fMRIflows

<img src="notebooks/templates/logo_fmriflows.gif" width="400px" align="right">

fMRIflows is a collection of fully autonomous uni- & multivariate fMRI processing pipelines. This comprises anatomical and functional preprocessing, estimation of singal confounds, as well as univariate and multivariate analysis on the subject and group level.

As is rather obvious, fMRIflows was greatly inspired by fmriprep and other open source projects and borrows strongly from their ideas and solutions. But while fmriprep can be described as a "glass" box software, fMRIflows is more like a shoebox. Simple to open, easy to understand what's inside and uncomplicated to replace and change components inside. This is all due to the fact that all the source code of fMRIflows is stored within the notebooks.

If you are using fMRIflows in your publication, please contact the author Michael Notter to get further information about how to cite this toolbox, as the publication is currently in preparation for submission.

Installation and Usage

Using Containers

The best way to use fMRIflows is to run it directly within the corresponding container (Docker or Singularity). The docker image for this can be downloaded from Docker Hub with the command docker pull miykael/fmriflows, and can be run with the command:

docker run -it --rm -p 9999:8888 -v /home/user/ds001:/data miykael/fmriflows

• The -it means that docker will be run in an interactive mode.
• The --rm means that the docker container (not the image!) will be removed from your system, once you close the container.
• The -p flag specifies which port should be used to access the jupyter notebook inside the docker container. The first 4 numbers can be changed (we recommend 9999), but the second half needs to be fixed to :8888.
• The -v flag tells docker which folder to mount within the docker container. The path /home/user/ds001 should therefore point to your BIDS conform dataset.

Once this command has been executed (note: the terminal will not go back to a new input prompt, but rather stays on this command) you need to copy paste/open the link http://127.0.0.1:9999/?token=fmriflows or http://localhost:9999/?token=fmriflows in your web browser to access the jupyter notebooks required to run fMRIflows.

Note: Should you by any chance encounter a web page that asks you for "Password or token", use the token fmriflows to login.

Using your native environment

It is also possible to run fMRIflows outside of a container. All the important code is stored within the notebooks. But for this, you need to make sure that all necessary neuroimaging and python dependencies are installed on your system. For a list of what needs to be done, see the generating file.

fMRIflows pipelines

fMRIflows contains many different pipelines. The following is a short summary and explanation of each individual notebook.

01_spec_preparation.ipynb

The notebook 01_spec_preparation.ipynb helps you to create the JSON files that contain the specifiation to run fMRIflows. This is the most direct way to specify the processing parameters for fMRIflows. Open the notebook, run it, change a few parameters as you want and you're good to go. Alternatively, you can also directly change the JSON specification file within your dataset folder (but you need to run 01_spec_preparation.ipynb for this to work at least once).

02_preproc_anat.ipynb

The notebook 02_preproc_anat.ipynb contains perform anatomical preprocessing. Parameters that can be specified in the fmriflows_spec_preproc.json file are the following:

• subject_list_anat: List of subject identifier to preprocess
• session_list_anat: List of session identifier to preprocess (set to [] if dataset doesn't contain session identifiers)
• T1w_id: T1-image identifier, usually T1w. For MP2RAGE acquisiton, this can for example be changed to T1w_UNI-DEN
• res_norm: Requested voxel resolution after normalization
• norm_accuracy: Precision accuracy of the normalization. This can be either precise or fast

The simplified workflow of the anatomical preprocessing looks as follows:

<img src="static/preproc_anat.png" width="400px" class="center">

After anatomical preprocessing the following output figures are generated:

Tissue Segmentation of T1w

This panel shows quality of the T1-weighted image segmentation, with brain tissues highlighted in green for Gray Matter (GM), in beige for White Matter (WM) and in blue for Cerebrospinal Fluid (CSF).

<img src="static/outputs/preproc_anat_segmentation.png" width="800px" class="center">

Brain Extraction of T1w

This panel shows the quality of the brain extraction. The brain highlighted in red is overlayed on the subject specific T1-weighted image in gray.

<img src="static/outputs/preproc_anat_brain.png" width="800px" class="center">

T1w to MNI registration

This panel shows the quality of the nonlinear template registration. The normalized brain highlighted in yellow is overlayed on the ICBM T1-weighted template image in gray. Regions in red and blue show negative and positive deformation discrepancy between the normalized subject image and the template.

<img src="static/outputs/preproc_anat_normalized.png" width="800px" class="center">

03_preproc_func.ipynb

The notebook 03_preproc_func.ipynb contains perform functional preprocessing. Parameters that can be specified in the fmriflows_spec_preproc.json file are the following:

• subject_list_func: List of subject identifier to preprocess
• session_list_func: List of session identifier to preprocess (set to [] if dataset doesn't contain session identifiers)
• task_list: List of task identifier to preprocess
• run_list: List of run identifiers (int) to preprocess (set to [] if dataset doesn't contain session identifiers)
• ref_timepoint: Reference timepoint for slicetime correction, in milliseconds
• res_func: Isometric resample resolution for after image coregistration
• filters_spatial: List of spatial filters to apply. Use LP for lowpass, HP for highpass and BP for bandpass spatial filtering. For example [[LP, 6.0], [HP, 2.0]] will run the analysis once with a spatial lowpass filter of FWHM=6mm and once with a spatial highpass filter of FWHM=2mm.
• filters_temporal: List of temporal filters to apply. First element will define lowpass, second highpass cutoff. Use null to not use given filter. For example [[5.0, 100.0], [null, 100.0]] will run the analysis once with a temporal lowpass filter of 5Hz and highpass filter of 100Hz, and once with a temporal filter of only a highpass filter of 100Hz.,
• n_compcor_confounds: Number of anatomical and functional CompCor componenets to compute. Defautl is 5.
• outlier_thresholds: Thresholds used to performe outlier detection on FD, DVARS, TV, GM, WM and CSF. Use None if parameter shouldn't be used for outlier detection.
• n_independent_components: Number of ICA components that should be estimaed. Default is 10.

The simplified workflow of the anatomical preprocessing looks as follows:

<img src="static/preproc_func.png" width="500px" class="center">

Additionally to the preprocessing, the following signal confounds are computed:

• Friston's 24-parameter model for motion parameters
• Framewise Displacement (FD) and DVARS
• Average signal in total volume (TV), in GM, in WM and in CSF
• Anatomical and temporal CompCor components
• Independent components in image before smoothing

After functional preprocessing the following output figures are generated:

Mean Image and CompCor Masks

This panel shows the mean functional image in gray, and the brainmask (red) that was used to compute DVARS and the temporal (green) and anatomical (blue) CompCor mask.

<img src="static/outputs/preproc_func_overlays.png" width="800px" class="center">

Carpet plot of the temporal filtered functional image

This panel shows the signal for (almost) every voxel (y-axis), over time in volumes (x-axis). The panel shows voxe