain development and how development is affected in IDDs. The postdoctoral research associates will have opportunities to participate in activities related to neuroimaging methods development and evaluation, study data collection and analyses, research publications, grant writing and are expected to make positive scholarly contributions to our interdisciplinary and highly collaborative research programs. Desired Qualifications: Ph.D. in medical physics, neuroscience, engineering and/or a related field. Prior experience with imaging techniques (e.g. MRI, DTI, fMRI, PET, fNIRS) is preferred. Experience with brain image analysis software tools, MATLAB, Python, C++ and/or other computer programming is highly desirable. Pulse sequence programming experience would be a plus but not required. The candidate is expected to have an emerging track record of publishing in academic journals, strong analytic skills, and capable of working within a collaborative environment. Interested candidates should send: 1) curriculum vitae; 2) cover letter outlining research interests, experience, and career goals; and 3) a list of three references to either Dr. Andy Alexander at alalexander2(a)wisc.edu<mailto:alalexander2@wisc.edu> or Dr. Douglas Dean at deaniii(a)wisc.edu<mailto:deaniii@wisc.edu>.

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Postdoc Position at UPenn
by Mackey, Allyson P 16 Oct '19

16 Oct '19

External Email - Use Caution Postdoctoral Fellowship in Pediatric Neuroimaging at the University of Pennsylvania Drs. Allyson Mackey and Dylan Tisdall are seeking a postdoctoral fellow for an NIH-funded study on early childhood brain development. The goals of the study are to develop motion-resilient structural and functional MRI sequences, and to validate the new sequences in 100 children between the ages of 3 and 5. Children will be recruited from high-poverty, high-opioid use neighborhoods of Philadelphia. This study is part of the HEALthy Brain and Child Development (HBCD)<https://heal.nih.gov/research/infants-and-children/healthy-brain> planning initiative. Ultimately, HBCD will enroll pregnant mothers and follow children for at least 10 years, and will include data on children’s experiences, brain structure and function, and social, emotional, and cognitive development. The postdoctoral fellow will join the Changing Brain Lab<http://www.allysonmackey.com/> at the University of Pennsylvania, an interdisciplinary research group that studies how young children’s experiences change their brains. We are interested in the development of academically-relevant skills including reasoning, curiosity, creativity, and persistence. The CBL is part of a vibrant psychology and neuroscience community at Penn, and is affiliated with mindCORE<https://mindcore.sas.upenn.edu/>, the Center for Neuroscience and Society<https://neuroethics.upenn.edu/>, and the Positive Psychology Center<https://ppc.sas.upenn.edu/>. Imaging is conducted at the Center for Functional Neuroimaging<https://cfn.upenn.edu/facilities.htm>, which includes research-dedicated 3T Siemens Prisma and 7T Siemens Terra scanners. We welcome candidates with doctoral degrees in a wide variety of fields, including physics, engineering, statistics, neuroscience, and psychology. Applicants should have strong publication records, experience with pediatric neuroimaging, strong statistical training, and excellent computational skills. To apply, please send a cover letter describing your doctoral research and your goals for postdoctoral training, as well as your CV to Allyson Mackey at mackeya(a)upenn.edu<mailto:mackeya@upenn.edu>. As the position involves collaborating with multiple advisors, graduate students, and research assistants, please provide contact information for someone who has mentored you, and for someone you have mentored. Applications will be reviewed as they are received.The position is for one year, renewable for up to 3 years total. Ideal start date is on or before February 1st, 2020. -- Allyson Mackey, Ph.D. Assistant Professor Department of Psychology University of Pennsylvania 425 S. University Avenue Levin Building, Room 354 Philadelphia, PA 19104 215-573-3074

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FS-FAST: cluster size from 1st level analysis
by Keri Woods 16 Oct '19

16 Oct '19

External Email - Use Caution Hi From the 1st level analysis of an fMRI study I'm trying to extract the size of the clusters of activation from each subject, but don't know how to go about this. I know that for the group analysis you can get the cluster sizes from the output from mri_glm-sim. Is there a way to run the multiple comparison correction on the first level analysis in order to get this table? Or is there another way of getting the cluster sizes? Any help would be much appreciated. Thank you, Keri

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Notice on freesurfer list archives
by Hoopes, Andrew 15 Oct '19

15 Oct '19

Hi all, Our primary archive for all posts to the mailing list can be found at: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu And we've now disabled the public 'pipermail' archives, which include anything under: https://mail.nmr.mgh.harvard.edu/pipermail/freesurfer If you come across any broken links pointing to the above, please let us know. best Andrew

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Re: [Freesurfer] ERROR gtmseg
by Marina Fernández 15 Oct '19

15 Oct '19

External Email - Use Caution Dear Douglas, I put the subjects yesterday. They are: qsm_04_yr3 (it is the first subject with problems and we have another copy that is solved) and mci13_hsp (it is the new subject with problems). Thank you very much for your attention. Marina.

