Evaluations of different analyses in resting-state functional connectivity and power spectrum

• Main Authors: Yu-Han Hong, 洪于涵
• Other Authors: Jun-Cheng Weng
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/22026743515403902079

碩士 === 中山醫學大學 === 生物醫學科學學系碩士班 === 101 === Resting-state functional magnetic resonance imaging (rs-fMRI) is recently a major direction in clinical studies, but various kinds of image analyses were used in the previous reports. Therefore, the main purpose of this study was to find a high stable and reliable analysis method in resting state fMRI. Specifically, we tried to compare the power spectrum and region correlation of independent components analysis and seed ROI analysis. The stable and reliable analysis method in resting state fMRI was expected to be widely used in the clinical disease research. In this study, we performed resting-state fMRI in the human brain (n = 18) at 1.5 tesla MRI scanner and in the rat brain (n = 5) at 7.0 tesla MRI scanner. Independent components analysis, MNI seed ROI analysis, and task-based seed ROI analysis were then used in the human brain data; independent components analysis and manual seed ROI analysis were used in the rat brain data, respectively. The functional connectivity between brain regions and the power spectrum were obtained in these various analyses. The advantages and disadvantages of these analyses were discussed, and the results obtained between the human brain and the rat brain were compared and discussed. In human brain, the result of the task-based seed ROI analysis was used as the gold standard and it was compared with MNI seed ROI analysis. We found there was no significant difference between these two analyses. Therefore, we believed the MNI seed ROI analysis was reliable to be performed in large numbers of clinical researches. The result of ICA was more objective, but it could be affected by other functional brain regions classified into the interesting brain networks. Although, MNI seed ROI was more subjective than ICA, the information of the correlations between the brain sub-regions could be obtained. The result found in the rat brain was consistent with the human brain. Our results showed there were advantages and disadvantages in the different analyses of rs-fMRI. We suggested ICA could be first used to find the alteration in unknown brain network in the study. For specific brain network, MNI seed ROI analysis could be performed further. Appropriate image analysis methods will be useful in the clinical disease of both human and animal studies.