Advancing functional connectivity research from association to causation
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Daniele Marinazzo | Michael W. Cole | Stephen José Hanson | Russell A Poldrack | Vince Calhoun | Michael W Cole | Ruben Sanchez-Romero | Bharat B Biswal | Lucina Q Uddin | Andrew T Reid | Drew B Headley | Ravi D Mill | Daniel J Lurie | Pedro A Valdés-Sosa | Drew B. Headley | S. Hanson | V. Calhoun | R. Poldrack | B. Biswal | L. Uddin | Daniele Marinazzo | P. Valdés-Sosa | A. Reid | R. Mill | Ruben Sanchez-Romero | Daniel J. Lurie
[1] Alfred Korzybski,et al. Science and sanity : an introduction to non-aristotelian systems and general semantics / Alfred Korzybski , 1942 .
[2] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.
[3] R. Lewontin,et al. The Genetic Basis of Evolutionary Change , 2022 .
[4] J. Crow. The genetic basis of evolutionary change , 1975 .
[5] J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .
[6] Judea Pearl,et al. The recovery of causal poly-trees from statistical data , 1987, Int. J. Approx. Reason..
[7] David Klahr,et al. Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..
[8] M K Habib,et al. Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.
[9] P. Spirtes,et al. Causation, prediction, and search , 1993 .
[10] Judea Pearl,et al. A Probabilistic Calculus of Actions , 1994, UAI.
[11] Karl J. Friston. Functional and effective connectivity in neuroimaging: A synthesis , 1994 .
[12] Lidia Fuentes,et al. GENESIS: An Object-Oriented Framework for Simulation of Neural Network Models , 1995, ICANNGA.
[13] Karl J. Friston,et al. Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.
[14] Nicholas T. Carnevale,et al. The NEURON Simulation Environment , 1997, Neural Computation.
[15] R. Buxton,et al. Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.
[16] J. Pearl,et al. Confounding and Collapsibility in Causal Inference , 1999 .
[17] David Hume,et al. The Clarendon Edition of the Works of David Hume: An Enquiry concerning Human Understanding , 2000 .
[18] S. Petersen,et al. Memory's echo: vivid remembering reactivates sensory-specific cortex. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[19] P. Bandettini,et al. Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response , 2001, NeuroImage.
[20] N. Logothetis. The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[21] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[22] Barry Horwitz,et al. The elusive concept of brain connectivity , 2003, NeuroImage.
[23] Karl J. Friston,et al. Evaluation of different measures of functional connectivity using a neural mass model , 2004, NeuroImage.
[24] Mark D'Esposito,et al. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.
[25] V. D. Calhoun,et al. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms , 2004, NeuroImage.
[26] N. Logothetis,et al. Neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging , 2004 .
[27] Simon B. Eickhoff,et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.
[28] Rainer Goebel,et al. Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.
[29] Régine Le Bouquin-Jeannès,et al. Linear and nonlinear causality between signals: methods, examples and neurophysiological applications , 2006, Biological Cybernetics.
[30] N. Westerhof,et al. Heterogeneity and prediction of hemodynamic responses to dobutamine in patients with septic shock , 2006, Critical care medicine.
[31] Jing Yu,et al. Computational Inference of Neural Information Flow Networks , 2006, PLoS Comput. Biol..
[32] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[33] Olaf Sporns,et al. Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.
[34] C. Segebarth,et al. Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation , 2008, PLoS biology.
[35] Romain Brette,et al. Brian: A Simulator for Spiking Neural Networks in Python , 2008, Frontiers Neuroinformatics.
[36] Karl J. Friston,et al. The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..
[37] Stephen M Smith,et al. Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.
[38] J. Schoffelen,et al. Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.
[39] Russell A. Poldrack,et al. Six problems for causal inference from fMRI , 2010, NeuroImage.
[40] J. Bennett,et al. Enquiry Concerning Human Understanding , 2010 .
[41] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[42] Karl J. Friston. Functional and Effective Connectivity: A Review , 2011, Brain Connect..
[43] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[44] Gabriel A. Silva,et al. A Framework for Simulating and Estimating the State and Functional Topology of Complex Dynamic Geometric Networks , 2009, Neural Computation.
[45] Timothy O. Laumann,et al. Functional Network Organization of the Human Brain , 2011, Neuron.
[46] Clark Glymour,et al. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study , 2011, NeuroImage.
[47] Karl J. Friston,et al. Effective connectivity: Influence, causality and biophysical modeling , 2011, NeuroImage.
[48] Michael W. Cole,et al. Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.
[49] Gabriele Lohmann,et al. Critical comments on dynamic causal modelling , 2012, NeuroImage.
[50] Jon Williamson,et al. What is a mechanism? Thinking about mechanisms across the sciences , 2012 .
