Gary E. Strangman

A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation

Near-infrared spectroscopy (NIRS) has been used to noninvasively monitor adult human brain function in a wide variety of tasks. While rough spatial correspondences with maps generated from functional magnetic resonance imaging (fMRI) have been found in such experiments, the amplitude correspondences between the two recording modalities have not been fully characterized. To do so, we simultaneously acquired NIRS and blood-oxygenation level-dependent (BOLD) fMRI data and compared Delta(1/BOLD) (approximately R(2)(*)) to changes in oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations derived from the NIRS data from subjects performing a simple motor task. We expected the correlation with deoxyhemoglobin to be strongest, due to the causal relation between changes in deoxyhemoglobin concentrations and BOLD signal. Instead we found highly variable correlations, suggesting the need to account for individual subject differences in our NIRS calculations. We argue that the variability resulted from systematic errors associated with each of the signals, including: (1) partial volume errors due to focal concentration changes, (2) wavelength dependence of this partial volume effect, (3) tissue model errors, and (4) possible spatial incongruence between oxy- and deoxyhemoglobin concentration changes. After such effects were accounted for, strong correlations were found between fMRI changes and all optical measures, with oxyhemoglobin providing the strongest correlation. Importantly, this finding held even when including scalp, skull, and inactive brain tissue in the average BOLD signal. This may reflect, at least in part, the superior contrast-to-noise ratio for oxyhemoglobin relative to deoxyhemoglobin (from optical measurements), rather than physiology related to BOLD signal interpretation.

Non-invasive neuroimaging using near-infrared light

This article reviews diffuse optical brain imaging, a technique that employs near-infrared light to non-invasively probe the brain for changes in parameters relating to brain function. We describe the general methodology, including types of measurements and instrumentation (including the tradeoffs inherent in the various instrument components), and the basic theory required to interpret the recorded data. A brief review of diffuse optical applications is included, with an emphasis on research that has been done with psychiatric populations. Finally, we discuss some practical issues and limitations that are relevant when conducting diffuse optical experiments. We find that, while diffuse optics can provide substantial advantages to the psychiatric researcher relative to the alternative brain imaging methods, the method remains substantially underutilized in this field.

Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters

Near-infrared spectroscopy (NIRS) can be used to noninvasively measure changes in the concentrations of oxy- and deoxyhemoglobin in tissue. We have previously shown that while global changes can be reliably measured, focal changes can produce erroneous estimates of concentration changes (NeuroImage 13 (2001), 76). Here, we describe four separate sources for systematic error in the calculation of focal hemoglobin changes from NIRS data and use experimental methods and Monte Carlo simulations to examine the importance and mitigation methods of each. The sources of error are: (1). the absolute magnitudes and relative differences in pathlength factors as a function of wavelength, (2). the location and spatial extent of the absorption change with respect to the optical probe, (3). possible differences in the spatial distribution of hemoglobin species, and (4). the potential for simultaneous monitoring of multiple regions of activation. We found wavelength selection and optode placement to be important variables in minimizing such errors, and our findings indicate that appropriate experimental procedures could reduce each of these errors to a small fraction (<10%) of the observed concentration changes.

The Accuracy of Near Infrared Spectroscopy and Imaging during Focal Changes in Cerebral Hemodynamics

Near infrared spectroscopy (NIRS) can detect changes in the concentrations of oxy-hemoglobin ([HbO]) and deoxy-hemoglobin ([Hb]) in tissue based upon differential absorption at multiple wavelengths. The common analysis of NIRS data uses the modified Beer-Lambert law, which is an empirical formulation that assumes global concentration changes. We used simulations to examine the errors that result when this analysis is applied to focal hemodynamic changes, and we performed simultaneous NIRS measurements during a motor task in adult humans and a neonate to evaluate the dependence of the measured changes on detector-probe geometry. For both simulations and in vivo measurements, the wide range of NIRS results was compared to an imaging analysis, diffuse optical tomography (DOT). The results demonstrate that relative changes in [HbO] and [Hb] cannot, in general, be quantified with NIRS. In contrast to that method, DOT analysis was shown to accurately quantify simulated changes in chromophore concentrations. These results and the general principles suggest that DOT can accurately measure changes in [Hb] and [HbO], but NIRS cannot accurately determine even relative focal changes in these chromophore concentrations. For the standard NIRS analysis to become more accurate for focal changes, it must account for the position of the focal change relative to the source and detector as well as the wavelength dependent optical properties of the medium.

Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy

Several current brain imaging techniques rest on the assumption of a tight coupling between neural activity and hemodynamic response. The nature of this neurovascular coupling, however, is not completely understood. There is some evidence for a decoupling of these processes at the onset of neural activity, which manifests itself as a momentary increase in the relative concentration of deoxyhemoglobin (HbR). The existence of this early component of the hemodynamic response function, however, is controversial, as it is inconsistently found. Near infrared spectroscopy (NIRS) allows quantification of levels of oxyhemoglobin (HbO(2)) and HbR during task performance in humans. We acquired NIRS data during performance of simple motor and visual tasks, using rapid-presentation event-related paradigms. Our results demonstrate that rapid, event-related NIRS can provide robust estimates of the hemodynamic response without artifacts due to low-frequency signal components, unlike data from blocked designs. In both the motor and visual data the onset of the increase in HbO(2) occurs before HbR decreases, and there is a poststimulus undershoot. Our results also show that total blood volume (HbT) drops before HbO(2) and undershoots baseline, raising a new issue for neurovascular models. We did not find early deoxygenation in the motor data using physiologically plausible values for the differential pathlength factor, but did find one in the visual data. We suggest that this difference, which is consistent with functional magnetic resonance imaging (fMRI) data, may be attributable to different capillary transit times in these cortices.

Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy?

We have measured the changes in oxy-haemoglobin and deoxy-haemoglobin in the adult human brain during a brief finger tapping exercise using near-infrared spectroscopy (NIRS). The cerebral metabolic rate of oxygen (CMRO2) can be estimated from these NIRS data provided certain model assumptions. The change in CMRO2 is related to changes in the total haemoglobin concentration, deoxy-haemoglobin concentration and blood flow. As NIRS does not provide a measure of dynamic changes in blood flow during brain activation, we relied on a Windkessel model that relates dynamic blood volume and flow changes, which has been used previously for estimating CMRO2 from functional magnetic resonance imaging (fMRI) data. Because of the partial volume effect we are unable to quantify the absolute changes in the local brain haemoglobin concentrations with NIRS and thus are unable to obtain an estimate of the absolute CMRO2 change. An absolute estimate is also confounded by uncertainty in the flow-volume relationship. However, the ratio of the flow change to the CMRO2 change is relatively insensitive to these uncertainties. For the linger tapping task, we estimate a most probable flow-consumption ratio ranging from 1.5 to 3 in agreement with previous findings presented in the literature, although we cannot exclude the possibility that there is no CMRO2 change. The large range in the ratio arises from the large number of model parameters that must be estimated from the data. A more precise estimate of the flow-consumption ratio will require better estimates of the model parameters or flow information, as can be provided by combining NIRS with fMRI.

Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work?

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 (O(2)Hb) and deoxy-hemoglobin (HHb), as a function of the rest period correlation coefficient, is presented. Results show that for O(2)Hb 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.

Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study

Following previous Monte Carlo simulations, we describe in detail an example of detecting evoked visual hemodynamic responses in a human subject as a preliminary demonstration of the novel global interference cancellation technology. The raw time series of oxyhemoglobin (O(2)Hb) and deoxyhemoglobin (HHb) changes, their block averaged results before and after adaptive filtering, together with the power spectral density analysis are presented. Simultaneous respiration and EKG recordings suggested that spontaneous low-frequency oscillation and cardiac activity were the major sources of global interference in our test. When global interference dominates (e.g., for O(2)Hb in our data, judged by power spectral density analysis), adaptive filtering effectively reduced this interference, doubling the contrast-to-noise ratio (CNR) for evoked visual response detection. When global interference is not obvious (e.g., in our HHb data), adaptive filtering provided no CNR improvement. The results also showed that the hemodynamic changes in the superficial layers and the estimated total global interference in the target measurement were highly correlated (r=0.96), which explains why this global interference cancellation method should work well when global interference is dominating. In addition, the results suggested that association between the superficial layer hemodynamics and the total global interference is time-varying.

Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template

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.

Scalp and skull influence on near infrared photon propagation in the Colin27 brain template

Near-infrared neuromonitoring (NIN) is based on near-infrared spectroscopy (NIRS) measurements performed through the intact scalp and skull. Despite the important effects of overlying tissue layers on the measurement of brain hemodynamics, the influence of scalp and skull on NIN sensitivity are not well characterized. Using 3555 Monte Carlo simulations, we estimated the sensitivity of individual continuous-wave NIRS measurements to brain activity over the entire adult human head by introducing a small absorption perturbation to brain gray matter and quantifying the influence of scalp and skull thickness on this sensitivity. After segmenting the Colin27 template into five tissue types (scalp, skull, cerebrospinal fluid, gray matter and white matter), the average scalp thickness was 6.9 ± 3.6 mm (range: 3.6-11.2mm), while the average skull thickness was 6.0 ± 1.9 mm (range: 2.5-10.5mm). Mean NIN sensitivity - defined as the partial path length through gray matter divided by the total photon path length - ranged from 0.06 (i.e., 6% of total path length) at a 20mm source-detector separation, to over 0.19 at 50mm separations. NIN sensitivity varied substantially around the head, with occipital pole exhibiting the highest NIRS sensitivity to gray matter, whereas inferior frontal regions had the lowest sensitivity. Increased scalp and skull thickness were strongly associated with decreased sensitivity to brain tissue. Scalp thickness always exhibited a slightly larger effect on sensitivity than skull thickness, but the effect of both varied with SD separation. We quantitatively characterize sensitivity around the head as well as the effects of scalp and skull, which can be used to interpret NIN brain activation studies as well as guide the design, development and optimization of NIRS devices and sensors.