On the pros and cons of using temporal derivatives to assess brain functional connectivity
暂无分享,去创建一个
Dante R. Chialvo | Wojciech Tarnowski | Jeremi K. Ochab | Jeremi K. Ochab | Maciej A. Nowak | M. Nowak | D. Chialvo | W. Tarnowski
[1] Maciej A. Nowak,et al. A random matrix approach to VARMA processes , 2010 .
[2] William H. Thompson,et al. A common framework for the problem of deriving estimates of dynamic functional brain connectivity , 2017, NeuroImage.
[3] Hao He,et al. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia , 2015, NeuroImage.
[4] Peter Fransson,et al. Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity , 2016, Scientific Reports.
[5] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[6] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[7] Richard A. Davis,et al. Introduction to time series and forecasting , 1998 .
[8] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[9] J. Duyn,et al. Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.
[10] Russell A. Poldrack,et al. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.
[11] R. Buxton,et al. Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.
[12] Catie Chang,et al. Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.
[13] G. Livan,et al. Asymmetric correlation matrices: an analysis of financial data , 2012, 1201.6535.
[14] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[15] M. Nowak,et al. Spectra of large time-lagged correlation matrices from random matrix theory , 2016, 1612.06552.
[16] Dante R Chialvo,et al. Brain organization into resting state networks emerges at criticality on a model of the human connectome. , 2012, Physical review letters.
[17] Ravi S. Menon,et al. Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.
[18] Alan J. Laub,et al. Matrix analysis - for scientists and engineers , 2004 .
[19] Stephen M. Smith,et al. Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.
[20] O. Sporns,et al. Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.
[21] Laura C. Buchanan,et al. The spatial structure of resting state connectivity stability on the scale of minutes , 2014, Front. Neurosci..
[22] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[23] Craig G. Richter,et al. A simulation and comparison of dynamic functional connectivity methods , 2017, bioRxiv.
[24] Dimitri Van De Ville,et al. The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.
[25] Evan M. Gordon,et al. On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.