Dataset Information

Assessing the repeatability of absolute CMRO₂, OEF and haemodynamic measurements from calibrated fMRI.

ABSTRACT: As energy metabolism in the brain is largely oxidative, the measurement of cerebral metabolic rate of oxygen consumption (CMRO₂) is a desirable biomarker for quantifying brain activity and tissue viability. Currently, PET techniques based on oxygen isotopes are the gold standard for obtaining whole brain CMRO₂ maps. Among MRI techniques that have been developed as an alternative are dual calibrated fMRI (dcFMRI) methods, which exploit simultaneous measurements of BOLD and ASL signals during a hypercapnic-hyperoxic experiment to modulate brain blood flow and oxygenation. In this study we quantified the repeatability of a dcFMRI approach developed in our lab, evaluating its limits and informing its application in studies aimed at characterising the metabolic state of human brain tissue over time. Our analysis focussed on the estimates of oxygen extraction fraction (OEF), cerebral blood flow (CBF), CBF-related cerebrovascular reactivity (CVR) and CMRO₂ based on a forward model that describes analytically the acquired dual echo GRE signal. Indices of within- and between-session repeatability are calculated from two different datasets both at a bulk grey matter and at a voxel-wise resolution and finally compared with similar indices obtained from previous MRI and PET measurements. Within- and between-session values of intra-subject coefficient of variation (CV_intra) calculated from bulk grey matter estimates 6.7?±?6.6% (mean?±?std.) and 10.5?±?9.7% for OEF, 6.9?±?6% and 5.5?±?4.7% for CBF, 12?±?9.7% and 12.3?±?10% for CMRO₂. Coefficient of variation (CV) and intraclass correlation coefficient (ICC) maps showed the spatial distribution of the repeatability metrics, informing on the feasibility limits of the method. In conclusion, results show an overall consistency of the estimated physiological parameters with literature reports and a satisfactory level of repeatability considering the higher spatial sensitivity compared to other MRI methods, with varied performance depending on the specific parameter under analysis, on the spatial resolution considered and on the study design.

SUBMITTER: Merola A

PROVIDER: S-EPMC6503182 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Assessing the repeatability of absolute CMRO2, OEF and haemodynamic measurements from calibrated fMRI.

Merola Alberto A Germuska Michael A MA Murphy Kevin K Wise Richard G RG

NeuroImage 20180214

As energy metabolism in the brain is largely oxidative, the measurement of cerebral metabolic rate of oxygen consumption (CMRO2) is a desirable biomarker for quantifying brain activity and tissue viability. Currently, PET techniques based on oxygen isotopes are the gold standard for obtaining whole brain CMRO2 maps. Among MRI techniques that have been developed as an alternative are dual calibrated fMRI (dcFMRI) methods, which exploit simultaneous measurements of BOLD and A ...[more]

PMID: 29454105

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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.

Project description:Rationale and objectivesMammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.Materials and methodsA matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.ResultsIn adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.ConclusionThe calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.

Project description:OBJECTIVE: To quantify nerve conduction study (NCS) reproducibility utilizing an automated NCS system (NC-stat, NeuroMetrix, Inc.). METHOD: Healthy volunteers without neuropathic symptoms participated in the study. Their median, ulnar, peroneal, and tibial nerves were tested twice (7 days apart) by the same technician with an NC-stat instrument. Pre-fabricated electrode arrays specific to each nerve were used. Both motor responses (compound motor action potential [CMAP] and F-waves - all nerves) and sensory responses (sensory nerve action potentials [SNAP] - median and ulnar nerves only) were recorded following supramaximal stimuli. Automated algorithms determined all NCS parameters: distal motor latency (DML), mean F-wave latency (FWL), distal sensory latency (DSL), CMAP amplitude, and SNAP amplitude. Latency was adjusted for skin temperature deviation from reference. Pearson correlation coefficient (CC), intraclass correlation coefficient (ICC), coefficient of variance (CoV), and relative intertrial variation (RIV) were calculated. RESULTS: Fifteen subjects participated in either upper or lower extremity studies with nine participating in both. With the exception of CMAP amplitude, all parameters had CoV less than 0.06. Upper extremity amplitude parameters had CCs greater than 0.85. CCs for latencies were greater than 0.80 except for the median nerve FWL (CC = 0.69). For lower extremity nerves, ICCs were highest for mean FWL (>0.90), followed by DML (>0.82) and then CMAP (peroneal 0.33, tibial 0.73). The 10th to 90th RIV percentiles were bounded by +/-7% for F-wave latencies; +/- 9% for all DSLs; and +/- 11% for DML (except peroneal at 15%). CONCLUSIONS: The reproducibility of NCS parameters obtained with an automated NCS instrument compared favorably with traditional electromyography laboratories. F-wave latencies had the highest repeatability, followed by DML, DSL, SNAP and CMAP amplitude. Given their high reproducibility, automated NCS instrument may encourage wider utilization of NCS in clinical and research applications.

Project description:Transport of therapeutic agents across epithelial barriers is an important element in drug delivery. Transepithelial flux is widely used as a measure of transit across an epithelium, however it is most typically employed as a relative as opposed to absolute measure of molecular movement. Here, we have used the calcium switch approach to measure the maximum rate of paracellular flux through unencumbered intercellular junctions as a method to calibrate the flux rates for a series of tracers ranging in 0.6-900kDa in size across barriers composed of human colon epithelial (Caco-2) cells. We then examined the effects of nanostructured films (NSFs) on transepithelial transport. Two different NSF patterns were used, Defined Nanostructure (DN) 2 imprinted on polypropylene (PP) and DN3 imprinted on polyether ether ketone (PEEK). NSFs made direct contact with cells and decreased their barrier function, as measured by transepithelial resistance (TER), however cell viability was not affected. When NSF-induced transepithelial transport of Fab fragment (55kDa) and IgG (160kDa) was measured, it was unexpectedly found to be significantly greater than the maximum paracellular rate as predicted using cells cultured in low calcium. These data suggested that NSFs stimulate an active transport pathway, most likely transcytosis, in addition to increasing paracellular flux. Transport of IgG via transcytosis was confirmed by immunofluorescence confocal microscopy, since NSFs induced a significant level of IgG endocytosis by Caco-2 cells. Thus, NSF-induced IgG flux was attributable to both transcytosis and the paracellular route. These data provide the first demonstration that transcytosis can be stimulated by NSFs and that this was concurrent with increased paracellular permeability. Moreover, NSFs with distinct architecture paired with specific substrates have the potential to provide an effective means to regulate transepithelial transport in order to optimize drug delivery.

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Assessing the repeatability of absolute CMRO₂, OEF and haemodynamic measurements from calibrated fMRI.

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Assessing the repeatability of absolute CMRO<sub>2</sub>, OEF and haemodynamic measurements from calibrated fMRI.

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