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

Area of Science:

• Biomedical Engineering
• Neuroscience
• Optical Imaging

Background:

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

Purpose of the Study:

• To investigate the efficacy of two- and three-layer tissue models in simulating in vivo dual-slope frequency-domain near-infrared spectroscopy (DS FD-NIRS) data.
• To assess the ability of simulated data to reproduce human subject data during brain activation.

Main Methods:

• Monte Carlo simulations were used to generate DS FD-NIRS data from two- and three-layer models with varying optical properties and thicknesses.
• In vivo DS FD-NIRS data were collected from human subjects over the occipital lobe during visual stimulation.
• 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 accounted for scalp/skull thickness.

Conclusions:

• A three-layer model offers a significant improvement over homogeneous models for analyzing DS FD-NIRS data.
• This approach enables more accurate cerebral hemodynamic measurements without requiring extensive datasets for tomographic reconstruction.