Functional dual-slope frequency-domain near-infrared spectroscopy data interpreted with two- and three-layer models

Area of Science:

• Biomedical optics
• Neuroimaging techniques
• Physiological monitoring

Background:

• Functional near-infrared spectroscopy (fNIRS) is susceptible to superficial hemodynamic signal contamination.
• Accurate measurement of cerebral hemodynamics requires methods that mitigate this contamination.

Purpose of the Study:

• To investigate the ability of simulated two-layer and three-layer tissue models to reproduce in vivo dual-slope (DS) frequency-domain near-infrared spectroscopy (FD-NIRS) data.
• To assess the utility of these models for improving cerebral hemodynamic measurements.

Main Methods:

• Monte Carlo simulations were used to generate DS FD-NIRS data from two- and three-layer tissue models with varying optical properties and thicknesses.
• In vivo DS FD-NIRS data were collected from human subjects during visual stimulation over the occipital lobe.
• Simulated and in vivo data were analyzed using diffusion theory for a homogeneous medium.

Main Results:

• Simulated data from a three-layer model successfully reproduced key qualitative features of the in vivo data.
• The three-layer model incorporated a cerebrospinal fluid layer with distinct optical properties and a top layer representing scalp and skull thickness.
• The three-layer model showed improved accuracy compared to a homogeneous model.

Conclusions:

• A three-layer tissue model offers a viable improvement over homogeneous models for analyzing DS FD-NIRS data.
• This approach can lead to more accurate cerebral hemodynamic measurements without requiring complex tomographic reconstructions.
• The findings support the use of multi-layer models for advanced fNIRS data analysis.