Detecting Functional Connectivity in fMRI Using PCA and Regression Analysis |
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Authors: | Yuan Zhong Huinan Wang Guangming Lu Zhiqiang Zhang Qing Jiao Yijun Liu |
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Institution: | (1) Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China;(2) Department of Medical Imaging, Nanjing Jinling Hospital, Clinical School of Medical College, Nanjing University, 305 Eastern Zhongshan Road, Nanjing, 210002, China;(3) Departments of Psychiatry and Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, Florida 32610, USA |
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Abstract: | A fMRI connectivity analysis approach combining principal component analysis (PCA) and regression analysis is proposed to
detect functional connectivity between the brain regions. By first using PCA to identify clusters within the vectors of fMRI
time series, more energy and information features in the signal can be maintained than using averaged values from brain regions
of interest. Then, regression analysis can be applied to the extracted principal components in order to further investigate
functional connectivity. Finally, t-test is applied and the patterns with t-values lager than a threshold are considered as functional connectivity mappings. The validity and reliability of the presented
method were demonstrated with both simulated data and human fMRI data obtained during behavioral task and resting state. Compared
to the conventional functional connectivity methods such as average signal based correlation analysis, independent component
analysis (ICA) and PCA, the proposed method achieves competitive performance with greater accuracy and true positive rate
(TPR). Furthermore, the ‘default mode’ and motor network results of resting-state fMRI data indicate that using PCA may improve
upon application of existing regression analysis methods in study of human brain functional connectivity. |
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Keywords: | Functional connectivity Principal component analysis Regression analysis fMRI Resting-state |
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