Functional Magnetic Resonance Dynamic Model And Static Brain Networks

Functional magnetic resonance imaging (fMRI) is mainly based on blood oxygenation level dependent (BOLD). It is the most efficient method that can be used to precisely locate brain activities without invasion. With very high spatial resolution and potential high temporal resolution, fMRI is fit for the spatial and temporal analysis of neural action and the research of advanced brain function. So it is being concerned by many science branches such as neuroscience, cognition and clinical diagnosis et al and is a hot point in current brain research.In the sight of research of fMRI dynamic model, basing on the analysis of Friston's BOLD model of dynamics, combining Agnes Aubert's coupling model of brain electrical activity and metabolism, we proposed an extended BOLD dynamic model. The improved model connected the cerebral electrical activity and metabolism with the blood flow or blood volume of the hemodynamic model, and the result of emulator consisted with the real experiment data. The result of researching of input signal indicated that different input signals caused different effect to the output of the model. The improved model could simulate the physiological process better.In the sight of research of cerebral default model, fMRI technique provides some measures for researching of brain functional action and correlation among different brain functional regions. The activity mode of different brain functional regions is a network mode, and these brain functional regions carry out many complex brain functional actions together. The default networks are one of these networks, and reflect the intrinsic activity and rules of brain. It is the base of research of brain function.In this paper, an approach integrating neighborhood local correlation (NLC) and hierarchical clustering analysis (HCA) methods is introduced into investigating default networks. In HC procedure, a new spatial temporal distance measure is proposed for better utilizing spatial-temporal information in fMRI datasets. Comparing to those model-driven methods, it doesn't need prior information, for instance the stimulus mode,...