00 - Overview

Overview about how to get from DICOM files from the Scanner to your GLM Analysis

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Published

July 28, 2025

1 General

This page gives an overview and brief information about the necessary steps to get from the DICOM files to a GLM Analysis. You can find more detailed information by navigating to the sub-pages by clicking on the caption of the respective point.

1.1 Two ways to your data

Regular scanning time:
- you get the data as nifti files
- a few days later via mri-dropoff in the J drive
- step 1 and 4 generally are already done for you then (but check the defacing anyway, sometimes this doesn´t work!)
Scanning with Natalia
- we have the raw DICOM files
- conversion to niftis has to be done (see 1)
- defacing has to be done (see 4)
- IMPORTANT: Load the DICOM files to the server and delete them from any external/local device (USB-Stick, local storage of your computer, etc.) as soon as possible!!!

2 DICOM-Files

the raw files from the scanner are DICOM-Files
not really useful since we need .nifti files for our analysis \(\qquad\to\) first step is to transform the DICOM- to .nift-files
need to be converted to nifti files

DELETE DICOMS FROM YOUR OWN DEVICES AS SOON AS POSSIBLE

Load the DICOM files to the server and delete them from any external/local device (USB-Stick, local storage of your computer, etc.) as soon as possible!!!

3 DICOM to nifti

using a script from Adam that can be found here (or here: /shared/dcm2bids.sh)
results in .nifti files in BIDS format

4 Check the “IntendedFor” entry of the fmap’s JSON file

tells fmriPrep on what files distortion correction should be applied to
depending on the fMRIprep version you plan to use, the field has to look slightly different!
to avoid unnecessary problems and delays, it doesn´t harm to always check this field before running fMRIprep!

5 Distribute the T1w files to participants?

if you promised participants that they will get their T1w images, you should make sure that they really get them
this should be done before defacing
You should delete the “non-defaced” files either after you distributed them to the respective participants, or after your study ended

6 Defacing your data

defaced data is necessary to savely run fMRIprep
Save faced T1w-files to give to your participants (if you got the data during regular scanning times, there should be a faced directory, but you should still make sure, that you have the faced T1w files if you need them for your participants!)
NEVER share data (e.g. on open Neuro) that was not defaced before!
Defacing is done with pydeface (Docker; Version 2.0.2)

7 (Remove denoising scan from your data)

if a denoising sequence was used in the scanner, but you don´t use denoising in your analysis, you might have to remove the last volume of each scan (which basically is a noise scan) before continuing

8 fmriprep

one of many BIDS-Apps (that require a BIDS-valid Dataset)
combines various anatomical and functional preprocessing steps within one tool
necessary, time intense process

9 Retinotopic Mapping

if you want to specifically test potential effects in specific visual areas (e.g. V1, V2, etc.), you might need to do retinotopic mapping
this allows you to differentiate different visual areas, eccentricity and more

10 first-level Analysis

in the first level GLM you process the data of individual subjects
for each subject, you define/estimate a model that can explain your data
you define meaningful contrasts (e.g. your experimental conditions)
you account for potential confounds

11 second-level Analysis

in the second level GLM you extract the relevevant (for your reasearch questions/hypothesis) information from your first level GLM
you should save this information in a format that allows you to later run your statistical analysis

12 Statistical Analysis

with your extracted data (that are saved in a .csv file), you can now run your statistical analysis