Resources for the paper: Moving Beyond Processing and Analysis-Related Variation in Neuroscience

The config folder contains C-PAC pipeline configuration files

• minimal: pipeline configuration files for minimal preprocessing

• fig4: pipeline configuration files for Fig. 4

• figs3: pipeline configuration file for Fig. S3

To run C-PAC Docker container, use the command below:

docker run \
-v <local data directory>:/bids_dataset \
-v <local output directory>:/outputs \
-v /tmp:/scratch \
fcpindi/c-pac:release-v1.8.1 /bids_dataset /outputs participant \
--pipeline_file <pipeline configuration file> \
--save_working_dir

Replace <local data directory> and <local output directory> to your local directories, and replace <pipeline configuration file> to your pipeline configuration file path such as /outputs/default_pipeline.yml. For more details, please check C-PAC user documentation: https://fcp-indi.github.io/

The figure folder contains code to plot figures in the paper

Before running:

1. Update .pipelineharmonizationrc with the applicable paths for your data.

2. Run source .pipelineharmonizationrc before running any scripts.

3. Run scripts (and IDEs like RStudio or Jupyter) from a terminal in which you've sourced .pipelineharmonizationrc

• Update extract_ROI.sh to create soft links for functional timeseries and run 3dROIstats

• Run run_ICC.sh

  • Results will be saved in the folder All_new_ICC_Schaefer200/600/1000_aggreg

• Organize folders as below

/root
└── figures
    └── ICC_Schaefer200
        ├── pipeline1_pipeline2.csv
        └── ...
    └── ICC_Schaefer600
    └── ICC_Schaefer1000
└── ROI
    └── ROI_Schaefer200
        ├── pipeline1
        └── pipeline2
        └── ...
    └── ROI_Schaefer600
    └── ROI_Schaefer1000

• Plot Fig. 1 using plot_fig1.py

• Plot Fig. 2 using plot_fig2.py

• Extract ROI

bash figure/3/extract_ROI.sh

• Save the data for calculating ICC and I2C2

python3.8 figure/3/run_ICC.py 

Outputs will be saved in a folder called Data_ICC_1000_All_pearson (60x19k matrix)

bash figure/3/run_ICC_I2C2.sh

Outputs will be saved in a folder called ICC_1000_All_pearson (19k matrix)

• Plot Fig. 3A using plot_fig3_time.py

• Plot Fig. 3B using plot_fig3_gsr.py

• Run extract_ROI.py

• Run corr.py and corr_harm.py to generate Pearson correlation

• Plot Fig. 4 using plot_fig4.ipynb

Notes: The post-processing folder has extra processing scripts for several runs

• Plot Fig. 5 using plot_fig5.py

• Plot ICC heatmaps in Fig. S1 using plot_figs1.ipynb

• Run ccs_postproc.sh and fmriprep_postproc.sh to generate intermediate files for comparison

• Run abcd.py/ccs.py/fmriprep.py to generate correlation

• Plot Fig. S2 using plot_figs2.ipynb

• Run corr_fd.py to calculate FD correlation

• Plot Fig. S3 using plot_figs3.ipynb

• Plot Fig. S4 using plot_figs4.py

• Plot Fig. S5 using plot_figs5.py

• Run corr_vol_ts.py to get voxel-wise timeseries correlation

Contributors: Xinhui Li, Lei Ai, Greg Kiar

Acknowledgements: Thank Jon Clucas for code review and Anibal Sólon for technical support.