Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements
Highlights
► Pial vasculature contaminates the NIRS signal. ► Concurrent NIRS-fMRI recordings enables estimation of the cortical signal contribution. ► 20% of the HbR signal and 75% of the HbO signal has cortical origins (finger tapping). ► HbT should be used rather than HbO or HbR to map cerebral activity with NIRS.
Introduction
Near-infrared spectroscopy (NIRS) (Hillman, 2007, Hoshi, 2007, Villringer et al., 1993) is a non-invasive technique for monitoring the hemodynamic changes occurring in superficial regions of the cortex. Using non-ionizing light, NIRS measures the fluctuations of the two dominant biological chromophores in the near-infrared spectrum: oxygenated (HbO) and deoxygenated or reduced hemoglobin (HbR).
Over the past 15 years, NIRS has become an attractive alternative to functional Magnetic Resonance Imaging (fMRI), with several clinical advantages (Irani et al., 2010). NIRS is portable and less susceptible to movement artifacts enabling long term monitoring of the hemodynamic activity at the bedside. However, the spatial resolution of NIRS is 1–3 cm (Boas et al., 2004) which is less than the resolution of standard fMRI scanners. Another disadvantage of NIRS is its penetration depth which limits its sensitivity to the upper 1 cm of the cerebral cortex (Boas et al., 2004).
The biophysical origin of the functional NIRS signal is the variation of HbO and HbR concentration resulting from changes in Cerebral Blood Flow (CBF) and Cerebral Metabolic Rate of Oxygen (CMRO2). For evoked brain activity, the resulting variations in HbO and HbR are described by the Balloon model (Buxton et al., 1998, Buxton et al., 2004, Friston, 2000). According to this model, the arterial dilation driven increase in CBF following brain activation induces a passive volume increase of the capillary and venous vasculature (termed the windkessel compartment) as well as an increase in oxygen saturation. Compartmental microscopic hemodynamic measurements (Drew et al., 2011, Hillman et al., 2007) have revealed that this evoked oxygenation increase propagates through the pial veins located at the surface of the cortex but that this pial compartment exhibits very little volume variation following brain activation. This pial vein signal is generally negligible in fMRI since the high anatomical resolution allows the signal coming from the cortical region to be isolated. Conversely, the NIRS signal is integrated through the different superficial layers of the head. This potentially gives rise to a pial vein contamination of the signal if the pial vessels happen to coincide with the path of the light during its propagation through the tissue.
Preliminary analysis of the impact of pial vasculature in NIRS has been performed by Dehaes et al. (2011). However, very few studies of the effect of pial vein oxygenation changes (termed “the washout effect”) on the NIRS signal have been performed (Firbank et al., 1998, Huppert et al., 2009).
In this paper, the effect of pial vein contamination of the NIRS signal is investigated over the motor cortex where superficial pial veins are present (Gray, 2000). We first quantify cortical and pial vein contributions to the NIRS signal by Monte Carlo simulation performed on a realistic anatomical volume containing pial veins acquired with MRI. We then use a biophysical model of the fMRI signal to analyze concurrent NIRS-fMRI data acquired over the motor cortex of human subjects during a finger tapping task. The cortical contribution to the HbR and HbO signals relative to the cortical contribution of the HbT signal are both estimated by fitting the biophysical model to the multimodal data.
Section snippets
Theory
We used biophysical modelling to investigate the contribution of cortical changes in HbO and HbR to the NIRS signal taken from in vivo NIRS-BOLD measurements. The Obata model (Obata et al., 2004), a refined version of the original Balloon model (Buxton et al., 1998), describes the fluctuations in the BOLD signal as a function of the changes in deoxyhemoglobin (HbR) concentration and cerebral blood volume (CBV) in a given voxel:
All the parameters
Monte Carlo simulations
The effect of pial vein washout was first investigated using Monte Carlo simulation. The anatomical model used in the simulation was the same as in Dehaes et al. (2011). A high resolution anatomical T1 MRI image was acquired and tissues where then segmented in four different types: scalp, skull, cerebrospinal fluid (CSF) and brain tissue (containing both white and gray matter). The segementation was performed using the Matlab package SPM8. A phase contrast MR angiography was also used to image
Simulation results
Results of the Monte Carlo simulations are summarized in Fig. 2. Panel (A) shows the simulated concentration changes when no increase in oxygen saturation was simulated in the pial veins. Using Eqs. ((11), (13)) with γHbR = γHbT = γHbO = 1, the PVCHbR/PVCHbT and PVCHbO/PVCHbT ratios computed were very close to 1 confirming that the Partial Volume Correction factor (PVC) was the same for HbR, HbO and HbT. The region of interest (ROI) was defined by the three source–detector pairs showing the strongest
Cortical contribution to the NIRS signal
As shown in Fig. 5, our simulations agreed very well with our modeling results from the in vivo measurements, confirming the assumptions made in our computations. The good agreement between the γHbR values computed from simulations (where γHbT was forced to be 1) and the γrHbR values computed from the experimental data indicates that γHbT modeled from the in vivo measurements was very close to 1. This result is strongly supported by exposed cortex animal imaging models (Drew et al., 2011,
Conclusion
We have shown that the NIRS signal collected over the motor cortex during an evoked task contained a smaller cortical contribution for ΔHbR and ΔHbO compared to ΔHbT. The cortical contribution to ΔHbR was equal to 19% of the cortical contribution to ΔHbT. Similarly, the cortical contribution to ΔHbO was equal to 76% of the cortical contribution to ΔHbT. Our results suggest that the pial contamination is less important for ΔHbT, and therefore, the ΔHbT signal should be used rather than ΔHbO or Δ
Acknowledgments
We want to thank Qianqian Fang for providing the Monte Carlo code used in this study as well as providing useful advice. The authors are also grateful to Sungho Tak, Yunjie Tong, Blaise deB. Frederick and Evgeniya Kirilina for fruitful discussions. This work was supported by NIH grants P41-RR14075 and R01-EB006385. L. Gagnon was supported by the Fonds Quebecois sur la Nature et les Technologies.
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