cpp-lln-lab · Remi-Gau · May 14, 2022 · May 14, 2022
diff --git a/README.md b/README.md
@@ -8,7 +8,7 @@
 [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
 [![codecov](https://codecov.io/gh/cpp-lln-lab/CPP_SPM/branch/master/graph/badge.svg?token=8IoRQtbFUV)](https://codecov.io/gh/cpp-lln-lab/CPP_SPM)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3554331.svg)](https://doi.org/10.5281/zenodo.3554331)
-[![All Contributors](https://img.shields.io/badge/all_contributors-10-orange.svg?style=flat-square)](#contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-10-orange.svg?style=flat-square)](https://github.com/cpp-lln-lab/CPP_SPM#contributors)
 
 # CPP SPM
 
@@ -17,6 +17,12 @@ This is a Matlab / Octave toolbox to perform MRI data analysis on a
 
 ## Installation
 
+```bash
+git clone \
+    --recurse-submodules \
+    https://github.com/cpp-lln-lab/CPP_SPM.git
+```
+
 Please see our
 [documentation](https://cpp_spm.readthedocs.io/en/latest/index.html) for more
 info.
@@ -25,17 +31,15 @@ info.
 
 ### Preprocessing
 
-If your data is fairly "typical" (for example whole brain coverage functonal
+If your data is fairly "typical" (for example whole brain coverage functional
 data with one associated anatomical scan for each subject), you might be better
 off running [fmriprep](https://fmriprep.org/en/stable/) on your data.
 
-If you have more exotic data that can't be handled well by fmriprep then CPP_SPM
+If you have more exotic data that cannot be handled well by fmriprep then CPP_SPM
 has some automated workflows to perform amongst other things:
 
 -   slice timing correction
 
--   fieldmaps processing and voxel displacement map creation (work in progress)
-
 -   spatial preprocessing:
 
     -   realignment OR realignm and unwarp
@@ -45,6 +49,8 @@ has some automated workflows to perform amongst other things:
 
 -   smoothing
 
+-   fieldmaps processing and voxel displacement map creation (work in progress)
+
 All preprocessed outputs are saved as BIDS derivatives with BIDS compliant
 filenames.
 

diff --git a/docs/source/general_information.md b/docs/source/general_information.md
@@ -0,0 +1 @@
+../../README.md
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -11,6 +11,7 @@ Welcome to CPP SPM documentation!
    :maxdepth: 1
    :caption: Content
 
+   general_information
    installation
    bids_app_api
    set_up
@@ -27,85 +28,6 @@ Welcome to CPP SPM documentation!
    docker
    links_and_references
 
-
-This is a Matlab / Octave toolbox to perform MRI data analysis on a
-`BIDS data set <https://bids.neuroimaging.io>`_ using SPM12.
-
-Features
-========
-
-Preprocessing
--------------
-
-If your data is fairly "typical" (for example whole brain coverage functonal
-data with one associated anatomical scan for each subject), you might be better
-off running `fmriprep <https://fmriprep.org/en/stable>`_ on your data.
-
-If you have more exotic data that can't be handled well by fmriprep then CPP_SPM
-has some automated workflows to perform amongst other things:
-
--   slice timing correction
-
--   fieldmaps processing and voxel displacement map creation (work in progress)
-
--   spatial preprocessing:
-
-    -   realignment OR realignm and unwarp
-    -   coregistration `func` to `anat`,
-    -   `anat` segmentation and skull stripping
-    -   (optional) normalization to SPM's MNI space
-
--   smoothing
-
-All preprocessed outputs are saved as BIDS derivatives with BIDS compliant
-filenames.
-
-Statistics
-----------
-
-The model specification are done via the
-`BIDS stats model <https://docs.google.com/document/d/1bq5eNDHTb6Nkx3WUiOBgKvLNnaa5OMcGtD0AZ9yms2M/edit?usp=sharing)>`_
-and can be used to perform:
-
--   whole GLM at the subject level
--   whole brain GLM at the group level à la SPM (meaning using a summary
-    statistics approach).
--   ROI based GLM (using marsbar)
--   model selection (with the MACS toolbox)
-
-Quality control
----------------
-
--   anatomical data
--   functional data (work in progress)
--   GLM auto-correlation check
-
-It can also prepare the data to run an MVPA analysis by running a GLM for each
-subject on non-normalized images and get one beta image for each condition to be
-used in the MVPA.
-
-
-Assumptions
-===========
-
-At the moment this pipeline makes some assumptions:
-
--   it assumes that the dummy scans have been removed from the BIDS data set and
-    it can jump straight into pre-processing,
-
-Citation
-========
-
-.. code-block:: bibtex
-
-    @software{CPP_SPM,
-        author = {Gau, Rémi and Barilari, Marco and Battal, Ceren and Rezk, Mohamed and Collignon, Olivier and Gurtubay, Ane and Falagiarda, Federica and MacLean, Michèle and Cerpelloni, Filippo},
-        license = {GPL-3.0},
-        title = {{CPP SPM}},
-        url = {https://github.com/cpp-lln-lab/CPP_SPM},
-        version = {1.1.3dev}
-    }
-
 Indices and tables
 ==================
 

diff --git a/src/workflows/preproc/bidsSegmentSkullStrip.m b/src/workflows/preproc/bidsSegmentSkullStrip.m
@@ -46,7 +46,6 @@ function bidsSegmentSkullStrip(opt)
   end
 
   prefix = get_spm_prefix_list;
-  opt.query.prefix = prefix.stc;
   opt.query.prefix = {prefix.bias_cor, 'c1', 'c2', 'c3', 'y_', 'iy_'};
   opt.query.suffix = opt.bidsFilterFile.t1w.suffix;
   bidsRename(opt);