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Abstract #5561

Intra- and inter-scanner reliability of scaled subprofile model of principal component analysis in resting-state fMRI

Li-Xia Yuan1, Jian-Bao Wang2, Na Zhao2, Yuan-Yuan Li2, Dong-Qiang Liu3, Hong-Jian He1, Jian-Hui Zhong1, Yi-Long Ma4, and Yu-Feng Zang2

1Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China, 2Center for Cognition and Brain Disorders and the Affiliated Hospital,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China, 3Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Liaoning, China, 4Center for Neurosciences, the Feinstein Institute of Medical Research, Manhasset, NY, United States

Scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate statistical method, widely used in positron emission tomography (PET). Recently, SSM-PCA has been applied to resting-state functional MRI (RS-fMRI). However, the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI is not investigated systematically yet. Results from eyes-open (EO) and eyes-closed (EC) dataset demonstrate that both the intra- and inter-scanner reliability is excellent for EO and EC related covariance pattern (EOEC-pattern) and fair to good for EOEC-pattern’s expression. Moreover, SSM-PCA and conventional T-test are complementary for neuroimaging researches. This study illustrates the great potential of SSM-PCA for further applications in RS-fMRI.

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