The goal of this project was to use a subset of an open fMRI dataset (Prevent-AD) to create a tutorial in the form of a Jupiter notebook, to pre-process fMRI data using fMRIPrep. The project was also aiming to develop a basic Nilearn pipeline to extract functional connectivity atlases. All these operations were initially planned be done on an HPC server (i.e. Beluga) using Slurm Job submission.
Having little experience with neuroimaging and my PhD thesis using fMRI, I wanted to be able to start from raw data (in BIDS format) and learn how to process the data until I obtain derivatives (i.e. connectivity matrices). Based on the current PhD project that I am working on, using various atlases leads to varying results. As such, I wanted to extract connectivity matrices from pre-processed time-series using Nilearn, which comes pre-loaded with several atlases of interest. Finally, as most of my work will involve large cohorts (which might not be feasible to do on a personnal computer), I wanted to be able to realize these analyses on a HPC, where ressources can be used appropriatly.
Data: A subset of 10 subjects, with 2 scans each, from the open data of the PREVENT-AD (part of the CONP initiative)
Preprocessing tool: fMRIPrep, an open fMRI processing software, available through a Singularity container
Matrice extraction: Nilearn, an open Python package, allowing for extraction of connectivity matrices based on specific atlases
HPC: Beluga
The deliverables will take the form of 1) Fingerprinting analyses using the 10 subjects and 2) A tutorial in the form of a notebook on how to pre-process fMRI data on an HPC server.
The initial project aimed to create a full tutorial on how to process fMRI images, starting from raw, un-processed data, all the way to extracting derivatives from pre-processed images. The project however changed substantially as the weeks advanced. Below is a representation of the objectives before (in week 2) and objectives of the current week (week 4):
| Week 2 - Objectives | Week 4 - Objectives |
|---|---|
| - [DONE] Download Prevent-AD Date from the CONP using DataLad | - [DONE] Despair and lose hope a little bit because the whole week 3 is gone converting images for nothing |
| - [DONE] Transform the .mnc format files in BIDS using a custom bash script and the minc-toolkit within a Docker container | - [DONE] Regain hope and change objectives |
| - [PROBLEM] Perform BIDS validation using a Docker container of bids/validator | - [DONE] Download two BIDSed visits of a single subject of the Prevent-AD directly from the lab's HPC using scp |
| - BIDS correction, if needed (e.g. including a dataset_description.json) | - [DONE] Run BIDS validator on the data to ensure it is 'BIDS-valid' and do appropriate corrections if needed. |
| - Running fMRIPrep using a Docker container | - [DONE] Launch fMRIPrep a couple of time to better understand the options available and correct errors |
| - Use the fMRIPrep output to build a basic Nilearn pipeline to extract functional connectivity | - [DONE] Wait 14h per subject as the subject was processed locally |
| - Run fingerprinting analyses between the 2 visits of each of the 10 subjects | - [DONE] Develop the Nilearn pipeline to extract the functional connectivity from the data |
| - [In progress] Create deliverables (Two notebooks: One with the failed steps and the second with the steps of week 4) | |
| - [In progress] Create presentation for BHS | |
| - [In progress] Clean up the repository | |
| - Create nice brain image |
To explain more clearly what happened, at the end of week 3, after I was able to convert the .mnc images in .nii format and 'BIDS-like' format, the bids-validator would not work due to the fact that there is usually a .json file that accompagnies the images and provide information that is necessary for pre-processing. After asking around, unless I would know enough about .mnc format to extract the necessary information in the headers to create the .json files myself, there was nothing I could do to transform in bids. As such, I had to change the project to use data that was already in bids.
Use 2 visits of a single subject in the Prevent-AD cohort (anonymized) in bids format to illustrate pre-processing using fMRIPrep and to extract connectivity matrices using Nilearn.
Docker containers were used to pull the bids-validator and fMRIPrep. Nilearn was used on the pre-processed data to generate connectivity matrices
Create 2 Jupyter notebooks: 1) A tutorial on using mnc2bids (first project, not currently working) and 2) A tutorial on using fMRIPrep and Nilearn.