app-C-PAC-brainlife.io

This is an app to run C-PAC: Configurable Pipeline for the Analysis of Connectomes via brainlife.io.

This app:

1. gets the latest C-PAC Singularity image
2. converts given anat/t1w and func/task (brainlife.io datatypes) to BIDS (the data structure required by C-PAC)
3. runs C-PAC, persisting the working directory
   • with a preconfigured pipeline given the optional preconfig configuration parameter
   • with the default pipeline otherwise

Authors

• The C-PAC Team

Contributors

• Soichi Hayashi
• Franco Pestilli
• Giulia Bertò
• Brad Caron

Funding Acknowledgements

brainlife.io is publicly funded and for the sustainability of the project it is helpful to acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your publications and code reusing this code.

See also C-PAC's funding acknowledgements

Citations

We kindly ask that you cite the following articles when publishing papers and code using this code.

1. Avesani, P., McPherson, B., Hayashi, S., et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). doi:10.1038/s41597-019-0073-y
2. Craddock C., Sikka S., Cheung B., et al. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC). Front. Neuroinform. Conference Abstract: Neuroinformatics 2013 (2013). doi:10.3389/conf.fninf.2013.09.00042

Running the App

On brainlife.io

You can run this App online at doi:10.25663/brainlife.app.399 via the "Execute" tab.

Running Locally (on your machine)

C-PAC is a BIDS app, so it requires a BIDS directory. This brainlife.io app expects that BIDS directory to be a directory called bids in the same directory as the main script.

1. git clone this repo.
2. Copy (cp -r /path/to/bids_dir ./bids) or link (cp -al /path/to/bids/dir ./bids) a BIDS directory to a directory called bids within the cloned directory.
3. Inside the cloned directory, create config.json with something like the following content with paths to your input files.

   {
     "preconfig": "fmriprep-options"
   }

   At this point, your directory structure should look like

   .
   ├── bids
   |   └── …
   ├── config.json
   ├── LICENSE
   ├── main
   └── README.md
   

4. Launch the App by executing main

   ./main

Simulating brainlife.io locally

1. git clone this repo.
2. If you don't have your own Brainlife input file, you can download sample datasets from brainlife.io, or you can use Brainlife CLI (again, in the cloned directory).

   npm install -g brainlife
   bl login
   bl dataset download 5e93e694aa1f998a35ac4d22
   bl dataset download 5e56f2220f7fa621dd3cd1b7

3. Inside the cloned directory, create config.json with something like the following content with paths to your input files. Brainlife generates this config file automatically based on your selections in the web GUI.