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Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 21
by Juan Rivas 14 Oct '19

14 Oct '19

External Email - Use Caution Dear Juan Eugenio, thanks for your answer. I tried to do with the corrected images and wit the raw ones (before the correction), and I got the same error. Then I tried with two patients more and happened the same. I don´t know if it is due to the dev version we used, because the first time it worked very well. Best regards, JC. El dom., 13 oct. 2019 a las 12:01, <freesurfer-request(a)nmr.mgh.harvard.edu> escribió: > Send Freesurfer mailing list submissions to > freesurfer(a)nmr.mgh.harvard.edu > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > or, via email, send a message with subject or body 'help' to > freesurfer-request(a)nmr.mgh.harvard.edu > > You can reach the person managing the list at > freesurfer-owner(a)nmr.mgh.harvard.edu > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Freesurfer digest..." > > > Today's Topics: > > 1. HippoAmyg (Juan Rivas) > 2. Learning dti processing tutorial (Renew Andrade) > 3. Re: HippoAmyg (Iglesias Gonzalez, Juan E.) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 12 Oct 2019 16:02:28 -0400 > From: Juan Rivas <jcrn12(a)gmail.com> > Subject: [Freesurfer] HippoAmyg > To: freesurfer(a)nmr.mgh.harvard.edu > Message-ID: > <CACYE61CZSKt0p-q= > k+Yph1dDvpW4O67qehmXfhVpem7gqobdUg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > External Email - Use Caution > > *Hi, I runned the reconall of my images with FS60 with this command:* > > *shiraz[0]:NIFTI$ recon-all -i > > /autofs/cluster/neuromod/rivas/imagenes/NIFTI/sub-esq-02-en/anat/sub-esq-02-en_T1w.nii.gz > -s /autofs/cluster/neuromod/rivas/subject-esq-02-en. There were no errors.* > > *Then I runned recon for hippocampus and amygdala with fsdev on Thu Aug 22 > 15:36:32 , with this command:* > > *segmentHA_T1.sh* > > *There were no errors. Then I identified and corrected manually the errors > on the FS60 images.* > > *Then I run recon-all on fs60 without to touch hippo-amyg.* > > *Now, I am trying to make the hippo-amyg correction with this command:* > > *segmentHA_T1.sh on fsdev, and I got this error:* > > > > [shiraz:FS] (nmr-dev-env) segmentHA_T1.sh test1 > > #-------------------------------------------- > > #@# Hippocampal Subfields processing (T1) left Fri Oct 11 17:20:27 EDT 2019 > > /usr/bin/time -o /dev/stdout > > @#@FSTIME 2019:10:11:17:20:27 run_segmentSubjectT1_autoEstimateAlveusML.sh > N 13 e %e S %S U %U P %P M %M F %F R %R W %W c %c w %w I %I O %O L 1.23 > 1.35 1.67 > > run_segmentSubjectT1_autoEstimateAlveusML.sh > /usr/local/freesurfer/dev/MCRv84/ test1 /cluster/neuromod/rivas/imagenes/FS > 0.333333333333333333333333333333333333 > /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasMesh.gz > /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasDump.mgz > /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt > 0.05 left L-BFGS v21 /usr/local/freesurfer/dev/bin/ 0 > > ------------------------------------------ > > Setting up environment variables > > --- > > LD_LIBRARY_PATH is > > .:/lib64:/usr/local/freesurfer/dev/MCRv84//runtime/glnxa64:/usr/local/freesurfer/dev/MCRv84//bin/glnxa64:/usr/local/freesurfer/dev/MCRv84//sys/os/glnxa64:/native_threads:/server:/client:: > > Registering imageDump.mgz to hippocampal mask from ASEG > > $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ > > > > --mov: Using imageDump.mgz as movable/source volume. > > --dst: Using > > /cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz > as target volume. > > --lta: Output transform as trash.lta . > > --mapmovhdr: Will save header adjusted movable as > imageDump_coregistered.mgz ! > > --sat: Using saturation 50 in M-estimator! > > > > reading source 'imageDump.mgz'... > > reading target > > '/cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... > > > > Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) > > Type Source : 0 Type Target : 3 ensure both FLOAT (3) > > Reordering axes in mov to better fit dst... ( -1 3 -2 ) > > Determinant after swap : 0.015625 > > Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) > > Dst: (1, 1, 1)mm and dim (37, 33, 61) > > Asserting both images: 1mm isotropic > > - reslicing Mov ... > > -- changing data type from 0 to 3 (noscale = 0)... > > -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. > > -- Resampled: (1, 1, 1)mm and (37, 33, 61) voxels. > > -- Reslicing using cubic bspline > > MRItoBSpline degree 3 > > - no Dst reslice necessary > > > > > > Registration::computeMultiresRegistration > > - computing centroids > > - computing initial transform > > -- using translation info > > - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) > > - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) > > - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) > > - initial