The fluctuation introduced by head motion, cardiac and respiratory fluctuations and other noise sources considerably confounds the interpretation of resting-state fMRI data. These noise fluctuations widely spread the whole brain regardless of the kinds of brain tissues, however, neural activity is more likely limited to gray matter tissue. Considering that the contribution of neural activity varies in different brain tissues, we hypothesized that disentangling gray matter and non-gray matter time series can clean fMRI data and improve the data quality. With such a hypothesis, we proposed a deep neural network method to denoise resting state fMRI data.
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