Cortical Complexity Analysis Based On Magnetic Resonance Imaging

Based on high resolution magnetic resonance imaging (MRI), numerous techniques have been developed for computational morphometry. Using these tools, we can detect changes in brain structures due to development, aging and disease. Among the brain structures one of the most studied is the cerebral cortex, a highly complex structure with rich cortical folding. Many brain diseases are associated with abnormal pattern of cortical folding. Therefore, defining sensitive measures of cortical folding and detecting changes of cortical folding are the most interesting and challenging problems faced by clinicians, neuroscientists and engineers. In this study, we mainly focus on detecting the abnormalities of cortical folding due to diseases and formulation of new measure of cortical folding. The main contributions are as follows:1. Using local Gyrification Index (1GI), we investigated the cortical folding pattern between normal controls (NC) and patients with major depressive disorder (MDD). We further calculated the mean 1GI of the difference regions between NC and patients with MDD and correlated the mean 1GI of each region with the duration of illness and severity of depressive symptom of the patients respectively. Compared with NC, patients with MDD showed significantly decreased 1GI in mood-related regions. In addition, we revealed that the mean 1GI of right insular and temporal operculum correlated inversely with the severity of depressive symptom.2. Using a 2D ROI-based Gyrification Index, a preliminary study reported significantly reduced cortical folding in pre-frontal lobe in MR. With a 3D 1GI method, this study aimed to further investigate the abnormalities of cortical folding in MR and to explore the possible causes of these abnormalities. Statistical analysis revealed that patients with MR had significantly reduced 1GI in the lateral and medial prefrontal cortices, the right superior temporal gyrus, the left superior parietal lobe, the bilateral insular and their adjacent cortices as well as visual and motor cortices, compared with normal controls. The observed abnormal pattern of cortical folding revealed by significant reduction of 1GI in multiple brain regions might reflect the developmental disturbance in intracortical organization and cortical connectivities in MR.3. We proposed a new surface-based fractal information dimension (FID) method to quantify the cortical complexity. Unlike the traditional box-counting method to measure the capacity dimension, our method is a surface-based fractal information dimension method, which incorporates surface area into the probability calculation and thus encapsulates more information of the original cortical surfaces. The accuracy of the algorithm was validated via experiments on phantoms. With the proposed method, we studied the abnormalities of the cortical complexity of the early blind, compared with matched NC. We found significantly increased FIDs in the left occipital lobe and decreased FIDs in the right frontal and right pafietal lobe in early blind compared with controls. The results demonstrated the potential of the proposed method for identifying cortical abnormalities.