transform: > > Ti = [ ... > > 1.0000000000000 0 0 -0.9335110261151 > > 0 1.0000000000000 0 -0.6030053897425 > > 0 0 1.0000000000000 -1.9033167008449 > > 0 0 0 1.0000000000000 ] > > > > - initial iscale: Ii =1 > > > > Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) > > Iteration(f): 1 > > -- diff. to prev. transform: 17.9258 > > Iteration(f): 2 > > -- diff. to prev. transform: 13.3727 > > Iteration(f): 3 > > -- diff. to prev. transform: 12.6349 > > Iteration(f): 4 > > -- diff. to prev. transform: 0.963353 > > Iteration(f): 5 > > -- diff. to prev. transform: 0.23376 max it: 5 reached! > > > > - final transform: > > Tf = [ ... > > 0.9994502743718 -0.0309610775894 -0.0118558311665 0.1083605389283 > > 0.0330356012422 0.9601637341023 0.2774783825190 -11.0026122646332 > > 0.0027925093931 -0.2777175100517 0.9606587253036 3.8161835807274 > > 0 0 0 1.0000000000000 ] > > > > - final iscale: If = 1 > > > > ********************************************************** > > * > > * WARNING: Registration did not converge in 5 steps! > > * Problem might be ill posed. > > * Please inspect output manually! > > * > > ********************************************************** > > > > Final Transform: > > Adjusting final transform due to initial resampling (voxel or size changes) > ... > > M = [ ... > > -0.2498625685929 -0.0029639577916 0.0077402693973 33.8236195063683 > > -0.0082589003105 0.0693695956298 -0.2400409335256 17.7299867104994 > > -0.0006981273483 0.2401646813259 0.0694293775129 -3.6765424847757 > > 0 0 0 1.0000000000000 ] > > > > Determinant : -0.015625 > > > > > > writing output transformation to trash.lta ... > > converting VOX to RAS and saving RAS2RAS... > > > > mapmovhdr: Changing vox2ras MOV header (to map to DST) ... > > > > To check aligned result, run: > > freeview -v > > /cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz > imageDump_coregistered.mgz > > > > > > Registration took 0 minutes and 1 seconds. > > > > Thank you for using RobustRegister! > > If you find it useful and use it for a publication, please cite: > > > > Highly Accurate Inverse Consistent Registration: A Robust Approach > > M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. > > http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 > > http://reuter.mit.edu/papers/reuter-robreg10.pdf > > > > $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ > > > > --mov: Using imageDump.mgz as movable/source volume. > > --dst: Using > > /cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz > as target volume. > > --lta: Output transform as trash.lta . > > --mapmovhdr: Will save header adjusted movable as > imageDump_coregistered.mgz ! > > --affine: Enabling affine transform! > > --sat: Using saturation 50 in M-estimator! > > > > reading source 'imageDump.mgz'... > > reading target > > '/cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... > > > > Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) > > Type Source : 0 Type Target : 3 ensure both FLOAT (3) > > Reordering axes in mov to better fit dst... ( -1 3 -2 ) > > Determinant after swap : 0.015625 > > Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) > > Dst: (1, 1, 1)mm and dim (37, 33, 61) > > Asserting both images: 1mm isotropic > > - reslicing Mov ... > > -- changing data type from 0 to 3 (noscale = 0)... > > -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. > > -- Resampled: (1, 1, 1)mm and (37, 33, 61) voxels. > > -- Reslicing using cubic bspline > > MRItoBSpline degree 3 > > - no Dst reslice necessary > > > > > > Registration::computeMultiresRegistration > > - computing centroids > > - computing initial transform > > -- using translation info > > - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) > > - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) > > - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) > > - initial transform: > > Ti = [ ... > > 1.0000000000000 0 0 -0.9335121201217 > > 0 1.0000000000000 0 -0.6030049400697 > > 0 0 1.0000000000000 -1.9033196349668 > > 0 0 0 1.0000000000000 ] > > > > - initial iscale: Ii =1 > > > > Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) > > Iteration(f): 1 > > -- diff. to prev. transform: 29.7552 > > Iteration(f): 2 > > -- diff. to prev. transform: 13.9258 > > Iteration(f): 3 > > -- diff. to prev. transform: 12.8176 > > Iteration(f): 4 > > -- diff. to prev. transform: 4.31284 > > Iteration(f): 5 > > -- diff. to prev. transform: 1.08934 max it: 5 reached! > > > > - final transform: > > Tf = [ ... > > 1.1485282870250 0.1923732018210 0.0456719546813 -8.8430815689836 > > 0.0248585691740 1.1974718877709 0.2901612074595 -15.4946754855698 > > 0.0132871026014 0.0262311630292 0.9721734242629 -1.7628900186542 > > 0 0 0 1.0000000000000 ] > > > > - final iscale: If = 1 > > > > ********************************************************** > > * > > * WARNING: Registration did not converge in 5 steps! > > * Problem might be ill posed. > > * Please inspect output manually! > > * > > ********************************************************** > > > > Final Transform: > > Adjusting final transform due to initial resampling (voxel or size changes) > ... > > M = [ ... > > -0.2871321056267 0.0114179894966 -0.0480933061884 36.4485194686672 > > -0.0062146408039 0.0725403105008 -0.2993680076302 19.7524929053736 > > -0.0033217759975 0.2430433850326 -0.0065577915391 -0.1873902167255 > > 0 0 0 1.0000000000000 ] > > > > Determinant : -0.020683 > > > > Decompose into Rot * Shear * Scale : > > > > Rot = [ ... > > -0.9973893744923 0.0079822957206 -0.0717685070543 > > 0.0722067536928 0.1210866300984 -0.9900122285773 > > -0.0007876362909 0.9926098483122 0.1213468939142 ] > > > > Shear = [ ... > > 1.0000000000000 -0.0253544642652 0.0881389503515 > > -0.0221787471386 1.0000000000000 -0.1442736100723 > > 0.0921761860222 -0.1724865310828 1.0000000000000 ] > > > > Scale = diag([ 0.2859363885412 0.2501220610640 0.2990338055489 ]) > > > > > > writing output transformation to trash.lta ... > > converting VOX to RAS and saving RAS2RAS... > > > > mapmovhdr: Changing vox2ras MOV header (to map to DST) ... > > > > To check aligned result, run: > > freeview -v > > /cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz > imageDump_coregistered.mgz > > > > > > Registration took 0 minutes and 1 seconds. > > > > Thank you for using RobustRegister! > > If you find it useful and use it for a publication, please cite: > > > > Highly Accurate Inverse Consistent Registration: A Robust Approach > > M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. > > http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 > > http://reuter.mit.edu/papers/reuter-robreg10.pdf > > > > Reading contexts of file > /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt > > Constructing image-to-world transform from header information > (asegModCHA.mgz) > > Constructing image-to-world transform from header information > > (/cluster/neuromod/rivas/imagenes/FS/test1/tmp/hippoSF_T1_v21_left/imageDump.mgz) > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Transforming points > > Reading contexts of file > /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt > > -------------- > > Making Left-Cerebral-Cortex map to reduced label 1 > > Making alveus map to reduced label 1 > > Making subiculum-body map to reduced label 1 > > Making subiculum-head map to reduced label 1 > > Making Hippocampal_tail map to reduced label 1 > > Making molecular_layer_HP-body map to reduced label 1 > > Making molecular_layer_HP-head map to reduced label 1 > > Making GC-ML-DG-body map to reduced label 1 > > Making GC-ML-DG-head map to reduced label 1 > > Making CA4-body map to reduced label 1 > > Making CA4-head map to reduced label 1 > > Making CA1-body map to reduced label 1 > > Making CA1-head map to reduced label 1 > > Making CA3-body map to reduced label 1 > > Making CA3-head map to reduced label 1 > > Making HATA map to reduced label 1 > > Making fimbria map to reduced label 1 > > Making presubiculum-body map to reduced label 1 > > Making presubiculum-head map to reduced label 1 > > Making parasubiculum map to reduced label 1 > > Making Lateral-nucleus map to reduced label 1 > > Making Paralaminar-nucleus map to reduced label 1 > > Making Basal-nucleus map to reduced label 1 > > Making Accessory-Basal-nucleus map to reduced label 1 > > Making Corticoamygdaloid-transitio map to reduced label 1 > > Making Central-nucleus map to reduced label 1 > > Making Cortical-nucleus map to reduced label 1 > > Making Medial-nucleus map to reduced label 1 > > Making Anterior-amygdaloid-area-AAA map to reduced label 1 > > -------------- > > Making Left-Cerebral-White-Matter map to reduced label 2 > > -------------- > > Making Left-Lateral-Ventricle map to reduced label 3 > > -------------- > > Making Left-choroid-plexus map to reduced label 4 > > -------------- > > Making hippocampal-fissure map to reduced label 5 > > Making Unknown map to reduced label 5 > > -------------- > > Making Left-VentralDC map to reduced label 6 > > -------------- > > Making Left-Putamen map to reduced label 7 > > -------------- > > Making Left-Pallidum map to reduced label 8 > > -------------- > > Making Left-Accumbens-area map to reduced label 9 > > -------------- > > Making Left-Caudate map to reduced label 10 > > Error using segmentSubjectT1_autoEstimateAlveusML (line 365) > > The vector of prior probabilities in the mesh nodes must always sum to one > over all classes. > > > > > How may I correct it? > > Thanks lot, > > JC. >

