KEYWORDS: Functional magnetic resonance imaging, Contamination, Blood, Two photon excitation microscopy, Angiography, Monte Carlo methods, Magnetism, Tissues, Brain, Capillaries
The vascular space occupancy (VASO) fMRI method probes changes in cerebral blood volume (CBV) under various physiological states, including neuronal activation in humans. However, it requires a careful choice of sequence parameters because the blood oxygen-level dependent (BOLD) effect offsets the VASO signal. Assessing this BOLD contamination as a function of pulse sequence parameters would improve the quantification of CBV changes with VASO. However, this task requires knowledge of the cerebral vascular geometry of the MRI voxel. Towards this end, optical microscopy can provide high-resolution 3D images of vasculature. Here, we use detailed angiograms of rodent brain acquired with two-photon microscopy to model fMRI signals (VASO and BOLD) from first principles using Monte Carlo diffusion of water protons. We present quantitative plots of VASO together with intra- and extravascular BOLD fractional signal changes as a function of echo time (TE), for spin echo (SE) and gradient echo (GRE) pulse sequences, at low to ultra-high magnetic fields. Our results indicate that at 3T, the BOLD contamination of the VASO response is under 12% for GRE and 2% for SE up to TE=6 ms, but this contamination is significantly higher at 7T and above. We also found GRE BOLD intravascular contributions of 85% at 1.5T, 55% at 3T and 4% at 7T and SE intravascular contributions of 70% at 1.5T, 40% at 3T and 10% at 7T. These results may provide important information to optimize the pulse sequence timing in human VASO and BOLD fMRI, leading the way to a wider application of these fMRI techniques in healthy and diseased brain.
Computing microvascular cerebral blood flow (μCBF) in real cortical angiograms is challenging. Here, we investigated whether the use of Doppler optical coherence tomography (DOCT) flow measurements in individual vessel segments can help in reconstructing μCBF across the entire vasculature of a truncated cortical angiogram. A μCBF computational framework integrating DOCT measurements is presented. Simulations performed on a synthetic angiogram showed that the addition of DOCT measurements, especially close to large inflowing or outflowing vessels, reduces the impact of pressure boundary conditions and estimated vessel resistances resulting in a more accurate reconstruction of μCBF. Our technique was then applied to reconstruct microvascular flow distributions in the mouse cortex down to 660 μm by combining two-photon laser scanning microscopy angiography with DOCT.
KEYWORDS: Near infrared spectroscopy, Veins, Monte Carlo methods, Brain, In vivo imaging, Tissue optics, Functional magnetic resonance imaging, Tissues, Oxygen, Data modeling
Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occuring at the surface of
the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these
veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the
NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS
signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the
cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic
geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during
motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo
measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR)
is equal to 16-22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the
cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73-79% of the cortical contribution
to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and
therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of
ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein
contributions, our finger tapping results do reveal the importance of considering the pial contribution.
Biophysical models of hemodynamics provide a tool for quantitative multimodal brain imaging by allowing a deeper
understanding of the interplay between neural activity and blood oxygenation, volume and flow responses to stimuli.
Multicompartment dynamical models that describe the dynamics and interactions of the vascular and metabolic
components of evoked hemodynamic responses have been developed in the literature. In this work, multimodal data
using near-infrared spectroscopy (NIRS) and diffuse correlation flowmetry (DCF) is used to estimate total baseline
hemoglobin concentration (HBT0) in 7 adult subjects. A validation of the model estimate and investigation of the partial
volume effect is done by comparing with time-resolved spectroscopy (TRS) measures of absolute HBT0. Simultaneous
NIRS and DCF measurements during hypercapnia are then performed, but are found to be hardly reproducible. The
results raise questions about the feasibility of an all-optical model-based estimation of individual vascular properties.
In this work, we explore diffuse correlation spectroscopy (DCS) in a two-layered geometry. We compare the
effiency of an homogeneous and a two-layered model to recover flow changes. By simulating a realistic human
head with MRI anatomical data, we show that the two-layered model allows distinction between superficial layers
and brain hemodynamic changes. The results show that the two-layered model provides a better estimate for
the flow change than the homogeneous one. Experimental measurements with a two-layered dynamical phantom
confirm the ability of the two-layered analytical model to distinguish flow increase in each layer.
We present in vivo measurements of baseline physiology from five subjects with a four-wavelength (690, 750, 800, and 850 nm) time-resolved optical system. The measurements were taken at four distances: 10, 15, 25, and 30 mm. All distances were fit simultaneously with a two-layered analytical model for the absorption and reduced scattering coefficient of both layers. The thickness of the first layer, comprising the skin, scalp, and cerebrospinal fluid, was obtained from anatomical magnetic resonance images. The fitting procedure was first tested with simulations before being applied to in vivo measurements and verified that this procedure permits accurate characterization of the hemoglobin concentrations in the extra- and intracerebral tissues. Baseline oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations and oxygen saturation were recovered from in vivo measurements and compared to the literature. We observed a noticeable intersubject variability of the hemoglobin concentrations, but constant values for the cerebral hemoglobin oxygen saturation.
KEYWORDS: Tissues, Absorption, Functional magnetic resonance imaging, Magnetic resonance imaging, Brain, Diffusion, Photons, Signal detection, 3D modeling, Monte Carlo methods
Diffuse optical imaging (DOI) is a relatively new functional imaging modality offering the possibility to record changes
in hemoglobin concentrations. It is based on the propagation of near-infrared light through biological tissues. By
measuring the optical absorption of the blood in the cortex, DOI enables the estimation of changes of deoxy-hemoglobin
(HbR) and oxy-hemoglobin (HbO2) concentrations. It thus provides indirect information on neuronal activity.
Drawbacks of optical imaging are its lack of quantification abilities as well as poor spatial resolution. Although not
much can be done concerning the second issue, diffusion being the limiting factor, one can aim at more quantitative data
by the use of extra information. As an example, the determination of baseline concentrations done by fitting a temporal
or frequency curve to recover background concentrations is not expected to be accurate due to the heterogeneity of the
underlying tissues. The vascular architecture, unknown when doing DOI alone, also plays a significant role in the signal
detected. Partial volume effects due to an optode pair overlapping a large vein will lead to confounding data and create
difficulties in analyzing the neuronal activation. Here we show that fusion with MRI, but done outside the scanner, may
help solving some of these issues.
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