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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