Freesurfer April 2023

freesurfer@nmr.mgh.harvard.edu

80 participants
105 discussions

extracting values from signifciant clusters
by Eszter Boros 28 Apr '23

28 Apr '23

External Email - Use Caution Dear FreeSurfer Team, We are doing a longitudinal analysis with Freesufer 7.1.1 (two-stage longitudinal model), and we would like to extract the Cortical thickness values from the significant clusters (before and after treatment changes) to visualize the individual values/changes. I am wondering how I can do that? Please let me know if you need any more information. Thank you for your answer in advance! All the best, Eszter Boros

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Subcortical regions of Brainnetome Atlas
by Garrett Tan 28 Apr '23

28 Apr '23

External Email - Use Caution Hi FreeSurfer Experts, I am using Brainnetome Atlas in FreeSurfer to calculate the brain volume. The cortical regions are done smoothly by using mris_ca_label->mris_anatomical_stats with materials on https://secure-web.cisco.com/1p7TY1frW67uqViolqd97Sub3WUDGgPjPK0_YAEmmtEENB… However, when I was trying to get the volume of subcortical regions (the hippocampus, amygdala, thalamus, and basal ganglia), they are excluded since the ?h.cortex.label files do not include them. My plan was to use aseg.mgz to get each subcortical regions and do the same procedure as for the cortical regions. However, mri_ca_label did not allow me to create .annot from BN_Atlas_subcortex.gca from Brainnetome website. So I continued using mris_convert to convert .gca to label.mgz, and I am stuck here. Is there any way to get the subcortical regions’ .gcs file, or even an easier way to calculate the volume of subcortical regions in brainnetome atlas from FreeSurfer? Best, Garrett

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Extracting statistical values from significant ROIs after running GLM
by Akansha Mahesh Naraindas 28 Apr '23

28 Apr '23

External Email - Use Caution Hi I have run a GLM using cortical thickness and a behavioural measure on specific ROIs, I have a query about extracting the statistical values, specifically the test statistic from the significant regions after running the GLM but prior to multiple comparison correction. Is there any way to extract the test statistic of each vertex of the significant regions? Thanks Akansha

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visualization, heatmap
by Eszter Boros 27 Apr '23

27 Apr '23

External Email - Use Caution Dear Freesurfer team, I noticed that some studies/publications add figures with heatmaps to visualize the significant cortical results (after correction), others do not. We tried to use the heat map (from yellow/red to dark blue/light blue) to visualize the group differences using Freeview but after correcting for multiple corrections (after mri_glm we used Monte Carlo simulation), we are not sure how to do that. Only the sig.mgh file can be visualized that way, right? Or that was available in tksurfer but not in Freeview? https://secure-web.cisco.com/1iHssxc0c4EPn_NgIsbQnxACsBkIj4sWVm7WSdle6CMXnC… Is tksurfer still available? We use Freesurfer 7.1.1. Thank you for your help in advance! Eszter

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FSFAST: wrong brain mask in preproc
by Elmira Hassanzadeh 27 Apr '23

27 Apr '23

External Email - Use Caution Hi everyone, We preprocessed resting-state data with preproc-sess. In the output, I noticed although *fmcpr.nii* covers the whole brain,* fmcpr.sm6.mni305.2mm.nii* is only covering brain stem+cerebellum (screenshots attached). Any clue what went wrong? This is an example of the command we ran for one of our subjects(R19): *preproc-sess -surface R19 lhrh -s R19 -fwhm 6 -fsd rest -mni305 -per-run* Thanks, Elmira

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questions about mri_normalize
by John Anderson 27 Apr '23

27 Apr '23

External Email - Use Caution Dear experts, I have been using the mri_normalize in FSv7.3.2 to standardize the intensity of FLAIR images , and I've observed that increasing the sigma parameter up to 25 is effective in improving the quality of the normalized images for noisy datasets. mri_normalize -sigma 25 -g 1 FLAIR_skullstripped.nii FLAIR_skullstripped_norm.mgz However, I have questions about the n argument, which is set to 2 by default. For noisy images, white matter intensity after normalization is around 140-150. When using n=10, I noticed that the white matter intensity decreased from 140-150 to around 110-120, resulting in better quality normalized images. 1) I'm wondering if there is a cut-off value for the number of iterations, or if a higher number of iterations always yields better results. 2) Also, if the intensity of the white matter after normalization is not around 110, but instead around 140 or 150, does that indicate that the normalization was not effective and that we need to increase the number of iterations? Thank you for your assistance. John

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Re: [Freesurfer] EasyReg for group-average template?
by AmirHussein Abdolalizadeh 27 Apr '23

