Installation

The package can be installed via pip as follows:

python -m pip install https://github.com/nw-duncan/scalp-distance/archive/main.tar.gz

Data organisation

The scripts assume that your data are in folders organised in a manner similar to the BIDS standard (https://bids.neuroimaging.io/).

Output files will be created in a new folder within the "derivatives" directory.

Usage

All functionality is accessed through the scalp_distance function.

Import this via the scalpdist package.

from scalpdist import scalpdist

It is assumed that processing is being run from the root directory within which the rawdata is located.

Calculate distance map only

When all that is required is the map of distances around the edge of the brain, the function requires only the subject ID:

scalpdist.scalp_distance('sub-01')

This will create the relevant NIFTI files in the derivatives folder.

Calculate distances at a specific MNI coordinate

The mean distance between scalp and brain around a coordinate or set of coordinates at which you intend to stimulate can be calculated. This distance is the average in a 1cm radius sphere around the coordinate.

Coordinates should be provided in MNI152 mm format.

Without a pre-calulated linear transform

The alignment between MNI and subject space will be calculated if no transformation is provided.

scalpdist.scalp_distance('sub-01',coords=[68,-18,32])

More than one set of coordinates can be entered as follows:

scalpdist.scalp_distance('sub-01',coords=([68,-18,32],[30,-100,6]))

With a pre-calculated linear transform

An existing linear transformation between MNI and subject space can be used. This should be compatible with FSL. The path to this file must be entered:

scalpdist.scalp_distance('sub-01',trans_file='derivatives/preprocess/sub-01/anat/mni_to_anat.mat',coords=[68,-18,32])

Dependencies

Required python packages

• Numpy
• Matplotlib
• Scipy
• Nibabel
• Scikit-Image
• Joblib
• Nipype

Other required tools

• FSL