Motion Correction for fMRI data using Conditional Transition Regime Switching General Autoregressive Conditional Heteroskedasticity Models

[1]  Gustavo Deco,et al.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? , 2016, NeuroImage.

[2]  T. Wan Structural Equation Models with Latent Variables , 2002 .

[3]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[4]  Chang‐Jin Kim,et al.  State-Space Models with Regime-Switching: Classical and Gibbs Sampling Approaches with Applications , 1999 .

[5]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[6]  James D. Hamilton A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[7]  Michael P Milham,et al.  Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. , 2012, The American journal of psychiatry.

[8]  Vince D. Calhoun,et al.  Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis , 2014, NeuroImage.

[9]  R. Engle Dynamic Conditional Correlation , 2002 .

[10]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[11]  James D. Hamilton Analysis of time series subject to changes in regime , 1990 .

[12]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[13]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[14]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[15]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[16]  Bronwyn H Hall,et al.  Estimation and Inference in Nonlinear Structural Models , 1974 .

[17]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[18]  Edward T. Bullmore,et al.  Change detection in children with autism: An auditory event-related fMRI study , 2006, NeuroImage.

[19]  Martin A. Lindquist,et al.  Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach , 2014, NeuroImage.

[20]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.