Huppert TJ, Allen MS, Diamond SG, Boas DAEstimating cerebral oxygen metabolism from fMRI with a dynamic multicompartment Windkessel model. Hum Brain Mapp 30:1548-1567

Department of Radiology, University of Pittsburgh, UPMC Presbyterian, 200 Lothrop St., Pittsburgh, PA 15213, USA.
Human Brain Mapping (Impact Factor: 5.97). 05/2009; 30(5):1548-67. DOI: 10.1002/hbm.20628
Source: PubMed


Stimulus evoked changes in cerebral blood flow, volume, and oxygenation arise from responses to underlying neuronally mediated changes in vascular tone and cerebral oxygen metabolism. There is increasing evidence that the magnitude and temporal characteristics of these evoked hemodynamic changes are additionally influenced by the local properties of the vasculature including the levels of baseline cerebral blood flow, volume, and blood oxygenation. In this work, we utilize a physiologically motivated vascular model to describe the temporal characteristics of evoked hemodynamic responses and their expected relationships to the structural and biomechanical properties of the underlying vasculature. We use this model in a temporal curve-fitting analysis of the high-temporal resolution functional MRI data to estimate the underlying cerebral vascular and metabolic responses in the brain. We present evidence for the feasibility of our model-based analysis to estimate transient changes in the cerebral metabolic rate of oxygen (CMRO(2)) in the human motor cortex from combined pulsed arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) MRI. We examine both the numerical characteristics of this model and present experimental evidence to support this model by examining concurrently measured ASL, BOLD, and near-infrared spectroscopy to validate the calculated changes in underlying CMRO(2).

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Available from: Solomon Gilbert Diamond
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    • "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). "
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    ABSTRACT: Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occurring 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.
    Full-text · Article · Feb 2012 · NeuroImage
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    • "This assertion is evident in the CMR O2 driven changes in OIS signal since its use for the calculation of the CMR O2 changes using typical steady-state models will yield CMR O2 changes with the same temporal profile as the OIS data (time constant of B10 seconds). To partly explain this temporal difference between tissue cellular CMR O2 and CMR O2 -driven blood oxygenation changes, relatively simple dynamic models can be used (Vazquez et al, 2008; Zheng et al, 2002; Huppert et al, 2009). However, these models are not sufficient to fully explain the observed temporal lag. "
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    ABSTRACT: The dynamic properties of the cerebral metabolic rate of oxygen consumption (CMR(O2)) during changes in brain activity remain unclear. Therefore, the spatial and temporal evolution of functional increases in CMR(O2) was investigated in the rat somato-sensory cortex during forelimb stimulation under a suppressed blood flow response condition. Temporally, stimulation elicited a fast increase in tissue mitochondria CMR(O2) described by a time constant of ~1 second measured using flavoprotein autofluorescence imaging. CMR(O2)-driven changes in the tissue oxygen tension measured using an oxygen electrode and blood oxygenation measured using optical imaging of intrinsic signal followed; however, these changes were slow with time constants of ~5 and ~10 seconds, respectively. This slow change in CMR(O2)-driven blood oxygenation partly explains the commonly observed post-stimulus blood oxygen level-dependent (BOLD) undershoot. Spatially, the changes in mitochondria CMR(O2) were similar to the changes in blood oxygenation. Finally, the increases in CMR(O2) were well correlated with the evoked multi-unit spiking activity. These findings show that dynamic CMR(O2) calculations made using only blood oxygenation data (e.g., BOLD functional magnetic resonance imaging (fMRI)) do not directly reflect the temporal changes in the tissue's mitochondria metabolic rate; however, the findings presented can bridge the gap between the changes in cellular oxidative rate and blood oxygenation.
    Full-text · Article · Feb 2012 · Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism
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    • "In the absence of pathology or abnormal physiology, the OEF tends to be very similar across all brain regions; this implies that variations in oxygen demand are generally accounted for by concurrent variations in blood supply. MRI techniques have been developed that measure a " whole brain " or global value for the OEF (Huppert et al., 2009; Lu and Ge, 2008; Qin et al., 2011; Van Zijl et al., 1998; Xu et al., 2009), which is valuable in healthy subjects and for developing or validating FMRI methods. However, in the presence of compromised vasculature, brain injury or tumours, these methods do not provide the regional information required for detailed diagnoses. "
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    ABSTRACT: Functional magnetic resonance imaging typically measures signal increases arising from changes in the transverse relaxation rate over small regions of the brain and associates these with local changes in cerebral blood flow, blood volume and oxygen metabolism. Recent developments in pulse sequences and image analysis methods have improved the specificity of the measurements by focussing on changes in blood flow or changes in blood volume alone. However, FMRI is still unable to match the physiological information obtainable from positron emission tomography (PET), which is capable of quantitative measurements of blood flow and volume, and can indirectly measure resting metabolism. The disadvantages of PET are its cost, its availability, its poor spatial resolution and its use of ionising radiation. The MRI techniques introduced here address some of these limitations and provide physiological data comparable with PET measurements. We present an 18-minute MRI protocol that produces multi-slice whole-brain coverage and yields quantitative images of resting cerebral blood flow, cerebral blood volume, oxygen extraction fraction, CMRO(2), arterial arrival time and cerebrovascular reactivity of the human brain in the absence of any specific functional task. The technique uses a combined hyperoxia and hypercapnia paradigm with a modified arterial spin labelling sequence.
    Full-text · Article · Dec 2011 · NeuroImage
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