[51] Stephen M. Smith,et al. The future of FMRI connectivity , 2012, NeuroImage.
[52] Mark A. Elliott,et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.
[53] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[54] C. Koch,et al. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.
[55] Mert R. Sabuncu,et al. The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.
[56] Jin Hyung Lee,et al. Informing brain connectivity with optogenetic functional magnetic resonance imaging , 2012, NeuroImage.
[57] R Cameron Craddock,et al. A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.
[58] Karl J. Friston,et al. Analysing connectivity with Granger causality and dynamic causal modelling , 2013, Current Opinion in Neurobiology.
[59] Viktor K. Jirsa,et al. The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging , 2013, Brain Connect..
[60] David A. Leopold,et al. Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.
[61] B. Biswal,et al. Calibrating BOLD fMRI activations with neurovascular and anatomical constraints. , 2013, Cerebral cortex.
[62] Jonathan D. Power,et al. Control-related systems in the human brain , 2013, Current Opinion in Neurobiology.
[63] Joseph Ramsey,et al. Bayesian networks for fMRI: A primer , 2014, NeuroImage.
[64] Ludovica Griffanti,et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.
[65] V. Calhoun,et al. A Robust Classifier to Distinguish Noise from fMRI Independent Components , 2014, PloS one.
[66] Timothy O. Laumann,et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.
[67] Anil K. Seth,et al. The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference , 2014, Journal of Neuroscience Methods.
[68] Karl J. Friston,et al. A systematic framework for functional connectivity measures , 2014, Front. Neurosci..
[69] Tobias C. Potjans,et al. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model , 2012, Cerebral cortex.
[70] Jonathan D. Power,et al. Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.
[71] Thomas E. Nichols,et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.
[72] Vince D. Calhoun,et al. Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data , 2015, IEEE Transactions on Biomedical Engineering.
[73] Kaustubh Supekar,et al. Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions , 2016, NeuroImage.
[74] Michael W. Cole,et al. Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.
[75] Hao He,et al. Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches , 2016, Human brain mapping.
[76] John R. Anderson,et al. Learning Problem-Solving Rules as Search Through a Hypothesis Space , 2016, Cogn. Sci..
[77] J. Pearl,et al. Causal Inference in Statistics: A Primer , 2016 .
[78] Clark Glymour,et al. A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images , 2016, International Journal of Data Science and Analytics.
[79] Trygve B. Leergaard,et al. 3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas , 2016, Front. Neuroanat..
[80] Michael W. Cole,et al. Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration , 2016, The Journal of Neuroscience.
[81] Michael W. Cole,et al. Functional connectivity change as shared signal dynamics , 2016, Journal of Neuroscience Methods.
[82] Sergey M. Plis,et al. Causal Discovery from Subsampled Time Series Data by Constraint Optimization , 2016, Probabilistic Graphical Models.
[83] M. D’Esposito,et al. Causal evidence for lateral prefrontal cortex dynamics supporting cognitive control , 2017, bioRxiv.
[84] S. Rotter,et al. From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance , 2017, 1708.02423.
[85] G. Deshpande,et al. Experimental Validation of Dynamic Granger Causality for Inferring Stimulus-Evoked Sub-100 ms Timing Differences from fMRI , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[86] Walter Schneider,et al. Empirical validation of directed functional connectivity , 2017, NeuroImage.
[87] Michael W. Cole,et al. From connectome to cognition: The search for mechanism in human functional brain networks , 2017, NeuroImage.
[88] Grant R. Gordon,et al. Impaired neurovascular coupling in aging and Alzheimer's disease: Contribution of astrocyte dysfunction and endothelial impairment to cognitive decline , 2017, Experimental Gerontology.
[89] Daniele Marinazzo,et al. Hemodynamic response function (HRF) variability confounds resting‐state fMRI functional connectivity , 2018, Magnetic resonance in medicine.
[90] Joachim M. Buhmann,et al. A generative model of whole-brain effective connectivity , 2018, NeuroImage.
[91] Stefan Rotter,et al. From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals , 2018, PLoS Comput. Biol..
[92] Konrad Paul Kording,et al. The lure of causal statements: Rampant mis-inference of causality in estimated connectivity , 2018, 1812.03363.
[93] Stephen M. Smith,et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.
[94] J. Pearl,et al. The Book of Why: The New Science of Cause and Effect , 2018 .
[95] Konrad Paul Kording,et al. Quasi-experimental causality in neuroscience and behavioural research , 2018, Nature Human Behaviour.
[96] Michael W. Cole,et al. Task activations produce spurious but systematic inflation of task functional connectivity estimates , 2018, NeuroImage.
[97] Clark Glymour,et al. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods , 2019, Network Neuroscience.
[98] L. Goldberg. The Book of Why: The New Science of Cause and Effect† , 2019, Quantitative Finance.