Time domain fNIRS

Alessandro Torricelli, Davide Contini, Lorenzo Spinelli, Politecnico di Milano – Dipartimento di Fisica, and Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milan, Italy

Duration: 180 min

Capacity: 15

Level: Introductory

Demo Tech.

Requirements: None

Synopsis: In the next future thanks to the recent technological development TD fNIRS systems will be more and more accessible to the fNIRS community. The mini course will provide a comprehensive introduction to TD fNIRS for both beginners and advanced fNIRS users. The mini-course is composed of a theory section where we will introduce the principles of TD fNIRS, and of a hands-on section in which we will demonstrate TD fNIRS features by means of a state-of-the art compact TD fNIRS setup.

Course structure:

  1. Principles of TD fNIRS
    • Basics of NIRS
    • The classical TD NIRS approach
    • The null source detector distance TD NIRS approach
  2. TD fNIRS modeling and data analysis
    • Forward model
    • Inverse model
    • Semi-empirical approaches
  3. TD fNIRS features
    • Quantification (homogeneous, perturbation, two-layer, tomography)
    • Reproducibility
    • Penetration depth
    • Depth selectivity
    • Spatial resolution
    • Contrast-to-noise ratio
  4. TD fNIRS instrumentation
    • Light sources
    • Detection techniques
    • Delivery and collection system
    • Effect of IRF
  5. TD fNIRS systems
    • Traditional TD fNIRS systems
    • State-of-the-art TD fNIRS systems
    • Co-registration with other modalities (DCS, US, EEG, …)
    • Next generation TD fNIRS systems
  6. Hands-on demonstration
    • Introduction
    • Basic measurements: IRF, DTOF
    • Measurements on homogeneous phantoms (e.g. absorption and scattering linearity)
    • Measurements on heterogeneous phantoms (e.g. phantom switchable)
    • Measurements in vivo (general): estimate of optical properties and DPF
    • Measurements in vivo (muscle): cuff occlusion (venous, arterial) of the arm
    • Measurements in vivo (brain): finger tapping

Learning objectives:

  • Basic TD fNIRS theory
  • Main components of a TD fNIRS system
  • Basic TD fNIRS data analysis
  • TD fNIRS advantages and limitations