The DCAN Labs Infant Processing Pipeline

This fMRI minimal preprocessing pipeline is based on Washington University's HCP Pipeline. Many changes were made to accomodate the differences in the developing brain of infants. Notably:

Skull Stripping: -- This pipeline utilizes ANTs SyN registration. -- This pipeline requires a T2w for skull stripping because the intensity of the CSF is better detected in T2w images.
Segmentation: The infant pipeline utilizes ANTs JointFusion. This can be perfomed using either the T1w image or the T2w image, depending on the quality. (Default is to use T1w.)
Surface Reconstruction: These steps in FreeSurfer have been modified:
- No hires.
- The aseg is generated from JLF.
- Adjust class means of tissue to fit T1w contrasts.

fMRI -> anatomical registration - no boundary based registration, use T2w to align.

Running PreFreeSurfer, FreeSurfer, and PostFreeSurfer stages will preprocess anatomical images. Following those with fMRIVolume and fMRISurface will preprocess functional images.

It is recommended to use the infant-abcd-bids-pipeline BIDS App (whose docker image is available on DockerHub) to run the pipeline as it simplifies the interface by providing defaults for most options.

The application can also run dcan-bold-preprocessing, executive summary, custom clean, and file-mapper. The stages are optional and can be controlled through that application's interface. Running the dcan-bold-preprocessing stage performs analysis and creates time series. The executive summary stage creates an HTML page (whose content will vary depending on the pipeline stages run) to show the primary outputs from the pipeline. Providing a Custom Clean json (via the option) will result in the pipeline running a custom-clean stage to remove many intermediate files generated during the processing. Providing a File Mapper json will cause pipeline to use file-mapper, in copy mode, to create BIDS derivatives.

If you still want to run these scripts without the application, the Examples/Scripts directory contains the basic individual building blocks of the pipeline (and some extra).

Please cite these papers for use of this pipeline:

Autio, Joonas A, Glasser, Matthew F, Ose, Takayuki, Donahue, Chad J, Bastiani, Matteo, Ohno, Masahiro, Kawabata, Yoshihiko, Urushibata, Yuta, Murata, Katsutoshi, Nishigori, Kantaro, Yamaguchi, Masataka, Hori, Yuki, Yoshida, Atsushi, Go, Yasuhiro, Coalson, Timothy S, Jbabdi, Saad, Sotiropoulos, Stamatios N, Smith, Stephen, Van Essen, David C, Hayashi, Takuya. (2019). Towards HCP-Style Macaque Connectomes: 24-Channel 3T Multi-Array Coil, MRI Sequences and Preprocessing. BioRxiv, 602979. https://doi.org/10.1101/602979

Donahue, Chad J, Sotiropoulos, Stamatios N, Jbabdi, Saad, Hernandez-Fernandez, Moises, Behrens, Timothy E, Dyrby, Tim B, Coalson, Timothy, Kennedy, Henry, Knoblauch, Kenneth, Van Essen, David C, Glasser, Matthew F. (2016). Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey. The Journal of Neuroscience, 36(25), 6758 LP – 6770. https://doi.org/10.1523/JNEUROSCI.0493-16.2016

Glasser, Matthew F, Sotiropoulos, Stamatios N, Wilson, J Anthony, Coalson, Timothy S, Fischl, Bruce, Andersson, Jesper L, Xu, Junqian, Jbabdi, Saad, Webster, Matthew, Polimeni, Jonathan R, Van Essen, David C, Jenkinson, Mark. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127