Analysis Of Resting-state FMRI Dataset And Its Applications In Amblyopia

Human brain is a huge network system with the most complex structure. In cognitive neuroscience, the application of brain imaging techniques plays a very important role to understand the characteristics of brain. The functional magnetic resonance imaging (fMRI) based on the theory of blood-oxygen level dependent (BOLD) is a non-invasive and non-radiative technology, and it has high temporal and spatial resolution. The technology has become an important tool to do scientific researches on brain and has been concerned by many science branches, such as neuroscience, cognition, psychology and clinic et al. Resting-state functional MRI could be an advantageous choice for applications and clinical researches because it can be more easily accepted by patients without subjects'response and intricate experiment design.A lot of fMRI studies have demonstrated that there exist spontaneous low frequency fluctuations (LFFs) in the resting brain. Although the neurophysiological mechanisms of LFFs remain unclear, it has been suggested that LFFs are physiologically meaningful for keeping normal brain function and are associated with pathophysiology of the diseases. In this work, focused on the application of fMRI in brain network, aimed at clinical applications in diseases and understandings of brain complexity, systematic researches and explorations are conduced to analysis methods of resting-state fMRI, including independent component analysis (ICA), regional homogeneity (ReHo), amplitude of low frequency fluctuations(ALFF). On the basis, dysfunction study of the visual cortex in patients with amblyopia is studied so as to enhance the understanding of the pathological mechanism of amblyopia. The main contents in this dissertation can be stated as follows:1?A fast ICA algorithm with cubic convergence is prorosed. ICA is a new statistical signal processing technique. The goal of ICA is to recover independent sources given only sensor observations that are unknown linear mixtures of the unobserved independent source signals. In this paper, negentropy is introduced as measure of independence, and an optimization model for ICA is presented. A new Newton iterative algorithm based on the model is proposed. In order to accelerate the convergence, an improvement on Newton method is made which makes the convergence of the new algorithm cubic. Applying the algorithm and two other algorithms to invivo fMRI data, the results show that the new algorithm separates independent components stably. It has faster convergence speed and less computation than the other two algorithms.2?A novel approach of selecting“seed”regions based on ReHo in functional connectivity is proposed. In the previous methods,“seed”is selected utilizing prior anatomical information (i.e. knowledge-based) or activation maps (i.e. activation-based) without taking the natures of resting-state data into account. In this dissertation, ReHo is used to select the region with maximum ReHo value as“seed”in functional connectivity studies using“seed”correlation analysis. This provides a novel way to study the resting-state functional networks. By using the new method combing ReHo and functional connectivity, the resting-state visual network of human brains is identified in normal subjects.3?A new method to study the resting-state visual network of the anisometropic amblyopia patients is advanced from the perspective of functional connectivity only using the resting-state fMRI data. ICA is used to separate the fMRI data. Considering that ICA algorithm can hardly choose the optimal one of the separated components, goodness-of-fit method is introduced to extract the resting-state visual networks of the anisometropic amblyopia patients and normal controls. Intra-group analysis and inter-group analysis are performed. The results show that there are remarkable deficits on different levels of visual cortex in anisometropic amblyopia predominantly in the extrastriate cortex rather than the striate cortex. The resting-state fMRI provides a new way to investigate visual cortex deficit in amblyopia.4?A new method to study the functional impairment in amblyopia using ALFF is proposed. Most studies of resting-state fMRI have investigated LFFs from the aspect of temporal synchronization, i.e. functional connectivity between brain regions. Although a result of abnormal functional connectivity between two remote areas is comprehensive and integrative, one could not draw any conclusion about which area is abnormal from such an examination. The ALFF signal can reflect cerebral spontaneous neuronal activity, indicating the energy of neuronal activity. The dissertation explores default mode network in the resting brain using ALFF. Our result shows a large overlap with those known suggesting ALFF is an effective method to study spontaneous LFFs of the resting-state. In addition, the brain activation of amblyopia patients is studied for the three resting states: eye-closed, healthy eye-stimulated, suffering eye-stimulated. The results show different brain activations for the three resting states: healthy eye-stimulated has the most increased ALFF in the extrastriate areas of the visual cortex due to the most visual input; suffering eye-stimulated has the most increased ALFF in the superior frontal gyrus which is related to selective attention, due to poor sight demanding more efforts to attention. ALFF has potential in the clinical applications of localization of the brain cortex function and provides a different way to investigate the pathophysiological mechanisms in amblyopia.