This course will be taught in english

Conduttore: Ana Fló - dal 13 al 17 maggio 2024

lunedì 13-05-2024 ore 9:00 - 13:00; [AULA 4M]
martedì 14-05-2024 ore 9:00 - 13:00; [AULA 4M]
mercoledì 15-05-2024 ore 9:00 - 13:00; [AULA 4M]
giovedì 16-05-2024 ore 8:30 - 12:30; [AULA 4N]
venerdì 17-05-2024 ore 9:00 - 13:00; [AULA 4N]
L'iscrizione al corso sarà possibile dal 29 aprile alle 9 al 3 maggio alle 14 a questo link

Functional Near Infrared Spectroscopy (fNIRS) is a relatively young technique that is increasingly used in cognitive neuroscience due to its versatility. It can be used with different populations, is more tolerant to motion than other neuroimaging techniques, is portable, and is not invasive. 
While the field is evolving fast, fNIRS data analysis must still be standardised and requires some expertise. This course will introduce the fNIRS technique's principles and signal processing concepts necessary for properly planning an fNIRS experiment and analysing the data. In particular, the course will cover the preprocessing steps required to obtain the hemodynamic response functions from the recorded changes in light intensity with the removal of different noise sources. During the course, we will analyse a dataset by implementing different preprocessing strategies while discussing advantages, disadvantages, and theoretical considerations. All the material for the course will be available online.
Different open-source packages are available for analysing fNIRS data, almost all implemented in MATLAB. During the course, we will focus on Homer3, a MATLAB package openly available online, one of the community's most used packages. Nevertheless, the conceptual consideration and algorithms learned during the course can be easily extrapolated to other software.

Program:

1. An introduction to fNIRS, fNIRS files, and the MATLAB language and environment 
2. Perform a basic analysis using the Homer3 GUI 
3. Write your scripts using Homer3 processing functions 
4. Dealing with instrumental noise and motion artefacts
5. Dealing with physiological artefacts and running a Generalized Linear Model