Synchropioid analysis pipeline

A:

To reproduct the results from the analysis of the Synchropioid project, you will need to go through each steps of this tutorial.

0/ Downloading the tutorial

a/ To download the following tutorial:

• Go to the public repository https://github.com/hcherkaoui/analysis_synchropioid

• Click on the green 'Code' button

• Click on the 'Download ZIP' button

• After downloading the repository, go to your chosen downloading folder and extract the downloaded compressed folder with

  unzip analysis_synchropioid-main.zip
  

• To start the tutorial, go to its root folder:

  cd analysis_synchropioid-main
  

1/ Installation step

a/ This step installs the tools to perform the analysis.

Install key packages needed for the tutorial:

sudo apt install python3-pip liblapack-dev docker.io

b/ Install the needed Python tools with the requirements.txt file:

python3 -m pip install -r requirements.txt

/!If pip struggles to install all package, re-run multiple time the command to force pip to resolve the problems.

2/ DICOM files BIDSification step

Need steps 0/ and 1/

a/ Running generate_symbolic_dicom_dir.py and bidsify_synchropioid.py on 20 CPUs to make the Synchropioid dataset BIDS compliant:

cd 00_prepro_fmri/
./bidsify_synchropioid.py -v -i /biomaps/acquisitions/dicom/SIGNA_PET/ -o /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/ --cpu 20

3/ Nifti files fmri-prep preprocessing step

Need steps 0/, 1/ and 2/

a/ Launching fmriprep on a console:

sudo docker run -ti --rm -v /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/:/data:ro -v /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/:/derivatives:rw -v /biomaps/freesurfer/license.txt:/opt/freesurfer/license.txt:ro poldracklab/fmriprep:latest /data /derivatives/ participant --output-space MNI152Lin --fs-license-file /opt/freesurfer/license.txt --fs-no-reconall --random-seed 0 --dummy-scans 10 --nthreads 20

/!If you need to restart a failed preprocessing step, first you will need to delete the previous failed produced folder:

rm -rf /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/fmriprep/

Simply re-run the docker command after the deleting step.

b/ To match the BIDS format, rename the produced preprocessing folder

mv /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/fmriprep/ /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/derivatives/

4/ Quality check step

Need steps 0/, 1/, 2/ and 3/

a/ Open the each fmri-prep .html report (one per subject) under the folder /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/derivatives/ to check each preprocessing step.

b/ Run MRI-quality-control to add a simple quality check:

mriqc /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/derivatives/ . participant

5/ HRF estimation step

Need steps 0/, 1/, 2/, 3/ and 4/

a/ Running decomposition_multi_subjects and decomposition_multi_groups.py to estimation the HRFs for each subject:

cd 02_hrf_est/
python3 decomposition_multi_subjects.py --max-iter 100 --seed 0 --preproc-dir /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/derivatives/ --results-dir results_hrf_estimation_single --cpu 20 --verbose 1

Those commands will produced the folder results_hrf_estimation_single and results_hrf_estimation_group that store the produced results.

6/ Connectome estimation step

Need steps 0/, 1/, 2/, 3/ and 4/

a/ Running estimation_connectome:

cd 03_connectome/
python3 estimation_connectome.py --preproc-dir /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/derivatives/ --result-dir results_connectome --verbose 1

Those commands will produced the folder results_connectome that store the produced results.

8/ HRF plotting step

Need steps 0/, 1/, 2/, 3/, 4/, 5/ and 7/

a/ Produce the silouette curve w.r.t the temporal regularization value plot for the HRF estimation:

cd 05_plotting_hrf_est/
python3 plot_silhouette_score_per_params_single.py --plots-dir plots --results-dir ../03_hrf_est/results_hrf_estimation_single/ --verbose 1

b/ Produce vascular maps for each subjects for the best temporal regularization value for the HRF estimation:

python3 haemodynamic_maps_per_subjects.py --bids-root-dir /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/ --results-dir ../03_hrf_est/results_hrf_estimation_single/ --best-params-file decomp_params/best_single_subject_decomp_params.json --output-dir output_dir  --verbose 1

c/ Produce the haemodynamic region scatter plot and the mean haemodynamic evolution plot for the HRF estimation:

python3 plot_haemodynamic_delays_comparison_subjects.py --plots-dir plots --bids-root-dir /biomaps/synchropioid/dataset_synchropioid/fmri_nifti_dir/ --results-dir ../03_hrf_est/results_hrf_estimation_single/ --best-params-file decomp_params/best_single_subject_decomp_params.json --verbose 1

d/ Produce the T-test on the vascular maps plot for the HRF estimation:

python3 plot_t_test_per_run.py --vascular-maps-dir output_dir --plots-dir plots --verbose 1
eog plots/

e/ Produce the temgesic vs contro examples comparisons on the vascular maps plot for the HRF estimation:

python3 plot_temgesics_vs_control_group.py --vascular-maps-dir output_dir  --bids-root-dir /media/veracrypt1/synchropioid/fmri_nifti_dir/ --plots-dir plots --task-filter only_hb_rest --verbose 1

All the plots are gathered under the plots folder.

8/ Connectome plotting step

Need steps 0/, 1/, 2/, 3/, 4/, 6/ and 7/

a/ Produce the norm plot for the Connectome:

cd 04_plotting_connectome/
python3 plot_connectome_norm_evolution.py --connectome-dir ../04_connectome/results_connectome/ --plots-dir plots --verbose 1

b/ Produce the temgesic/control prediction plot for the Connectome:

python3 plot_decoding_connectomes.py --connectomes-dir ../04_connectome/results_connectome/ --plots-dir plots --seed 0 --cpu 20 --verbose 1

c/ Produce the learning curve for the temgesic/control prediction task plot for the Connectome:

python3 plot_learning_curve_connectomes.py --connectomes-dir ../04_connectome/results_connectome/ --plots-dir plots --seed 0 --cpu 20 --verbose 1

d/ Produce the T-test on the connectome matrices plot for the Connectome:

python3 plot_t_test_per_run.py --connectome-dir ../04_connectome/results_connectome/ --plots-dir plots --verbose 1
eog plots/

e/ Produce the ANOVA on the connectome matrices plot for the Connectome:

python3 plot_anova_connectomes.py --connectomes-dir ../03_connectome/results_connectome/ --plots-dir plots --seed 0 --cpu 20 --task-filter only_hb_rest --verbose 1

All the plots are gathered under the plots folder.

B:

To add a new subject to the Synchropioid dataset, simply edit the dicom_subjects_list.txt file by adding a newline with the corresponding DICOM directory name (e.g. add a new line S00...).