[WIP] GLM pipeline: surface and template based

Overview

Inputs

Assumptions

Examples

Sample data

The data set consists of one original subject undergoing a tapping task with an optimal montage over the motor cortex. This single subject has been duplicated 9 times to produce 10 dummy subjects coregistered with the Colin27 template. All dummy subjects have the same optode coordinates.

Minimal example script

This script provides a minimal working example of the pipeline on the set of 10 dummy subjects. It starts by importing raw data files in brainstorm. It performs some default preprocessings up to cortical projection on the Colin27 template. Then a GLM with precoloring is run for all nodes of the surface to produce subject-level contrast maps. These maps finally enter a group-level GLM producing mixed effects contrast maps.

Refer to the comments in the script for details about every steps.

In what follows, the outline of the main steps is presented with output illustrations:

1. Importation

[sFiles, imported] = nst_ppl_surface_template_V1('import', options, nirs_fns, subject_names);

With the 'import' action specified as the 1st argument of nst_ppl_surface_template_V1, it imports all given nirs data files into brainstorm. The naming of subjects and condition folders is controlled by the function. To clearly separate the outputs of this function from other user-specific data, the suffix tag (__nspst_V1) is used. It is advised not to modify these folders. That way, one knows that the tagged data have been generated automatically by the pipeline function. They can be safely removed and regenerated by running the script again.

Here is the imported anatomy data. All subjects have the default anatomy that is the Colin27 template.

Here is the imported functional data. For every subject, the raw data is located in a folder called "origin".

2. Preprocessings

Add DB snapshot here

3. Subject-level GLM

Add DB snapshot + contrast maps

4. Group-level

Add DB snapshot + contrast t-maps