Neural physiological modeling towars a hemodynamic response function for fMRI (2007)
Motivation
The knowledge of how the Magnetic Resonance "sees" a burst ativation of a small amount of neurons is essential to all analysis and processing done to the fMRI data. Indeed, the blood-oxygenation-level-depedent (BOLD) signal as interesting properties that allow for the existence of fMRI, but it´s underlying physiological are not usually considered when preparing the final data, usually in the form of brain maps. This is, in our point of view, an error and is the main purpose behind this paper that will be published at the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society hekd in Lyon, France: To create a stable and computationally efficient model of the BOLD hemodynamic response function based on the modulation of Physiological events behind it. This would provide first, a more assured model and second, a way of looking at the changes of some physiological events after the models parameterization.
Abstract
The BOLD signal provided by the functional MRI medical modality measures the ratio of oxy- to deoxyhaemoglobin at each location inside the brain. The detection of activated regions upon the application of an external stimulus, e.g., visual or auditive, is based on the comparison of the mentioned ratios of a rest condition (pre-stimulus) and of a stimulated condition (post-stimulus). Therefore, an accurate knowledge of the impulse response of the BOLD signal to neural stimulus in a given region is needed to design robust detectors that discriminate, with a high level of confidence activated from non activated regions.
Usually, in the literature, the hemodynamic response has been modeled by known functions, e.g., gamma functions, fitting them, or not, to the experimental data. In this paper we present a different approach based on the physiologic behavior of the vascular and neural tissues.
Here, a linear model based on reasonable physiological assumptions about oxygen consumption and vasodilatation processes are used to design a linear model from which a transfer function is derived. The estimation of the model parameters is performed by using the minimum square error (MSE) by forcing the adjustment of the stimulus response to the observations.
Experimental results using real data have shown that the proposed model successfully explains the observations allowing to achieve small values for the fitting error.
Model Schematic
Link
29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society in conjunction with the biennial Conference of the French Society of Biological and Medical Engineering (SFGBM)