Chaos Analysis of Brain MRI for Studying Mental Disorders

Abstract

A tendency of increase has been observed for mental disorders in the last years. However, the conventional diagnosis methods like history taking suffer from low objectivity. Recently, some research on the diagnosis of mental disorders focused on cerebral blood flow change has been reported. Data of near-infrared spectroscopy(NIRS) is needed for this analysis, but this device is not available in all medical facilities and the method is not established yet. It is a current challenge to develop a method that can be conducted easily and provides an accurate diagnosis for patients at an easily stage of the disease. In this research, we based our analysis method on brain MRI, which many medical institutions use to take medical images. At first the characteristics of the grey matter distribution have been extracted in order to generate a time series of this feature for every slice image. The time series were further analyzed by extracting the largest Lyapunov Exponent(LLE), a measure of chaos theory. The descriptor of mental health of a subject is finally calculated by averaging the LLE measures for all image slices. We were able to confirm that a significant difference of descriptors can be found for young, middle-age, elderly persons and patients with Alzheimer's disease. It is an open task for the future to verify if this method can be used for other mental diseases as well. Our long-term perspective is to introduce this diagnostic method to official medical guidelines. © Springer-Verlag Berlin Heidelberg 2014.