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Study of brain networks in patients with stress disorder after trauma based on magnetic resonance

Author: XuQiang
Tutor: ChenHuaFu
School: University of Electronic Science and Technology
Course: Biomedical Engineering
Keywords: Post-traumatic stress disorder functional connectivity network structuralconnectivity network effective connectivity network graph analysis
CLC: R749.5
Type: Master's thesis
Year: 2012
Downloads: 133
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The understanding of human brain function can be dominated by these two kinds ofideas: functional segregation and functional integration, which can also be used to understandthe pathophysiological mechanism of brain mental and nervous disorder. The functionalsegregation refers that the specific region or neural cluter of brain takes charge the specificfunction; while the functional intergration refers that everal activity of human being needs thecooperation of some different brain regions. These two distinct principles support each other.The large-scale network analysis techonology, the newest one of the functional interationanalysis techonology, has been applied to lots of brain research, especially to the research ofbrain mental and nervous disorder. However, it has not been used to the study of such amental disorder, Post-traumatic stress disorder(short for PTSD).Depending on the image datas scaned by the Magnetic resonance imaging techonology,including rest-state functional magenetic resonance imaging datas, diffusion tensor imagingdatas and structural magenetic resonance datas, the current work developed some newtechonologies for network connection, and built three types of brain networks, which are thenetworks based on functional connectivity, structural conncetivity and effective connectivity,then explored the pathophysiological mechanism of PTSD. Four aspects of this dissertationhave been put forward:1. We developed a new deconvolution method of functional MRI signals, which did notneed the prior knowleged, then built the neural-level effective network and compared to theBOLD-level effective networks. The result showed that the signal after deconvolution may bemore robust to the noise interference, and the small-world attribution of neural-level networkswere better than the BOLD-level networks.2. We explored the pathophysiological mechanism of PTSD by using the large-scalefunctional network analysis techonology. The result showed that the strength of network inPTSD patients was stronger than the one in the normal controls. Besides, the different regionsin the network between the PTSD patients and normal controls spreaded over the whole brainwidely, not only in emotion circle which was reported in other researches. We conjectured thatPTSD was a widespread dysfunctional disorder. 3. After the functional network analysis, we performed a large-scale structural networkanalysis on the PTSD. As we known that the structural connecitivity was the basis of thefunctional connectivity. In the part2of current work, we observed the differences in thefunctional network of PTSD, so in this part, we would like to detect the structural differencesof PTSD by using the large-scale structural network analysis techonology. The tractographytechonology of diffusion tensor imaging(DTI) was used during the building of the sturcturalnetwork. The results told us that the shortest path length of the PTSD patients’ network wasshorter than the normal controls’, which performed the trend of randomzation for patients. Thenodals, which were abnorm in patients, were almost in the emotional circle of the brain. Thedifferences between functional networks and structural networks were thought to be causedby the fact that the structue was more stabilized than the function.4. The large-scale functional network analysis techonology and the effective networkanalysis techonology were used to study the repairing mechanism of PTSD. We scaned thePTSD patients group and the two-year followup group. The results of the functional networksshowed that attributions of the the followup group were among the PTSD group and thenormal controls. It meant that the repairing process of brain might be a process of informationprocessing mechanism optimization. The abnorm regions of patients were in these rest-statenetworks, the default mode network, the sensorimotor network and the core network. Theresults of effective networks supported the results of the former functional networks, and itsuggested that the differences between PTSD and normal controls might be caused by theabnorm of the in-degree networks.

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