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Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study

Joseph D. Ramsey, Stephen José Hanson, Clark Glymour
2011 NeuroImage  
We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater  ...  Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions.  ...  Smith et al. test 38 search methods, including five " Please cite this article as: Ramsey, J.D., et al., Multi-subject search correctly identifies causal connections and most causal directions in  ... 
doi:10.1016/j.neuroimage.2011.06.068 pmid:21745580 fatcat:2bji3guzpjbzxb67eax6tm5fde

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

Natalia Z. Bielczyk, Sebo Uithol, Tim van Mourik, Paul Anderson, Jeffrey C. Glennon, Jan K. Buitelaar
2018 Network Neuroscience  
We finish with formulating some recommendations for the future directions in this area.  ...  In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau  ...  Johannes Wilbertz for sharing knowledge about causal inference in fMRI, and for providing a valuable content.  ... 
doi:10.1162/netn_a_00062 pmid:30793082 pmcid:PMC6370462 fatcat:byhtpa3pq5dxpfmpvbwtcmctny

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Rahul Biswas, Eli Shlizerman
2022 Frontiers in Systems Neuroscience  
In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature.  ...  We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed  ...  AUTHOR CONTRIBUTIONS RB and ES initiated the study and developed the methods, verified the results, and wrote and edited the manuscript. RB implemented the methods and performed comparative studies.  ... 
doi:10.3389/fnsys.2022.817962 pmid:35308566 pmcid:PMC8924489 fatcat:ru44xu4nzvgbdngykgtngsqcye

Empirical validation of directed functional connectivity

Ravi D. Mill, Anto Bagic, Andreea Bostan, Walter Schneider, