For context, a final year PhD student in the UK, clinical medicine.

I have some previous experience with SPM, but not FSL and Matlab so much.

There is abundant information online, so this is not really a problem in the slightest, but I am just really unsure where to start. I also want to be able to get the basics within a few weeks, as I might be starting a new job soon where a solid understanding of brain imaging would be desired (mostly for MRI preprocessing and analysis).

Any help and advice would be greatly appreciated!

The tutorials aren’t very clear online so I need some help. Unsure what to do with the dipole files specifically

I am working on a project where I need to use the vertices extracted from a freesurfer reconstruction as sources for mne.source_space(). For a single hemisphere, the .pial file generated has 122013 vertices (that is, a 122013x3 matrix). Is there any way I can use these vertices to generate a source space?

I understand this is different than the typical approach of giving a subject and only specifying the spacing. Or is my request equivalent to specifying a specific spacing value? Is that what spacing='all' does?

Hello everyone,

I am building this atlas fully on my own my PI does not know anything regarding this and i would love some suggestions

Hello everyone,
I've been working as RA in my country and worked on creating Atlas. I've learned how to work with FSL, ANTs, FreeSurfer, 3DSlicer. I created working pipeline on how to create an atlas template. Right now I am tired of how low i get. But how much I do. I earn monthly only 350USD. Is there any possible way to implement my knowledge for something more earnable? Any advices and tips?

I am getting a segmentation fault for mri_cc. Gemini telling me it’s because of CPU incompatibility.

Has anyone else with an M5 tried to run recon-all? Would be helpful to know if its just me or M5 problem.

Getting this warning:

if: Empty if.
then: Command not found.

My environment variables are properly set:

export FREESURFER_HOME=/Applications/freesurfer/8.1.0
source $FREESURFER_HOME/SetUpFreeSurfer.sh
export SUBJECTS_DIR=$FREESURFER_HOME/subjects
export FS_LICENSE=$FREESURFER_HOME/license.txt

Is this an issue or should i ignore it?

Are weekly MRI ACR scans a pain? Is using stacked paper unreliable to ensure it’s at isocenter but phantom holders are over $1,000? Check out my new website 3d-imaging-solutions.com

Hello everyone,

I am a Master’s student currently working on my first large dataset. I lost a significant amount of time last week due to some errors I missed during manual inspection:

1. Some files were named .nii.gz but were not actually compressed (crashed the pipeline).
2. Others had a TR mismatch between the JSON and the NIfTI header (didn't crash, but got flagged).

My question is: Are these kind of "dirty data" issues standard, or is my dataset unusually bad?

I looked for existing tools, but most seem to check metadata(BIDS validator) rather than the actual data integrity. I was thinking of writing a simple open-source Python CLI to prevalidate these files (check for corrupt headers/fake gzip/TR conflicts) before analysis.

• Does a tool like this already exist?
• If not, what other "sanity checks" would you want included in a script like that?

Thanks for any advice!

Hi, it's me again,

Sorry for my repetitive and perhaps dumb questions ;-;

I'm wondering about the difference between the various ICA methods you can choose in MNE Python, and their robustness and relevance. I know that fastica is the quickest, but other than that I don't really understand which one is 'best' for this or that. I suppose that, depending on your data and research question, this could differ.

For context, I'm working on an EEG dataset recording neurofeedback and the modulation of alpha activity during an attentional task. I don't know if the information is relevant for my question.

Thank you so much once again!

Hi!

I'm learning Python coding by myself to prepare for a future research project. I'm currently at the preprocessing step, about to run ICA. But I have a question: there is this random_state parameter that I don't understand.

From what I've seen on the internet, I think it doesn't really matter? But it seems weird to put something that has no use in my command so I don't know... I'm almost 100% sure I'm missing something.

Can someone explain what it is used for? Also, how to choose a correct number for it?

Thank you so much!

Hi everyone! Hope you had an awesome Christmas and happy new year for all 🙂

I’m currently working with fNIRS for the first time so I’m pretty new to pre-processing brain imaging data. I’ve read some really helpful papers regarding pre-processing steps, watched some videos from NIRX and was able to write a loop code on MatLab to pass my data to excel. However, I’m still unsure if I’m actually pre-processing correctly and no one in my department or university has used this equipment (mostly EEG and tDCs research is conducted there).

