Functional connectivity with fNIRS: from basic to integrated approaches

Rickson C. Mesquita, University of Campinas, Brazil 

Duration: 180 min

Capacity: 30

Level: Introductory, some coding experience

Analysis Methods

Requirements: Laptop; Matlab pre-installed

Synopsis: The brain is a complex system with integrated functional capabilities that can be revealed by neuroimaging modalities. In particular, functional connectivity has been extensively employed in fNIRS studies to better understand the connection between different regions of the brain both at rest and during functional activation. Despite its simple experimental protocol, it is important to understand the intrinsic properties of the fNIRS time- series in order to correctly interpret functional connectivity in fNIRS. In this mini-course we will analyze and discuss different approaches to perform functional connectivity with fNIRS data. By exploring hands-on activities of published data, we will start with basic approaches based on seed correlations and advance to more integrated approaches based on graph theory and complex systems analysis. 

Rationale: Functional connectivity studies have gained interest by fNIRS users in the past few years. In the majority of the cases, methods developed for fMRI data have been simply translated to fNIRS data without considering the intrinsic properties of the fNIRS time-series, which can lead to confusion and misinterpretation of functional connectivity maps acquired with fNIRS. The main goal of this mini-course is to provide users with a clear description of the advantages and pitfalls of fNIRS functional connectivity, and present simple solutions to overcome limitations of functional connectivity with fNIRS data with focused hands-on activities. The course is aimed at fNIRS users at all levels; basic MatLab skills are needed to complete the hands-on exercises.

Learning objectives: After participating in this course, participants will be able to: 

  • Understand the advantages and limitations of fNIRS functional connectivity protocols. 
  • Understand the intrinsic properties of the fNIRS signal and how to pre-process fNIRS data for functional connectivity. 
  • Perform seed-based functional connectivity analysis in fNIRS data. 
  • Compute network parameters from fNIRS functional connectivity data with graph-based approaches. 

Requirements: You are expected to bring a laptop with Matlab installed and have some rudimentary knowledge of coding.