Visual motion localizers

Small demo using visual motion localizer data to show how to set up an analysis with bidspm from scratch with datalad.

Using bidspm and datalad

Ideally better to use the datalad fMRI template we have set up, this shows a set by step approach.

Note

The bash script vismotion_demo.sh will run all the steps described here in one fell swoop.

You can run it by typing the following from within the bidspm/demos/vismotion

bash vismotion_demo.sh

Set up

Create a new datalad dataset with a YODA config

datalad create -c yoda visual_motion_localiser
cd visual_motion_localiser

Add the bidspm code as a sub-dataset, checkout the dev branch ands initializes all submodules.

datalad install \
    -d . \
    -s https://github.com/cpp-lln-lab/bidspm.git \
    -r \
    code/bidspm

In case you get some errors when installing the submodules you might have to initialize them manually, and update your dataset with that update

cd code/bidspm
git checkout main
git submodule update --init --recursive && git submodule update --recursive
cd ..
datalad save -m 'update bidspm submodules'

Now let’s get the raw data as a subdataset and put it in an inputs/raw folder.

The data from the CPP lab is openly available on GIN:

https://gin.g-node.org/cpp-lln-lab/Toronto_VisMotionLocalizer_MR_raw

Note that to install it you will need to have set up Datalad to play nice with GIN: see the datalad handbook

This will install the data:

datalad install -d . \
                -s git@gin.g-node.org:/cpp-lln-lab/Toronto_VisMotionLocalizer_MR_raw.git \
                --recursive \
                --jobs 12 \
                inputs/raw

After this your datalad dataset should look something like this:

├── code
│   └── bidspm
└── inputs
    └── raw
        ├── derivatives
        │   └── fmriprep
        ├── sub-con07
        ├── sub-con08
        └── sub-con15

To finish the setup you need to download the data:

cd inputs/raw
datalad get .

Running the analysis

Start matlab and run the step_1_preprocess.m and step_2_stats.m scripts.

In the end your whole analysis should look like this.

├── code
│   └── bidspm
│       ├── binder
│       ├── demos
│       │   ├── face_repetition
│       │   ├── lesion_detection
│       │   ├── MoAE
│       │   ├── openneuro
│       │   ├── tSNR
│       │   └── vismotion          # <--- your scripts are there
│       ├── docs
│       ├── lib
│       ├── manualTests
│       ├── notebooks
│       ├── src
│       ├── templates
│       └── tests
├── inputs
│   └── raw                        # <--- input data
│       ├── derivatives
│       │   └── fmriprep           # <--- fmriprep data
│       ├── sub-con07
│       │   └── ses-01
│       ├── sub-con08
│       │   └── ses-01
│       └── sub-con15
│           └── ses-01
└── outputs
    └── derivatives
        ├── bidspm-preproc        # <--- smoothed data
        │   ├── jobs
        │   ├── sub-con07
        │   ├── sub-con08
        │   └── sub-con15
        └── bidspm-stats          # <--- stats output
            ├── jobs
            ├── sub-con07
            ├── sub-con08
            └── sub-con15