A predictor-informed multi-subject bayesian approach for dynamic functional connectivity

HIGHLIGHTS

• What: The authors develop framework where the estimation of dynamic functional networks is by time-varying exogenous physiological covariates that are simultaneously recorded in each during the fMRI experiment. The authors evaluate the performances of the method vs alternative approaches on synthetic data. The authors propose a strategy for edge selection that combines the posterior shrinkage-informed thresholding approach of_[35] with the Bayesian False Discovery Rate controlling method of_[36]. In the application , the authors demonstrate how the model can recover expected change points in dynamic FC states, as those states align quite well with the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.