No abstract

No abstract

No abstract

No abstract

Understanding the spatial and depth sensitivity of non-invasive near-infrared spectroscopy (NIRS) measurements to brain tissue–i.e., near-infrared neuromonitoring (NIN) – is essential for designing experiments as well as interpreting research findings. However, a thorough characterization of such sensitivity in realistic head models has remained unavailable. In this study, we conducted 3,555 Monte Carlo (MC) simulations to densely cover the scalp of a well-characterized, adult male template brain (Colin27). We sought to evaluate: (i) the spatial sensitivity profile of NIRS to brain tissue as a function of source-detector separation, (ii) the NIRS sensitivity to brain tissue as a function of depth in this realistic and complex head model, and (iii) the effect of NIRS instrument sensitivity on detecting brain activation. We found that increasing the source-detector (SD) separation from 20 to 65 mm provides monotonic increases in sensitivity to brain tissue. For every 10 mm increase in SD separation (up to ∼45 mm), sensitivity to gray matter increased an additional 4%. Our analyses also demonstrate that sensitivity in depth (S) decreases exponentially, with a “rule-of-thumb” formula S = 0.75*0.85depth. Thus, while the depth sensitivity of NIRS is not strictly limited, NIN signals in adult humans are strongly biased towards the outermost 10–15 mm of intracranial space. These general results, along with the detailed quantitation of sensitivity estimates around the head, can provide detailed guidance for interpreting the likely sources of NIRS signals, as well as help NIRS investigators design and plan better NIRS experiments, head probes and instruments.

No abstract

The sensitivity of near-infrared spectroscopy (NIRS) to evoked brain activity is reduced by physiological interference in at least two locations: 1. the superficial scalp and skull layers, and 2. in brain tissue itself. These interferences are generally termed as "global interferences" or "systemic interferences," and arise from cardiac activity, respiration, and other homeostatic processes. We present a novel method for global interference reduction and real-time recovery of evoked brain activity, based on the combination of a multiseparation probe configuration and adaptive filtering. Monte Carlo simulations demonstrate that this method can be effective in reducing the global interference and recovering otherwise obscured evoked brain activity. We also demonstrate that the physiological interference in the superficial layers is the major component of global interference. Thus, a measurement of superficial layer hemodynamics (e.g., using a short source-detector separation) makes a good reference in adaptive interference cancellation. The adaptive-filtering-based algorithm is shown to be resistant to errors in source-detector position information as well as to errors in the differential pathlength factor (DPF). The technique can be performed in real time, an important feature required for applications such as brain activity localization, biofeedback, and potential neuroprosthetic devices.

In previous work we introduced a novel method for reducing global interference, based on adaptive filtering, to improve the contrast to noise ratio (CNR) of evoked hemodynamic responses measured non-invasively with near infrared spectroscopy (NIRS). Here, we address the issue of how to generally apply the proposed adaptive filtering method. A total of 156 evoked visual response measurements, collected from 15 individuals, were analyzed. The similarity (correlation) between measurements with far and near source-detector separations collected during the rest period before visual stimulation was used as indicator of global interference dominance. A detailed analysis of CNR improvement in oxy-hemoglobin (O2Hb) and deoxy-hemoglobin (HHb), as a function of the rest period correlation coefficient, is presented. Results show that for O2Hb measurements, 66% exhibited substantial global interference. For this dataset, dominated by global interference, 71% of the measurements revealed CNR improvements after adaptive filtering, with a mean CNR improvement of 60%. No CNR improvement was observed for HHb. This study corroborates our previous finding that adaptive filtering provides an effective method to increase CNR when there is strong global interference, and also provides a practical way for determining when and where to apply this technique.