Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals
暂无分享,去创建一个
Shang-Yueh Tsai | Fa-Hsuan Lin | Yi-Cheng Hsu | Kevin Wen-Kai Tsai | Wen-Jui Kuo | Ying-Hua Chu | Jo-Fu Lotus Lin | W. Kuo | F. Lin | K. W. Tsai | Ying-Hua Chu | S. Tsai | Yi-Cheng Hsu
[1] Jürgen Hennig,et al. Tracking dynamic resting-state networks at higher frequencies using MR-encephalography , 2013, NeuroImage.
[2] E. Bullmore,et al. A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.
[3] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[4] Xiaoping Hu,et al. Effect of hemodynamic variability on Granger causality analysis of fMRI , 2010, NeuroImage.
[5] R. Goebel,et al. Investigating directed influences between activated brain areas in a motor-response task using fMRI. , 2006, Magnetic resonance imaging.
[6] Stephen M. Smith,et al. Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[7] Bharat B. Biswal,et al. The oscillating brain: Complex and reliable , 2010, NeuroImage.
[8] J. Geweke,et al. Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .
[9] Mingzhou Ding,et al. Analyzing information flow in brain networks with nonparametric Granger causality , 2008, NeuroImage.
[10] Arnold Neumaier,et al. Estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.
[11] Edward T. Bullmore,et al. A simple view of the brain through a frequency-specific functional connectivity measure , 2008, NeuroImage.
[12] Anders M. Dale,et al. Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.
[13] Aki Vehtari,et al. Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER , 2012, NeuroImage.
[14] B. Rosen,et al. fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG , 2013, NeuroImage.
[15] C. Granger. Investigating causal relations by econometric models and cross-spectral methods , 1969 .
[16] N. Logothetis,et al. Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.
[17] Karl J. Friston. Functional and Effective Connectivity: A Review , 2011, Brain Connect..
[18] Alexis T Baria,et al. Anatomical and Functional Assemblies of Brain BOLD Oscillations , 2011, The Journal of Neuroscience.
[19] Stephen M. Smith,et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.
[20] W. Kuo,et al. Increasing fMRI Sampling Rate Improves Granger Causality Estimates , 2014, PloS one.
[21] P. Barbaresi,et al. Laminar pattern of termination of the ipsilateral cortical projection from SII to SI in cats , 1995, The Journal of comparative neurology.
[22] C. Segebarth,et al. Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation , 2008, PLoS biology.
[23] B. Rosen,et al. Functional mapping of the human visual cortex by magnetic resonance imaging. , 1991, Science.
[24] Gary H. Glover,et al. BOLD fractional contribution to resting-state functional connectivity above 0.1Hz , 2015, NeuroImage.
[25] Matti S Hämäläinen,et al. Dynamic magnetic resonance inverse imaging of human brain function , 2006, Magnetic resonance in medicine.
[26] R. Knight,et al. The functional role of cross-frequency coupling , 2010, Trends in Cognitive Sciences.
[27] C. Granger. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .
[28] Catie Chang,et al. Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.
[29] Thomas E. Nichols,et al. Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.
[30] Anders M. Dale,et al. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.
[31] Tapio Seppänen,et al. BOLD-contrast functional MRI signal changes related to intermittent rhythmic delta activity in EEG during voluntary hyperventilation—simultaneous EEG and fMRI study , 2004, NeuroImage.
[32] Michael Eichler,et al. A graphical approach for evaluating effective connectivity in neural systems , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[33] Rainer Goebel,et al. Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.
[34] M. D’Esposito,et al. A comparison of Granger causality and coherency in fMRI‐based analysis of the motor system , 2009, Human brain mapping.
[35] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[36] Karl J. Friston. Dynamic causal modeling and Granger causality Comments on: The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution , 2011, NeuroImage.
[37] Klaas E. Stephan,et al. A short history of causal modeling of fMRI data , 2012, NeuroImage.
[38] R. P. McDonald,et al. Some algebraic properties of the Reticular Action Model for moment structures. , 1984, The British journal of mathematical and statistical psychology.
[39] Bharat B. Biswal,et al. Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging , 2014, NeuroImage.
[40] Jonathan R. Polimeni,et al. Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging , 2013, NeuroImage.
[41] Markus Barth,et al. Generalized iNverse imaging (GIN): Ultrafast fMRI with physiological noise correction , 2013, Magnetic resonance in medicine.
[42] R. Weisskoff,et al. Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel‐level false‐positive rates in fMRI , 1998, Human brain mapping.
[43] Rainer Goebel,et al. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.
[44] N. Shah,et al. The Default Mode Network and EEG Regional Spectral Power: A Simultaneous fMRI-EEG Study , 2014, PloS one.
[45] A. Dale,et al. Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.
[46] A. Dale,et al. High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.
[47] João Ricardo Sato,et al. A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality , 2006, NeuroImage.
[48] Hanbing Lu,et al. Low- but Not High-Frequency LFP Correlates with Spontaneous BOLD Fluctuations in Rat Whisker Barrel Cortex. , 2014, Cerebral cortex.
[49] R. Turner,et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[50] Mark W. Woolrich,et al. The danger of systematic bias in group-level FMRI-lag-based causality estimation , 2012, NeuroImage.
[51] S. Bressler,et al. Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[52] D G Pelli,et al. The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.
[53] Karl J. Friston,et al. Comparing dynamic causal models , 2004, NeuroImage.
[54] Dietrich Lehmann,et al. Assessing direct paths of intracortical causal information flow of oscillatory activity with the isolated effective coherence (iCoh) , 2014, Front. Hum. Neurosci..
[55] James Theiler,et al. Testing for nonlinearity in time series: the method of surrogate data , 1992 .
[56] J. Duyn,et al. Investigation of Low Frequency Drift in fMRI Signal , 1999, NeuroImage.
[57] Lawrence L. Wald,et al. Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing , 2008, NeuroImage.
[58] Oliver Speck,et al. MR-Encephalography: Fast multi-channel monitoring of brain physiology with magnetic resonance , 2007, NeuroImage.
[59] Ravi S. Menon,et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[60] Peter A. Bandettini,et al. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.
[61] Demis Basso,et al. A new method for detecting causality in fMRI data of cognitive processing , 2006, Cognitive Processing.
[62] Adrian T. Lee,et al. Discrimination of Large Venous Vessels in Time‐Course Spiral Blood‐Oxygen‐Level‐Dependent Magnetic‐Resonance Functional Neuroimaging , 1995, Magnetic resonance in medicine.
[63] S. Petersen,et al. Characterizing the Hemodynamic Response: Effects of Presentation Rate, Sampling Procedure, and the Possibility of Ordering Brain Activity Based on Relative Timing , 2000, NeuroImage.
[64] Arnold Neumaier,et al. Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.