Estimating fluctuations in the rate of cerebral oxygen consumption associated with resting state networks

We propose a method to estimate the level of variation of cerebral metabolic oxygen consumption associated with a visual resting state network. This focuses on extracting relative CBF changes associated with a BOLD-defined visual network. The linked BOLD and CBF variations were estimated using a Bayesian fitting of a non-linear model of dual-echo arterial spin labeling data. Although this relies on the BOLD signal to define the resting state network, the estimates of CMRO2 variation derived from BOLD and CBF estimates appear plausible at around 5%.

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