|Other Abstract|| Diffusion MRI (dMRI) is a non-invasive imaging technique, which can be utilized to reveal the microstructure of the white matter of the in-vivo human brain. Diffusion MRI is widely used in the studies of brain structural connectivity and structural network because it can explore the information of neural micro-structure by probing the diffusion of water molecules. There are two major types for characterizing the diffusion of water molecules. One is diffusion tensor imaging (DTI) and the other is high angular resolution diffusion imaging (HARDI). Schizophrenia (SZ) is a psychiatric disorder and many studies indicated that it may result from the improper functional and structural connectivity among brain regions. In this dissertation, we focus on the methodology of diffusion MRI and its applications in schizophrenia. We firstly studied the effect of b-value in HARDI reconstruction using clinical dMRI data. Then we developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. In the part of applications in schizophrenia, we studied the structural connectivity in schizophrenia patients with auditory verbal hallucination (AVH) and the structural network in schizophrenia high-risk individuals. The main contents and contributions of the dissertation are as follows:|
We used diffusion MRI to study the connectivity and network in schizophrenia (SZ) high-risk (HR) individuals and found abnormal connecting patterns and topological properties. We applied the ICBM-DTI-81 white matter atlas to study the 48 tracts in HC, SZ and HR. And found a significant group difference of FA and MD values in the fornix, right cingulum and right anterior corona radiate. There was a significant difference between SZ and HC in fornix and right cingulum. HR showed intermediate value, and did not differ significantly from either group. In the analysis of brain network, we utilized the AAL template to segment the brain into 90 brain regions which acted as nodes in the network. And the number of fibers between regions is the edge of the network. We found a significant group difference in global efficiency and local efficiency. SZ group showed decreased efficiency compared to HC. HR showed intermediate value, and did not differ significantly from either group. Our findings ont only supported the relationship between schizophrenia and the genetic factor, but also provided help in early diagnosis and treatment of schizophrenia.
We proposed the optimal b-value in HARDI reconstruction for clinical studies. Diffusion MRI data with various b-values (650, 1000, 1500, 2000 and 2500 s/mm2) were collected on a GE 3T MRI scanner. To reconstruct the diffusion ODF and fiber ODF, spherical polar fourier imaging and constrained spherical deconvolution approaches were applied separately. The full width at half maximum (FWHM) of the ODF and the angular difference of the peaks extracted from ODF were measured to investigate the effect of b-value on the ODF reconstruction. The differences in the FWHM for the diffusion ODF and the fiber ODF between the b-values of 2000 s/mm2 and 2500 s/mm2 were not significant. The angular differences of the ODF between 2000 s/mm2 and 2500 s/mm2 were lowest in both single-directional and two-directional situations. Visual inspection of the ODF was used to evaluate the reconstructions. b = 2000 s/mm2 and above revealed most of the two-way or three-way crossing-fiber structures. Considering both the signal-to-noise ratio and the acquisition time, b = 2000 s/mm2 is the basic requirement for ODF reconstruction using current HARDI methods on clinical data.
We developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. Different groups developed various tools for studies of brain network. However, the existing toolkits for dMRI analysis have some limitations. The DiffusionKit package is implemented in C/C++ and Qt/VTK. It includes data format conversion, dMRI preprocessing, local reconstruction, white matter fiber tracking, fiber statistical analyses and various visualization schemes. The rich functions for both data analysis and visualization will facilitate and benefit dMRI research.
We found reproducible abnormal pattern of specific tracts in schizophrenia with auditory verbal hallucination (AVH) base on multi-site diffusion MRI data. AVH is one of the primary symptoms of schizophrenia. Previous diffusion MRI studies have proposed considerable heterogeneity in the abnormal patterns of left arcuate fasciculus (AF) and interhemispheric auditory connectivity. In this study, we investigated the left AF and interhemispheric auditory connectivity in healthy controls (HC), non-AVH and AVH using relative large sample collected from multiple sites. AVH showed significantly increased FA in AF compared with HC. The non-AVH group showed intermediate FA value, and did not differ significantly from either group. We found a significant increase of MD in the interhemispheric auditory connectivity in the non-AVH group compared to the HC group. The AVH group showed intermediate FA value, and did not differ significantly from either group. Our findings not only supported the hypothesis of self-monitoring account and spontaneous activation account, but also reveled consistent abnormal pattern.