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Publications (3)0 Total impact

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    ABSTRACT: Small-world network is a highly clustered system but with small mean path length between networks which allow the information transferred with high efficiency. The human brain can be considered as a sparse, complex network modeled by the small-world properties. Once the brain network was disrupted by disease, the small-world properties would be altered to manifest that the information integration was inefficiency and the network was loosely organized. The aim of this study is to investigate the difference of small-world properties of brain functional network derived from resting-state functional magnetic resonance imaging (fMRI) between the healthy subjects and the patients with Bipolar disorder (BD). The functional MRI data was acquired from 5 healthy subjects and 5 patients with Bipolar disorder. All images of each subject were parcellated into 90 cortical and sub-cortical regions which were defined as the nodes of the network. The functional relations between the 90 regions were estimated by the frequency-based mutual information followed by thresholding to construct a set of undirected graphs. Small-world properties, such as the degree and strength of the connectivity, clustering coefficient of connections, mean path length among brain regions, global efficiency and local efficiency, are examined between any pair of functional areas. Our findings indicated that, in comparison with the control subjects, the BD patients presented smaller values of the degree, the strength of the connectivity and the clustering coefficient of connections, whereas larger values of mean path length among brain regions. This suggested the reduced global and local efficiency of the small-world properties for BD patients. In addition, the small-world properties of BD patients were altered significantly in some regions in the frontal lobes and limbic system which were in good agreement with the dysfunction connectivity reported by the previous literatures in the study of bipolar disorder.
    12/2008: pages 726-729;
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    ABSTRACT: Multiple system atrophy (MSA) is a well-known neurodegenerative disorders that present parkinsonism syndrome and autonomic dysfunction. Patients with MSA who have the combination of parkinsonism and cerebellar ataxia are referred to as MSA-C. Brain diffusion-weighted imaging (DWI) offers the potential for objective criteria in the diagnosis of MSA. We aim to develop an automatic method to segment out the abnormal whole brain area in MSA-C patients based on the 13-direction DWI raw data. The whole brain DWI raw data of fifteen normal subjects and nine MSA-C patients were analyzed. In this study, we proposed a novel method to perform tissue segmentation directly based on the directional information of the DWI images, rather than using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) as in the previous literatures. Specifically, a hierarchical clustering (HC) technique was first applied on the down-sampled data to initialize the model parameters for each tissue cluster followed by automatic segmentation using the expectation maximization (EM) algorithm. Our results demonstrate that the HC-EM is effective in multi-tissue classification, namely, the cerebrospinal fluid, gray matter, and several areas of white matters, on the DWI raw data. The segmented patterns and the corresponding intensities of thirteen directions of the cerebellum in MSA-C patients showed the decrease of the anisotropy, which were evidently different from the results in normal subjects.
    12/2008: pages 722-725;
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    ABSTRACT: Multiple system atrophy-cerebellar (MSA-C) is a degenerative neurological disease of brain. In this study, we used three-dimensional (3D) fractal dimension (FD) method to investigate the structural complexity change of human cerebellum white matter (CBWM) and gray matter (CBGM) for MSA-C disease diagnosis. Twenty patients and twenty-three normal subjects as the control group participated in this study. The T-1 weighted magnetic resonance (MR) images were processed and 3D CBGM and 3D CBGM were analyzed by the FD method. Results demonstrated that the FD values of patients’ CBWM and CBGM decreased significantly, and that their CBWM FD values decreased more significant than that of CBGM. Results also showed that the 3D FD method was superior to the conventional volumetric method in terms of better reliability, accuracy, and sensitivity.