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help
by Asim Hassan 14 Oct '19

14 Oct '19

External Email - Use Caution Good afternoon, I would like to know is there a way to run recon-all on subjects in which manual edits have been done on the brainmask.mgz files? Or at least incorporate manually edited brainmask files into the recon-all process? Thanks! Asim Hassan Research Technician II Division of Pediatric Neurology University of Texas Southwestern Medical Center ________________________________ UT Southwestern Medical Center The future of medicine, today.

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BIDS Governance and Steering Group election closing tomorrow!
by Franklin Feingold 14 Oct '19

14 Oct '19

External Email - Use Caution Time is running out to vote on our BIDS Governance document (https://docs.google.com/document/d/1R-J2lL9V_wIkYhye4zH-feyl4P4J8NyO40rIYyY…) and for the inaugural Steering Group (https://docs.google.com/document/d/1yb6j3a90serWbtab7ukSMyecs16sGfYTSGftH-4…) Voting closes tomorrow October 15, 2019, at 11:59pm Pacific Daylight Time (UTC -7). Votes will be recorded using a Google form (https://forms.gle/YT5SvPgACHhVvf6x8) and a separate form for email validation (https://docs.google.com/forms/d/1zr-w3M2K7ApaW3yePXNRc1zYMhIiDsOVnG15nJ1sgx…) to make sure the vote is confirmed. We created separate forms to dissociate contact information from voting information, and no attempt will be made to re-associate these data. Contact information will be used to ensure that the number of votes matches the number of voters. All email addresses will be deleted immediately once the election results have been verified, with the exception of voters who opt to have their email address retained. If you have any questions regarding this process or BIDS please feel free to email Franklin ([ffein@stanford.edu](mailto:ffein@stanford.edu)) Thank you for taking the time to vote! Franklin

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Learning dti processing tutorial
by Renew Andrade 13 Oct '19

13 Oct '19

External Email - Use Caution Dear freesurfer experts: I am trying to learn dti processing. For using Tracula it appears there is a need for running "recon-all -all -i -s” on every subject before starting to run Tracula. But there seems to be a problem. I have a Parkinson dwi images database and every subject gives me an error of the type “input(s) cannot have multiple frames!”. I am a little bit stuck on this step. Is it a problem of the file as input? Am I putting the wrong file? Can it be done a recon-all analysis to a dwi image? With trackvis I can obtain the results without any problem. My doubt comes from FreeSurfer only and may be my little knowledge about it. Thanks for your help! Sincerely, Andrade.

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