Functional Covariance Networks: Obtaining Resting-State Networks from Intersubject Variability

@article{Taylor2012FunctionalCN,
  title={Functional Covariance Networks: Obtaining Resting-State Networks from Intersubject Variability},
  author={Paul A. Taylor and Suril Gohel and Xin Di and Martin Walter and Bharat B. Biswal},
  journal={Brain connectivity},
  year={2012},
  volume={2 4},
  pages={
          203-17
        }
}
In this study, we investigate a new approach for examining the separation of the brain into resting-state networks (RSNs) on a group level using resting-state parameters (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [fALFF], the Hurst exponent, and signal standard deviation). Spatial independent component analysis is used to reveal covariance patterns of the relevant resting-state parameters (not the time series) across subjects that are shown to be related to known, standard… 

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