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

TL;DR

This study demonstrates that three-layer tissue models better replicate in vivo dual-slope frequency-domain near-infrared spectroscopy data, improving the accuracy of cerebral hemodynamic measurements during brain activation.

Contribution

It introduces a three-layer tissue model for analyzing DS FD-NIRS data, enhancing the interpretation of cerebral hemodynamics over traditional homogeneous models.

Findings

Three-layer models qualitatively match in vivo data

Second layer represents cerebrospinal fluid with distinct optical properties

Three-layer approach improves measurement accuracy without large datasets

Abstract

Functional near-infrared spectroscopy (fNIRS) is impacted by signal contamination from superficial hemodynamics. It is important to develop methods that account for such contamination and provide accurate measurements of cerebral hemodynamics. This work aims to investigate whether simulated data with two-layer or three-layer tissue models are able to reproduce in vivo data collected with dual-slope (DS) frequency-domain (FD) near-infrared spectroscopy (NIRS) on human subjects during brain activation. We performed Monte Carlo simulations to generate DS FD-NIRS data from two- and three-layer media with a range of layer thicknesses and optical properties. We collected in vivo data with DS FD-NIRS (source-detector distances: 25, 37 mm; wavelengths: 690, 830 nm; modulation frequency: 140 MHz) over the occipital lobe of human subjects during visual stimulation. Simulated and in vivo data were analyzed with diffusion theory for a homogeneous medium and results were compared for each DS FD-NIRS data type. We found that the main qualitative features of in vivo data could be reproduced by simulated data from a three-layer medium, with a second layer (representing the cerebrospinal fluid in the subarachnoid space) that is less absorbing and less scattering than the other two layers, and with a top layer thickness that represents the combined scalp and skull thickness. A three-layer model is a viable improvement over a homogeneous model to analyze DS FD-NIRS data (or any other fNIRS data) to generate more accurate measurements of cerebral hemodynamic changes without a need for large data sets for tomographic reconstructions.