Dataset Information

Quantitative ? mapping for calibrated fMRI.

ABSTRACT: The metabolic and hemodynamic dependencies of the blood oxygenation level-dependent (BOLD) signal form the basis for calibrated fMRI, where the focus is on oxidative energy demanded by neural activity. An important part of calibrated fMRI is the power-law relationship between the BOLD signal and the deoxyhemoglobin concentration, which in turn is related to the ratio between oxidative demand (CMRO2) and blood flow (CBF). The power-law dependence between BOLD signal and deoxyhemoglobin concentration is signified by a scaling exponent ?. Until recently most studies assumed a ? value of 1.5, which is based on numerical simulations of the extravascular BOLD component. Since the basal value of CMRO2 and CBF can vary from subject-to-subject and/or region-to-region, a method to independently measure ? in vivo should improve the accuracy of calibrated fMRI results. We describe a new method for ? mapping through characterizing R2' - the most sensitive relaxation component of BOLD signal (i.e., the reversible magnetic susceptibility component that is predominantly of extravascular origin at high magnetic field) - as a function of intravascular magnetic susceptibility induced by an FDA-approved superparamagnetic contrast agent. In ?-chloralose anesthetized rat brain, at 9.4 T, we measured ? values of ~0.8 uniformly across large neocortical swathes, with lower magnitude and more heterogeneity in subcortical areas. Comparison of ? maps in rats anesthetized with medetomidine and ?-chloralose revealed that ? is independent of neural activity levels at these resting states. We anticipate that this method for ? mapping can help facilitate calibrated fMRI for clinical studies.

SUBMITTER: Shu CY

PROVIDER: S-EPMC4733593 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Quantitative β mapping for calibrated fMRI.

Shu Christina Y CY Sanganahalli Basavaraju G BG Coman Daniel D Herman Peter P Rothman Douglas L DL Hyder Fahmeed F

NeuroImage 20151124

The metabolic and hemodynamic dependencies of the blood oxygenation level-dependent (BOLD) signal form the basis for calibrated fMRI, where the focus is on oxidative energy demanded by neural activity. An important part of calibrated fMRI is the power-law relationship between the BOLD signal and the deoxyhemoglobin concentration, which in turn is related to the ratio between oxidative demand (CMRO2) and blood flow (CBF). The power-law dependence between BOLD signal and deoxyhemoglobin concentrat ...[more]

PMID: 26619788

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Project description:This study aims to map the acute effects of caffeine ingestion on grey matter oxygen metabolism and haemodynamics with a novel MRI method. Sixteen healthy caffeine consumers (8 males, age=24.7±5.1) were recruited to this randomised, double-blind, placebo-controlled study. Each participant was scanned on two days before and after the delivery of an oral caffeine (250mg) or placebo capsule. Our measurements were obtained with a newly proposed estimation approach applied to data from a dual calibration fMRI experiment that uses hypercapnia and hyperoxia to modulate brain blood flow and oxygenation. Estimates were based on a forward model that describes analytically the contributions of cerebral blood flow (CBF) and of the measured end-tidal partial pressures of CO2 and O2 to the acquired dual-echo GRE signal. The method allows the estimation of grey matter maps of: oxygen extraction fraction (OEF), CBF, CBF-related cerebrovascular reactivity (CVR) and cerebral metabolic rate of oxygen consumption (CMRO2). Other estimates from a multi inversion time ASL acquisition (mTI-ASL), salivary samples of the caffeine concentration and behavioural measurements are also reported. We observed significant differences between caffeine and placebo on average across grey matter, with OEF showing an increase of 15.6% (SEM±4.9%, p<0.05) with caffeine, while CBF and CMRO2 showed differences of -30.4% (SEM±1.6%, p<0.01) and -18.6% (SEM±2.9%, p<0.01) respectively with caffeine administration. The reduction in oxygen metabolism found is somehow unexpected, but consistent with a hypothesis of decreased energetic demand, supported by previous electrophysiological studies reporting reductions in spectral power with EEG. Moreover the maps of the physiological parameters estimated illustrate the spatial distribution of changes across grey matter enabling us to localise the effects of caffeine with voxel-wise resolution. CBF changes were widespread as reported by previous findings, while changes in OEF were found to be more restricted, leading to unprecedented mapping of significant CMRO2 reductions mainly in frontal gyrus, parietal and occipital lobes. In conclusion, we propose the estimation framework based on our novel forward model with a dual calibrated fMRI experiment as a viable MRI method to map the effects of drugs on brain oxygen metabolism and haemodynamics with voxel-wise resolution.

Project description:Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n=30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

Dataset Information

Quantitative ? mapping for calibrated fMRI.

Publications

Quantitative β mapping for calibrated fMRI.

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