Addiction Connectome Preprocessed Initiative (ACPI)

The Addiction Connectome Preprocessed Initiative (ACPI) aims to facilitate the image processing and analysis of datasets generated by NIDA investigators, with a particular focus on the determination of robustness to preprocessing decisions. In this regard, we are providing 8 preprocessed versions of the resting state fMRI datasets provided by contributors:

  1. ANTs Registered Anatomical, Motion Correction without Scrubbing, Nuisance Correction without Global Signal Regression
  2. ANTs Registered Anatomical, Motion Correction without Scrubbing, Nuisance Correction with Global Signal Regression
  3. ANTs Registered Anatomical, Motion Correction with Scrubbing, Nuisance Correction without Global Signal Regression
  4. ANTs Registered Anatomical, Motion Correction with Scrubbing, Nuisance Correction with Global Signal Regression
  5. FNIRT Registered Anatomical, Motion Correction without Scrubbing, Nuisance Correction without Global Signal Regression
  6. FNIRT Registered Anatomical, Motion Correction without Scrubbing, Nuisance Correction with Global Signal Regression
  7. FNIRT Registered Anatomical, Motion Correction with Scrubbing, Nuisance Correction without Global Signal Regression
  8. FNIRT Registered Anatomical, Motion Correction with Scrubbing, Nuisance Correction with Global Signal Regression

The 8 pipelines differ with respect to the specific steps employed as follows:

  • Registration: ANTs vs. FNIRT
  • Additional Motion Correction: scrubbing vs. no scrubbing
  • Additional Nuisance signal Correction: global signal regression (GSR) vs. no GSR
  • In addition to 4D voxelwise data in nifti format, roi based average representations of the data are also available.See here for the masks. The following parcellations were used:

  • Anatomical Automatic Labelling
  • Craddock 200
  • Harvard-Oxford
  • Random parcels (3200)
  • Organizers

    Initiative Founders: Michael P. Milham, Cameron Craddock

    Project Coordinators: Michael P. Milham

    Pipeline Support: Configurable Pipeline for Analyzing Connectomes (CPAC)

    Preprocessing Strategy Design: Preprocessed Connectomes Project (PCP)

    Data Aggregation and Organization: David O’Connor

    Website: David O'Connor

    Database Support: Cameron Craddock, Daniel Clark, David O'Connor, Amazon Web Services

    Acknowledgements

    The ACPI is primarily supported by a grant supplement (R01 MH094639) provided to PI Milham by the National Institute on Drug Abuse (NIDA). Additional support provided by the Child Mind Institute and the Nathan Kline Institute.

    Table Of Contents

    An open neuroscience resource brought to you by: