Boilerplate

[araikes]

Hi @mattcieslak I'm getting toward putting together a manuscript with outcomes based on QSIPrep processing. Just curious about the state of the boiler plate. Right now it is light on details after FSL Fast on the anatomical side and devoid of processing steps for the DWI (see below for current boilerplate in 0.11.0 with my data). Is there a more expansive description of the details somewhere that I'm not finding?

Preprocessing was performed using QSIPrep 0.11.0, which is based on Nipype 1.5.0 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502).

Anatomical data preprocessing
A total of 2 T1-weighted (T1w) images were found within the input BIDS dataset. All of them were corrected for intensity non-uniformity (INU) using N4BiasFieldCorrection (Tustison et al. 2010, ANTs 2.3.1). A T1w-reference map was computed after registration of 2 T1w images (after INU-correction) using mri_robust_template (FreeSurfer 6.0.1, Reuter, Rosas, and Fischl 2010). The T1w-reference was then skull-stripped using antsBrainExtraction.sh (ANTs 2.3.1), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c (Fonov et al. 2009, RRID:SCR_008796) was performed through nonlinear registration with antsRegistration (ANTs 2.3.1, RRID:SCR_004757, Avants et al. 2008), using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using FAST (FSL 6.0.3:b862cdd5, RRID:SCR_002823, Zhang, Brady, and Smith 2001).

Diffusion data preprocessing
Several confounding time-series were calculated based on the preprocessed DWI: framewise displacement (FD) using the implementation in Nipype (following the definitions by Power et al. 2014). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. Slicewise cross correlation was also calculated. The DWI time-series were resampled to ACPC, generating a preprocessed DWI run in ACPC space.

Many internal operations of QSIPrep use Nilearn 0.6.2 (Abraham et al. 2014, RRID:SCR_001362) and Dipy (Garyfallidis et al. 2014). For more details of the pipeline, see the section corresponding to workflows in QSIPrep’s documentation.

[mattcieslak]

It looks like the boilerplate is pretty empty. Can you tell from the rest of the report whether distortion correction, denoising, unringing or bias correction were run?

[araikes]

Everything ran as expected. I've check across a couple of datasets with both 0.11.0 and 0.8.0 and they're all the same in terms of how much information they give.

[araikes]

Hi @mattcieslak,

I saw you release 0.12.0. Will this potentially fix the issues with the boilerplate?

[mattcieslak]

Hey, sorry for the delay on this. I hope to fill in a bunch of the boilerplate and correct some of the text in the visual reports within the next few weeks.

[araikes]

Sounds good. Grateful for this tool.