Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes

TLDR

Functional near‑infrared spectroscopy measures cerebral activity, yet scalp‑hemodynamics often contaminate signals, and while long‑channel methods over‑estimate artifacts and short‑channel approaches require many probes, both have drawbacks. The authors propose a new method that combines these techniques to accurately estimate cerebral activity without the individual disadvantages, and demonstrate its feasibility with simultaneous fNIRS and fMRI during a motor task. The method estimates a global scalp‑hemodynamic component from a small number of short‑channel measurements and removes it from long‑channel data using a general linear model, with principal component analysis showing that as few as four short channels suffice to capture the global component. The GLM produced cerebral activity maps comparable to fMRI despite strong task‑related scalp hemodynamics, indicating that combining four short channels with a GLM yields robust, low‑cost cerebral activity estimation.

Abstract

Functional near-infrared spectroscopy (fNIRS) is used to measure cerebral activity because it is simple and portable. However, scalp-hemodynamics often contaminates fNIRS signals, leading to detection of cortical activity in regions that are actually inactive. Methods for removing these artifacts using standard source–detector distance channels (Long-channel) tend to over-estimate the artifacts, while methods using additional short source–detector distance channels (Short-channel) require numerous probes to cover broad cortical areas, which leads to a high cost and prolonged experimental time. Here, we propose a new method that effectively combines the existing techniques, preserving the accuracy of estimating cerebral activity and avoiding the disadvantages inherent when applying the techniques individually. Our new method accomplishes this by estimating a global scalp-hemodynamic component from a small number of Short-channels, and removing its influence from the Long-channels using a general linear model (GLM). To demonstrate the feasibility of this method, we collected fNIRS and functional magnetic resonance imaging (fMRI) measurements during a motor task. First, we measured changes in oxygenated hemoglobin concentration (∆ Oxy-Hb) from 18 Short-channels placed over motor-related areas, and confirmed that the majority of scalp-hemodynamics was globally consistent and could be estimated from as few as four Short-channels using principal component analysis. We then measured ∆ Oxy-Hb from 4 Short- and 43 Long-channels. The GLM identified cerebral activity comparable to that measured separately by fMRI, even when scalp-hemodynamics exhibited substantial task-related modulation. These results suggest that combining measurements from four Short-channels with a GLM provides robust estimation of cerebral activity at a low cost.