"Global MRI Diagnostic Tools Via Statistical Complexity Measures"
Magnetic resonance imaging (MRI) data, which are available in a large variety of modalities, has led to challenges regarding how to best utilize and interpret combined information for diagnostic purposes. For example, a MRI study of the brain may involve structural, spectroscopy, perfusion, and functional MRI in the same session, providing anatomical, metabolic, physiological, and functional information. Furthermore, great progress has been made in registering different MRI modalities via the use of brain atlases, so that regional information is also maintained. However, a major problem of this approach is identification of relevant information for diagnosis from the huge amount of regional and multimodal information. This research explores a complimentary, global approach that attempts to to utilize entropy and statistical complexity measures applied to multimodal data to obtain global measures of brain function. As a demonstration of the methods we use simulated brain data from an extension of the BrainWeb Simulated Brain Database to generate simulations for which e.g. levels of brain metabolites in different brain regions can be controlled. We then track changes in entropy and statistical complexity as a function of variation in simulated disease states such as loss of the neuronal marker N-acetylaspartate in gray matter that is widely thought to occur in Alzheimer's disease.