   {
     "t1": "./5e93e694aa1f998a35ac4d22/t1.nii.gz",
     "bold": "./5e56f2220f7fa621dd3cd1b7/bold.nii.gz",
     "preconfig": "fmriprep-options",
     "_inputs": [
       {
         "id": "t1",
         "datatype": "58c33bcee13a50849b25879a",
         "meta": {
           "subject": "10",
           "RepetitionTime": 3,
           "TaskName": "Viewing alternating blocks presenting either ready-to-eat edible objects or pictures of items unrelated to food",
           "MagneticFieldStrength": 3,
           "Manufacturer": "GE",
           "ManufacturersModelName": "DISCOVERY_MR750",
           "SoftwareVersions": "23_LX_MR_Software_release:DV22.0_V02_1122.a",
           "SeriesDescription": "fmri_2d_epi_condition_1",
           "ScanningSequence": "EP_GR",
           "SequenceVariant": "SS",
           "ScanOptions": "SAT_GEMS_EPI_GEMS_ACC_GEMS_FS",
           "ImageType": ["ORIGINAL", "PRIMARY", "EPI", "NONE"],
           "SliceThickness": 3,
           "SpacingBetweenSlices": 3,
           "EchoTime": 0.03,
           "FlipAngle": 90,
           "PhaseEncodingPolarityGE": "Flipped",
           "CoilString": "32Ch_Head",
           "PercentPhaseFOV": 100,
           "AcquisitionMatrixPE": 64,
           "ReconMatrixPE": 64,
           "EffectiveEchoSpacing": 0.000468,
           "TotalReadoutTime": 0.029484,
           "PixelBandwidth": 7812.5,
           "PhaseEncodingDirection": "j",
           "SliceTiming": [0,1.5,0.06,1.56,0.12,1.62,0.18,1.68,0.24,1.74,0.3,1.8,0.36,1.86,0.42,1.92,0.48,1.98,0.54,2.04,0.6,2.1,0.66,2.16,0.72,2.22,0.78,2.28,0.84,2.34,0.9,2.4,0.96,2.46,1.02,2.52,1.08,2.58,1.14,2.64,1.2,2.7,1.26,2.76,1.32,2.82,1.38,2.88,1.44,2.94],
           "InPlanePhaseEncodingDirectionDICOM": "COL",
           "task": "viewingfoodlowres",
           "dim":[3,160,192,128,1,1,1,1],
           "pixdim":[1,1,1.3333330154418945,1.3333330154418945,1,0,0,0]
         },
         "tags": [],
         "datatype_tags": [],
         "subdir": "5e93e694aa1f998a35ac4d22",
         "dataset_id": "5e93e694aa1f998a35ac4d22",
         "project": "5d64733db29ac960ca2e797f",
         "task_id": "5f861c7bc7adcbb6ae137fe1",
         "keys": [
           "t1"
         ]
       },
       {
         "id": "func",
         "datatype": "59b685a08e5d38b0b331ddc5",
         "meta": {
           "AcquisitionDateTime": "2017-04-13T11:48:43.116000",
           "EchoTime": 0.0352,
           "EffectiveEchoSpacing": 0.000580013,
           "FlipAngle": 56,
           "ImageType": ["ORIGINAL","PRIMARY","M","MB","ND","MOSAI"],
           "MagneticFieldStrength": 3,
           "Manufacturer": "Siemens",
           "ManufacturersModelName": "Prisma",
           "PhaseEncodingDirection": "j-",
           "RepetitionTime": 0.955,
           "SliceTiming": [0.875,0.795,0.715,0.635,0.5575,0.4775,0.3975,0.3175,0.2375,0.1575,0.08,0,0.875,0.795,0.715,0.635,0.5575,0.4775,0.3975,0.3175,0.2375,0.1575,0.08,0,0.875,0.795,0.715,0.635,0.5575,0.4775,0.3975,0.3175,0.2375,0.1575,0.08,0,0.875,0.795,0.715,0.635,0.5575,0.4775,0.3975,0.3175,0.2375,0.1575,0.08,0,0.875,0.795,0.715,0.635,0.5575,0.4775,0.3975,0.3175,0.2375,0.1575,0.08,0],
           "TaskName": "rest",
           "TotalReadoutTime": 0.0580013,
           "subject": "10",
           "task": "rest"
         },
         "tags": ["task-rest"],
         "datatype_tags": ["rest"],
         "subdir": "5e56f2220f7fa621dd3cd1b7",
         "dataset_id": "5e56f2220f7fa621dd3cd1b7",
         "project": "5d64733db29ac960ca2e797f",
         "task_id": "5f861c7bc7adcbb6ae137fe1",
         "keys": ["bold"]
       }
     ]
   }

   At this point, your directory structure should look like

   .
   ├── 5e56f2220f7fa621dd3cd1b7
   |   └── bold.nii.gz
   ├── 5e93e694aa1f998a35ac4d22
   |   └── t1.nii.gz
   ├── config.json
   ├── LICENSE
   ├── main
   └── README.md
   

   which is quite like what the directory structure would be on brainlife.io.
4. Launch the App by executing main

   ./main

Output

A C-PAC output directory and a C-PAC working directory. See Check Your Outputs and Output Settings for detailed information about C-PAC's outputs.

Dependencies

This App requires

• brainlife
• Singularity
• bl2bids
• jq

All of these dependencies are available on brainlife.io.

Issues and troubleshooting

Limitations

Pipelines

Currently this app only runs preconfigured pipelines. Please see the documentation for Pre-configured Pipelines to see the available pipelines.

C-PAC version

Currently this app only runs the latest version of C-PAC.

Bug reports and feature requests

If you run into an issue, please check existing bug reports.

If a bug report already exists for your issue, please add a 👍 and/or add any new information you have that might help us resolve the issue.

If a bug report does not exist yet, please open one using our bug report template, filling out as much detail as you can.

If you want to request a feature, we also have a template to create a feature request.

brainlife.io: MIT Copyright © 2020 brainlife.io The University of Texas at Austin and Indiana University

C-PAC: BSD 3-Clause "New" or "Revised" License © 2016–2020 Child Mind Institute, Inc. and C-PAC developers