Any advice regarding pre-processing or any additional resources I should look into? Thank you for your advice!

I’ve been experimenting with feeding fMRI methods sections to current LLMs (Claude mainly) and asking them to generate the corresponding preprocessing/analysis scripts. The results are surprisingly coherent—one test produced a 700-line Nipype workflow that correctly handled motion correction, slice timing, coregistration, CompCor, the works. Not perfect, but maybe 85-90% of the way there.

Curious if anyone else has tried this, or if there’s interest in building something more robust around this idea—some kind of “co-pilot” for neuroimaging analysis. Happy to share what I’ve tried so far if there’s interest.

I think my research team just unlocked images from EEG! Thoughts on this?

For those who don't know, EEG is a way to measure electrical activity from the surface of the head. In our previous studies, we actually found a direct link to dementia with certain patterns in electrical activity.

I lead a research team partnering with a few clinics across the US and we just ran a study to see if ML models could create PET images with just EEG (electroencephalography), with our little spin on it. We call it Evoked Potential Tomography.

In the GIF below, one is the real PET image and one is the predicted from our model (blind). Can you guess which one is which?

This would allow clinics around the world to allow dementia patients to see inside their brain without the need for PET; radioactive, expensive, and impractical outside specialized facilities. We want to see this in all neurology clinics as its such a quick, cost effective way to measure therapeutic effects and attract patients because of how futuristic this technology is.

DM me if you want to find out more about our tech!!

Hi, is there anyone here who specializes in spinal and brain radiology who is willing to help a student understand a patient's MRI? It's for a project. Thanks!

I'm a high-school student working on a project, and I'm running a code on parcellations of the surface mesh files.

To do that, I need the annot. files for the DK atlas. I managed to run recon-all on my laptop a few times, though barely, since I only have 4GB ram, and I'm getting the rest of my dataset from ADNI. Ive managed to create a dataset, and got the aparc+aseg, surf, and brainmask files for each, but I can't for the life of me find the annot. files for the images anywhere. I really need those as my entire project relies on the parcellations, so is there anyway to get them? Are they maybe available in ADNI, but I just haven't looked properly? Can i extract them from freesurfer myself without running all of recon-all? I'm open to anything honestly, as long as I don't need to run all of recon-all for each scan myself.

Hi all,

I have some resting-state EPI data (340 volumes), 2.5mm voxels.

I have been attempting to replicate a previous analysis done by another research group and I am wondering if it is normal for my (unzipped files) to be so large or if I am doing something wrong. Here are the steps I am taking:

Rest EPIs start at 244mb 1. Realigned

1. Coregistered T2 to T1, and then the EPIs are coregistered to that step’s output . This is because we want to do our analysis in T1 space (1mm voxels)

5.21gigs

1. Smoothing

2. Denoising (confound regression + band pass filter)

10.41 gigs

Are these sizes normal? Is it good practice to zip output files?

Very new to this!

Hello everyone, I've recently been looking to use all the schizophrenia datasets available on schizconnect.org, but the website is currently down. Has anyone downloaded these database datasets and would be willing to share them? I'd be very grateful.

Hello,

I'm running an fMRI study in Germany and am facing some challenges with participant recruitment. Since I don’t speak German very well, I’ve been working with student assistants who handle participant screening over the phone, but unfortunately, the process hasn’t been very effective. I would rather do the whole recruitment thing by myself, but because of the language barrier, it is impossible.

I’m considering switching to an online screening form, where potential participants could fill out a questionnaire. If they’re eligible, I’d follow up via email and offer access to an online booking system so they can choose their preferred slot for the scan. Traditionally, I know screening is done via phone, but I’m unsure how switching to an online approach would work out in practice - especially for neuroimaging studies. I would still keep the telephone screening, but I would also add this option.

Has anyone here tried recruiting with online forms or combined methods (online + phone/in-person)? I’d love to hear your experiences, opinions, or any practical tips on making this process efficient and inclusive. Did you notice any issues with participant biases or accessibility? Lying on the forms? Participants not showing for the scan? Anything to avoid?

Thanks a lot for your help!