Thalamus Segmentation Based On Resting State Functional MRI

During the resting-state experiment, volunteers were instructed to rest with eyes closed, not to think of anything in particular, and not to fall asleep during data acquisition. Resting-state functional connectivity examines spatial synchronization of spontaneous neuronal activity, reflecting a level of ongoing functional connectivity between brain regions during rest. In the recent years, the development of resting-state functional magnetic resonance imaging(fMRI) technique becomes one of the most popular neuroimaging tools to examine functional interactions between brain regions noninvasively, as well as provides new insight into the development of brain science. The thalamus acts as the central relay station of the brain with nearly all of the sensory tract projections reach to the cortex passing through the thalamus. In this thesis, the thalamus was segmented based on the connected regions of each voxel, which was obtained by the analysis of resting state fMRI data.The image data were acquired on a 3T Siemens Trio MRI scanner. Thirty healthy participants were recruited in this study, and were provided full informed consent before experiment. The thalamus segmentation approach proposed in this thesis was based on k-means clustering algorithm, and the functional connectivity of thirty subjects’ resting state fMRI data was analyzed. The approach can be divided into five cascaded steps:(1) all the resting-state fMRI data were preprocessed;(2) each voxel of thalamus was defined as the seed point individually to obtain the temporal cross-correlations with other voxels in the whole brain;(3) a one-sample t-test was performed among the functional connectivity maps across randomly selected twenty-five participants.(4) the results were mapped to the AAL template to get the segmentation matrix.(5) clustering algorithm was applied to segment the data, and the thalamus segmentation results were presented.Innovation points of this thesis included two parts:(1) the computational cost was significantly reduced by applying clustering algorithm in the thalamus segmentation.(2) according to the segmentation results, the connected regions of each cluster were analyzed. The thalamus was segmented into seven parts in this thesis. The segmentation results can be used as a template for localizing areas of the thalamus that show functional alterations with diseases, such as minimal hepatic encephalopathy, Parkinson’s disease, and Schizophrenia, and provide valuable reference for prediction, diagnosis and treatment of the related diseases.