Computational Methods for Analyzing Functional and Effective Brain Network Connectivity Using fMRI
暂无分享,去创建一个
[1] Rainer Goebel,et al. The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution , 2011, NeuroImage.
[2] Aapo Hyvärinen,et al. New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.
[3] V D Calhoun,et al. Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.
[4] Cornelis J. Stam,et al. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.
[5] Karl J. Friston,et al. Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[6] M. V. D. Heuvel,et al. Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.
[7] Mingzhou Ding,et al. Analyzing information flow in brain networks with nonparametric Granger causality , 2008, NeuroImage.
[8] Karl J. Friston. Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging , 2009, PLoS biology.
[9] G. Cecchi,et al. Scale-free brain functional networks. , 2003, Physical review letters.
[10] J. Xiong,et al. Detecting functional connectivity in the resting brain: a comparison between ICA and CCA. , 2007, Magnetic resonance imaging.
[11] W. Kuo,et al. Increasing fMRI Sampling Rate Improves Granger Causality Estimates , 2014, PloS one.
[12] Klaas E. Stephan,et al. Dynamic causal modelling: A critical review of the biophysical and statistical foundations , 2011, NeuroImage.
[13] Li Yao,et al. Effective connectivity analysis of default mode network based on the Bayesian network learning approach , 2009, Medical Imaging.
[14] Anil K. Seth,et al. A MATLAB toolbox for Granger causal connectivity analysis , 2010, Journal of Neuroscience Methods.
[15] R. Malach,et al. Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems , 2008, Neuropsychologia.
[16] Juan Li,et al. A new dynamic Bayesian network approach for determining effective connectivity from fMRI data , 2013, Neural Computing and Applications.
[17] Dante R Chialvo,et al. Identifying directed links in large scale functional networks: application to brain fMRI , 2007, BMC Cell Biology.
[18] Kaiming Li,et al. Review of methods for functional brain connectivity detection using fMRI , 2009, Comput. Medical Imaging Graph..
[19] S. Ogawa. Brain magnetic resonance imaging with contrast-dependent oxygenation , 1990 .
[20] Jia-Hong Gao,et al. Comparison of TCA and ICA techniques in fMRI data processing , 2004, Journal of magnetic resonance imaging : JMRI.
[21] T. Krauss,et al. Real-space observation of ultraslow light in photonic crystal waveguides. , 2005, Physical review letters.
[22] Karl J. Friston,et al. Analysis of functional MRI time‐series , 1994, Human Brain Mapping.
[23] 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.
[24] Juan Zhou,et al. Learning effective brain connectivity with dynamic Bayesian networks , 2007, NeuroImage.
[25] Santanu Chaudhury,et al. Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data , 2017, Journal of Neuroscience Methods.
[26] William D. Penny,et al. Comparing Dynamic Causal Models using AIC, BIC and Free Energy , 2012, NeuroImage.
[27] Mark W. Woolrich,et al. The danger of systematic bias in group-level FMRI-lag-based causality estimation , 2012, NeuroImage.
[28] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[29] Mingzhou Ding,et al. Is Granger Causality a Viable Technique for Analyzing fMRI Data? , 2013, PloS one.
[30] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[31] 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.
[32] M. Fox,et al. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.
[33] Carl D. Hacker,et al. Clustering of Resting State Networks , 2012, PloS one.
[34] Karl J. Friston,et al. Statistical parametric maps in functional imaging: A general linear approach , 1994 .
[35] R Baumgartner,et al. Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. , 2000, Magnetic resonance imaging.
[36] Vinod Menon,et al. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[37] E. DeYoe,et al. Analysis and use of FMRI response delays , 2001, Human brain mapping.
[38] Seong-Gi Kim,et al. Relative changes of cerebral arterial and venous blood volumes during increased cerebral blood flow: Implications for BOLD fMRI , 2001, Magnetic resonance in medicine.
[39] Stephen M. Smith,et al. Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.
[40] Qiang Ji,et al. Knowledge Based Activity Recognition with Dynamic Bayesian Network , 2010, ECCV.
[41] M. P. van den Heuvel,et al. Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.
[42] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[43] Jonathan D. Power,et al. The Development of Human Functional Brain Networks , 2010, Neuron.
[44] Karl J. Friston,et al. Network discovery with DCM , 2011, NeuroImage.
[45] Alan C. Evans,et al. Multi-level bootstrap analysis of stable clusters in resting-state fMRI , 2009, NeuroImage.
[46] A. Seth,et al. Granger Causality Analysis in Neuroscience and Neuroimaging , 2015, The Journal of Neuroscience.
[47] C. Granger. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .
[48] F. Gonzalez-Lima,et al. Structural equation modeling and its application to network analysis in functional brain imaging , 1994 .
[49] Jonathan D. Power,et al. Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..
[50] Rainer Goebel,et al. Spatial independent component analysis of functional MRI time‐series: To what extent do results depend on the algorithm used? , 2002, Human brain mapping.
[51] Alan C. Evans,et al. Comparing functional connectivity via thresholding correlations and singular value decomposition , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[52] Marina Vannucci,et al. Bayesian models for functional magnetic resonance imaging data analysis , 2014 .
[53] Russell A. Poldrack,et al. Six problems for causal inference from fMRI , 2010, NeuroImage.
[54] Babak Sohrabi,et al. A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system , 2011, Neural Computing and Applications.
[55] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[56] D. Tank,et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[57] Mohammad Hossein Fazel Zarandi,et al. Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning , 2018, Math. Comput. Simul..
[58] K. Worsley,et al. The geometry of correlation fields with an application to functional connectivity of the brain , 1999 .
[59] E. Bullmore,et al. Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.
[60] Karl J. Friston,et al. Analysing connectivity with Granger causality and dynamic causal modelling , 2013, Current Opinion in Neurobiology.
[61] Karl J. Friston. Functional and Effective Connectivity: A Review , 2011, Brain Connect..
[62] O. Sporns,et al. Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.
[63] Karl J. Friston. Functional and effective connectivity in neuroimaging: A synthesis , 1994 .
[64] Andrea Mechelli,et al. A report of the functional connectivity workshop, Dusseldorf 2002 , 2003, NeuroImage.
[65] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[66] Adem Karahoca,et al. An early warning system approach for the identification of currency crises with data mining techniques , 2012, Neural Computing and Applications.
[67] Adriana Galván,et al. The use of functional and effective connectivity techniques to understand the developing brain , 2015, Developmental Cognitive Neuroscience.
[68] Pierre Grammond,et al. Geographical Variations in Sex Ratio Trends over Time in Multiple Sclerosis , 2012, PloS one.
[69] Dietmar Cordes,et al. Hierarchical clustering to measure connectivity in fMRI resting-state data. , 2002, Magnetic resonance imaging.
[70] 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.
[71] Steven L. Bressler,et al. Wiener–Granger Causality: A well established methodology , 2011, NeuroImage.
[72] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[73] Karl J. Friston,et al. Ten simple rules for dynamic causal modeling , 2010, NeuroImage.
[74] Guillermo A Cecchi,et al. BMC Cell Biology , 2022 .
[75] Lee M. Miller,et al. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data , 2004, NeuroImage.