Abstract

AbstractA theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO2) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model we obtain a transcendental equation relating CBF and CMRO2 changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bio-heat equation describing the rate of temperature changes in the brain, we obtain a non autonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. In order to test the method, temperature maps obtained from a real BOLD dataset are calculated showing temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The method could find potential clinical uses as it is an improvement over conventional methods which require invasive probes and can record only few locations simultaneously. Interestingly, the statistical analysis revealed that significant temperature variations are more localized than BOLD activations. This seems to exclude the use of temperature maps for mapping neuronal activity as areas where it is well known that electrical activity occurs (such as V5 bilaterally) are not activated in the obtained maps. But it also opens questions about the nature of the information processing and the underlying vascular network in visual areas that give rise to this result.

Highlights

  • The balance between metabolic heat production, heat removal by cerebral blood flow (CBF ), and conductive heat loss from the region of interest (ROI) to neighbouring regions, characterize the temperature dynamics in the ROI (Trübel et al, 2006)

  • The simulated Blood Oxygenation Level Dependent (BOLD) data were generated with a step size of 0.1s using the Metabolic/Hemodynamic Model (MHM) proposed in Sotero and Trujillo-Barreto (2007) with the standard parameters set used in that paper

  • In this paper we presented a theoretical framework for obtaining CBF, CMRO2 and temperature responses from registered BOLD signals

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Summary

Introduction

The balance between metabolic heat production (due to the oxidation of glucose), heat removal by cerebral blood flow (CBF ) , and conductive heat loss from the region of interest (ROI) to neighbouring regions, characterize the temperature dynamics in the ROI (Trübel et al, 2006). We rewrite the coupling between oxygen consumption and cerebral blood flow given by the oxygen limitation model (Buxton and Frank, 1997) as a gamma function When substituting this new equation in the BOLD signal model of Davis et al (1998) we obtain a transcendental equation relating CBF and BOLD responses, which is solved in terms of the Lambert W function (Corless et al, 1996). The temperature in a brain voxel can be obtained from the associated BOLD signal by solving numerically the following non autonomous ordinary differential equation: Tɺ (t) + p (t )T (t) = g (t ).

From simulated BOLD data to temperature time series
From real BOLD data to brain temperature maps
Discussion
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