27 Apr '23

External Email - Use Caution Hi Eugenio, Thanks for your reply. Just to check if I've understood correctly, these can be the steps by using mri_easyreg: *Linear (with --affine-only):* 1. Register all the subjects to the MNI (subj_to_MNI_lin_1) 2. Average the subj_to_MNI_lin_1s to reach a new template (MNI_mod_lin_1) 3. Register subj_to_MNI_lin_1s to MNI_mod_lin_1 and achieve subj_to_MNI_lin_2 and MNI_mod_lin_2 (and repeat it 5-6 times) *NonLinear:* 1. Register subj_to_MNI_lin_5s to MNI_mod_lin_5 nonlinearly to achieve subj_to_MNI_nlin_1 and MNI_modified_nlin_1. 2. Repeat the steps similarly 5 times. Correct? I also have another question: Should I do the linear-nonlinear in an interleaved manner or first linear registrations iterations and then non-linear? Best, Amir

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Does Incorporating T2 into recon-all impact ICV and local morphometric estimates?
by Steven Meisler 26 Apr '23

26 Apr '23

External Email - Use Caution Hi FS team, I am running a morphometry study on a public dataset with most, but not all, subjects having a T1 and T2 (about 75% have both, remaining only have T1). To get the best surfaces possible, I have been including the T2 in recon-all process when able to. However, this means subjects with only a T1 have underwent a different processing workflow. ICV is an important predictor in my study, and I was wondering if the presence of the T2 image in recon-all should impact the ICV estimates? As I see it, I have the following options: 1) T2 is a factor, so I use only include T1 in recon-all so all subjects have the same pipeline 2) T2 is a factor, so I harmonize the data as I would with data from different sites/scanners 3) T2 is a factor, so I only analyze subjects with T1 + T2 4) T2 is not a factor, just use all subjects with no special considerations I am also running vertex-wise surface based morphometry, where I imagine pial refinements could be more impactful. I suppose in that case, I should harmonize the morphometry data, but was curious if you think something like that is necessary. Thank you, Steven Meisler

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FSFAST: multiple resting state runs
by Elmira Hassanzadeh 26 Apr '23

26 Apr '23

External Email - Use Caution Hi, How do we analyze multiple resting state runs for a subject at once? - do we need certain folder structure/naming? - do we do concatenation? Thanks, Elmira

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Neuroimaging Postdoctoral Fellowship Position: Bronte-Stewart Lab at Stanford University
by Kevin Bryant Wilkins 26 Apr '23

26 Apr '23

External Email - Use Caution The Human Motor Control and Neuromodulation Lab under Dr. Helen Bronte-Stewart (https://secure-web.cisco.com/1--QzHnbqbSp-JZkXqnRQUXC9NwJSM-Wjso7zaYuabDXqn…) part of the Stanford Movement disorders Center within the Department of Neurology and Neurological Sciences at Stanford University’s School of Medicine, is seeking a postdoctoral scholar with neuroimaging expertise to investigate cognitive-motor dysfunction in Parkinson’s disease. The current position is for a project where we will be conducting a pilot clinical trial using a novel deep brain stimulation approach for cognitive and cognitive-motor dysfunction in Parkinson’s disease. The candidate will help validate the use of a novel target for deep brain stimulation using a combination of tractography and DBS modeling (e.g., volume of tissue activated). You will work together with neurosurgeons, radiologists, and neurologists on the trial to validate various trajectories and targets. You will work with stimulation modeling software to individualize stimulation parameters for participants to optimize efficacy. In addition to your role on this project, you will get the chance to investigate structural differences in brain morphology between different neurodegenerative diseases. The current position offers an exceptional opportunity for neuroscientists with a background in structural neuroimaging and/or cognition to apply these skills in a clinically-relevant domain. The desired candidate would have a PhD in Neuroscience or a related discipline with experience in neuroimaging, cognition, and motor control. Preferred start date is before September 2023 but is flexible. Job Requirements Required: PhD in Neuroscience or related disciplines with expertise in data science Experience in structural neuroimaging (tractography, voxel-based morphometry, etc.) Experience working in the area of cognition and motor control Comfortable analyzing data in Matlab, Python, or similar languages Strong data science skills Ability to work well in a diverse team Effective oral and written communication skills Excellent organization skills and demonstrated ability to complete detailed work accurately Excellent problem-solving ability Preferred: Experience with Parkinson’s disease or other clinical populations Experience with deep brain stimulation modeling (volumes of tissue activated, etc.) Experience with larger data sets How to Apply: Apply by sending the following to Kevin Wilkins (wilkins2(a)stanford.edu) CV Cover letter describing interest and relevant experience for the project Three potential reference contacts (We will not contact references until confirming with you) The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY23 minimum is $68,238. Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce. It welcomes applications from women, members of minority groups, veterans, persons with disabilities, and others who would bring additional dimensions to the university's research and teaching mission

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Freesurfer April 2023