NIRSTORM mini-course
A Brainstorm plugin dedicated to fNIRS statistical analysis, 3D reconstructions and optimal probe design
Wednesday, January 29, 2020
Loyola Jesuit Hall and Conference Centre, 7141 Sherbrooke Street West (Loyola Campus)
NIRSTORM is a plugin dedicated for fNIRS data analysis, built upon Brainstorm, an internationally recognized software for EEG/MEG processing, featuring advanced databasing, visualization, signal processing, source localization and statistical analysis methods. The purpose of this mini-course is to introduce NIRSTORM as a user-friendly and fully complete environment dedicated to fNIRS statistical analysis. The first section will be dedicated to beginners, introducing NIRSTORM database, data importation and classical channel-space fNIRS processing (band pass filtering, Modified Beer-Lambert Law, motion correction and window averaging). Most recent updates will then be presented: General Linear Model based statistical analyses (auto- regressive/precoloring model, mixed-effect group level analysis) to provide statistics of the hemodynamic response either in the channel space or along the cortical surface after 3D reconstruction. Finally, we will present the most advanced NIRSTORM features, such as the integration of MCXLab software [Fang and Boas Opt. Express 2009] to estimate light sensitivity profiles within anatomical head models, our method allowing personalized optimal montage design targeting a predefined brain region [Machado et al JNS-Methods 2018] and advanced 3D reconstructions using Maximum Entropy on the Mean.
Program
The course will consist of hands-on sessions; fNIRS data sets dedicated for the training will be made available to the participants.
1 – 1:30 p.m.
Introductory lecture: NIRS data acquisition, Montage design and 3D reconstructions 101
Christophe Grova, PERFORM Centre, Concordia University
1:30 – 3 p.m.
Introduction of NIRSTORM and NIRS data processing
- Database organization in Brainstorm and fNIRS data importation
- Standard fNIRS preprocessing and quality check (co-registration, filtering, Modified Beer Lambert law, motion correction, block averaging)
- Statistical analysis of the hemodynamic response: General Linear Model at the single subject level and at the group level, at the level of the sensors and after 3D reconstruction along the cortical surface
3 p.m. – 3:30 p.m.
Coffee break
3:30 – 5 p.m.
Advanced NIRS data processing
- fNIRS forward model through MCXLab, using head models derived either from a standard template MRI (Colin 27) or a subject-specific MRI.
- Personalized optimal montage design targeting a predefined brain region. This method consists in maximizing light sensitivity to the target region, while ensuring spatial overlap between sensors to allow local 3D reconstruction [Machado et al JNS-Meth. 2018, Pellegrino et al Front. Neurosc. 2016].
- Advanced 3D reconstruction methods, inspired from methods developed for EEG/MEG source imaging, notably within the Maximum Entropy on the Mean framework.
5 – 5:30 p.m.
Q&A
Learning objectives
At the end of the session, participants will be able to use efficiently the GUI of Brainstorm and NIRSTORM to perform standard fNIRS processing, statistical analysis through GLM approaches and more advanced features such as tomographic reconstructions and optimal montage design.
Requirements
You are expected to bring a laptop with Matlab and NIRSTORM installed.
Detailed instructions and training datasets will be provided before the course.
Acknowledgments
Introduction lecture
- Christophe Grova, Physics Department and PERFORM Centre, Concordia University, Montreal, Canada
Hands-on session
- Thomas Vincent, EPIC center, Montreal Heart Institute, Montreal, Canada
- Zhengchen Cai, Physics Department and PERFORM Centre, Concordia University, Montreal, Canada
- Edouard Delaire, Physics Department and PERFORM Centre, Concordia University, Montreal, Canada
- Amanda Spilkin, Physics Department and PERFORM Centre, Concordia University, Montreal, Canada
