Spatial And Temporal Analysis Of Intrinsic Optical Imaging Dataset

This dissertation is focused on the spatial/temporal analysis methods for intrinsic optical imaging dataset and their applications in spatio-temporal pattern analysis of spontaneous low frequency oscillations.In blind separation of brain mapping signals, the spatial plus temporal structure information is utilized. It is assumed that the interesting signals alter smoothly cross both space and time, i.e. the neighboring sample-points are similar. Then the object function which quantifies and integrates temporal and spatial structure information is defined and maximized. Three problems are solved in this procedure. (a) A new definition of the spatial pattern of a temporal signal is given. This makes it possible to quantify the spatial structure information for a temporal source. (b) A novel method for maximizing autocorrelation is proposed. Unlike traditional methods, it does not rely on the so-called"symmetry assumption for delayed/shifted covariance matrix". (c) A low dimensional procedure for temporal analysis is developed. It could be applied to any traditional temporal analysis methods and reduce their computational complexity without losing any information.The straightforward image projection technique is introduced into the temporal source separation. By represented it in the matrix format, the differences and relationship between this technique and the temporal/spatial analysis are revealed. It is indicated that the straightforward image projection technique performs the data analysis from a novel viewpoint by traditional procedure. The concept of"generalized timecourse"is proposed. Because there are both temporal and spatial relationships among the sample points in one"generalized timecourse", it is possible to define temporal plus spatial structure information and maximize it.The critical problem in OI for human brain, the cortex movement reduction, is also studied in this dissertation. A new cortex image registration algorithm based on thin-plate splines is proposed. In the splines interpolation, the point constraints are weakened and the interpolation function needs not to exactly go through the landmarks. Based on estimating the localization accuracy of each landmark, all landmarks are categorized into several groups. Each group is weakened by different weight values. A cost function which quantifies the registration errors is given, and the weight values are decided by minimizing this cost function.By the blind source separation method and Fourier spectrum analysis technique, the spatio-temporal pattern of spontaneous low frequency oscillations is studied. After the electrical stimulation, it is observed that the phases of the LFO signals are changed, the amplitudes are increased, and most importantly, the signals in the bilateral somatosensory cortex tend to be synchronized. Based on these phenomena, the origin of the LFO signals is discussed. It is argued that the arteriole vasomotion may be the major contribution to the LFO signals under green illumination (~546nm). The phase relationship among the LFO signals of arteries, veins and cortex is also studied. Based on the phase relationship under red/green illumination, it is suggested that remarkable phase difference at ~605nm shows the motion of deoxy-hemoglobin and none phase difference at ~546nm may imply different mechanism of the LFO signals of cortexes and vessels.