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.

Fig. 1

Top: diagram showing the experimental design for the within-session and between-sessions datasets. Bottom: list of indices calculated for each measure, both at whole grey matter (GM) and voxel-wise resolution. All indices were calculated for every repeatability considered: within session, between sessions - same day and between sessions – different day. [1] (McGraw and Wong, 1996).

Fig. 2

A - Inspired gas fractions during the respiratory task. B – Tidal traces of a single representative subject. C - End-tidal traces averaged across all subjects and sessions of the within-session dataset. Vertical lines highlight the timing of the respiratory task. In both B and C periods of hypercapnia and of hyperoxia are clearly visible, interleaved with short periods of normocapnia-normoxia. Positive and negative emphases can be distinguished before and after the plateau hyperoxic periods, respectively. As expected, periods of hyperoxia appear to produce a reduction in end-tidal CO₂ and periods of hypercapnia are associated with slight increases in end-tidal O₂.

Fig. 3

Scatterplots for the correlation analysis between the two sets of measurement (denoted as 1 and 2) of the within-session dataset. Dotted lines show unity and also displayed are the coefficient of determination (R²) and statistical significance (p).

Fig. 4

ICC and CV indices calculated at a grey matter level for all estimated parameters. A,B: within session dataset; C,D: between sessions, same day dataset; E,F: between sessions, different day dataset. Indices for individual subjects (CV_intra and ICC_global) are shown in black circles and dots while group indices (CV_inter and ICC_global) are shown in red stars and crosses respectively for ICC and CV. CV_intra is the intra-subject CV and CV_inter is the inter-subjects CV; ICC_global is the ICC(A,k) calculated between subjects at a GM level and ICC_spatial is the ICC(A, 1) calculated within subjects across voxels.

Fig. 5

Voxel-wise CV indices calculated from the within-session dataset. A: results for intra>, the mean across subjects of the intra-subject CV. B: results for CV_inter, the inter-subject CV. For both, reported are the axial views of the calculated maps for each physiological parameter and relative histograms showing the distributions of the calculated values (in red the median and in black the interquartile range limits).

Fig. 6

Results of the repeatability analysis on the between-sessions dataset with measurements acquired in the same day. Scatterplots for the correlation analysis between the two sets of measurement (denoted as 1 and 2), for all four estimted physiological parameters. Displayed are the line of unity (dotted), the coefficient of determination (R²) and the statistical significance (p).

Fig. 7

Results of the repeatability analysis on the between-sessions dataset with measurements acquired in different days. Scatterplots for the correlation analysis between the two sets of measurement (denoted as 1 and 2), for all four estimted physiological parameters. Displayed are the line of unity (dotted), the coefficient of determination (R²) and the statistical significance (p).

Fig. 8

Voxel-wise CV indices calculated from the between-session dataset for the same day (A,B) or different day (C,D) case. Axial view of the calculated maps for each physiological parameter and relative histograms showing the distributions of the calculated values (in red the median and delimited in black the interquartile range). intra> is the mean across subjects of the intra-subject CV and CV_inter is the inter-subjects CV.