TECHNIQUES FOR MEASURING BLOOD OXYGENATION

Neural firing is metabolically demanding and results first in a local increase in deoxygenated hemoglobin (deOxy-Hb), followed immediately by a compensatory sustained increase in oxygenated hemoglobin (Oxy-Hb) and subsequent decrease in deOxy-Hb (see Figure 1). The specific relationship of the circulatory system to local changes in neural activity is referred to as neurovascular coupling.



When a specific pool of neurons is active, the hemodynamic response is believed to be localized within 1–3mmof this neural activity (e.g., Shmuel et al. 2007, using 7T in human occipital cortex, V1) and likely represents the local field potentials rather than spiking activity of clusters of neurons (Goense&Logothetis 2008).However, the hemodynamic response unfolds slowly compared with the rapid change in neural activity. Even after a brief burst of stimulus-evoked neural activity (<1 s), the adult hemodynamic response peaks approximately 6 s later and the concentrations of Oxy-Hb and deOxy-Hb do not return to baseline until more than 10 s after the stimulus was initially presented (Malonek & Grinvald 1996) (see Figure 1). These spatial-temporal aspects of the hemodynamic response greatly constrain the designs and interpretations that can be drawn from neuroimaging methods that rely on neurovascular coupling.

FMRI employs the presentation of radio wave pulses in a staticmagnetic field to record what is called the blood-oxygen-level-dependent (BOLD) response that varies with fluctuations in local concentrations of deOxy-Hb. By contrast, fNIRS simultaneously records changes in bothOxy-Hb and deOxy-Hb by employing two wavelengths of near-infrared light (e.g., 690 nm and 830 nm), which span the crossover point in their respective absorbance spectra.
While the spatial resolution of fMRI varieswith the strength of the staticmagnetic field (B0; e.g., 1.5T, 3T, or 7T) and the specific scanning parameters, the typical fMRI voxel size of 3mm3 is well matched to the resolution of the hemodynamic response itself. In contrast, current fNIRS systems have a much coarser spatial resolution. As shown in Figure 2, near-infrared light is delivered to the surface of the scalp via optical fibers (emitters). Surrounding each emitter are one or more optical fibers (detectors) that collect a small fraction of the photons that return to the surface of the scalp after passing through various layers of neural tissue between the emitter and the detector. Each pair of optical fibers that link an emitter and a detector is referred to as a fNIRS channel and samples the underlying tissue from which the hemodynamic response is being recorded. Photons leaving the emitter and reaching the detector are primarily absorbed in the surface layers above the cortex, including the scalp, skull, cerebral-spinal fluid, and surface vasculature (see Figure 2). The remaining photons travel along a banana-shaped trajectory that dips into the underlying cortex. The distance between the emitter and detector determines the lowest point of the banana-shaped trajectory; the greater the separation between the two, the deeper the trajectory through the cortex. However, greater separation also leads to greater attenuation of the signal. Current systems typically employ emitter-detector separations of 2–3 cm, which optimize cortical sampling and signal strength.However, photons must necessarily enter and exit the brain through the scalp and therefore are substantially modulated by absorption in the superficial layers of tissue between the surface of the cortex and the scalp. This introduces a substantial source of noise into what is a rather weak signal. Thus, signal averaging, as in the ERP, must be used to factor out noncortical noise (under the assumption that background systemic vascular effects are uncorrelated with the presentation of a stimulus).

One final consideration regarding spatial resolution of fNIRS is skull thickness. The thickness of the skull is directly related to the amount of cortex that is being recorded from in a given fNIRS channel. This is important not only because the skull is quite thin in neonates (6mmon average) and becomes thicker with age (10 mm by age 7) but also because skull thickness varies across regions of the head within a given age (Beauchamp et al. 2011), creating a confound when examining absolute changes in cortical activation across ages or across brain regions (e.g., hemispheres). This underlying anatomy should be taken into consideration for studies that compare neural activity to a given stimulus across fNIRS channels without normalizing these activations to a second stimulus. In the future, anatomical data that compensate for this differential path length from fNIRS could help to prevent the false attribution of hemispheric and regional differences (unless only relative changes in activation comprise the dependent measure). In addition to variability in skull thickness, it is important to determine the distance from the tips of the optical fibers on the scalp to the depth of the cortical area that is being targeted in a given recording session.

Finally, the temporal resolution of fMRI is typically 0.5 Hz (i.e., a whole-brain sample every 2 s). This slow sample rate has proven to be sufficient for most applications because the underlying hemodynamic response is an order of magnitude slower. In contrast, fNIRS is typically recorded at 10 Hz and higher sampling rates are possible because detection of the optical response is not limited by the interaction between slice selection and gradient encoding in fMRI. Thus, fNIRS has much better temporal resolution than fMRI and in principle could provide a more accurate measure of the shape and timing of the hemodynamic response. In practice, however, that potential has not yet been realized, in part because of noise from noncortical, surface-vascular responses (which does not affect fMRI) and because infants cannot provide a sufficient number of stimulus blocks (or events) to average out the noise. Moreover, phased-array head coils have improved the sampling rates of fMRI (Keil et al. 2013) so that the intrinsic superiority of fNIRS in the temporal domain is not likely to be a significant advantage in the future.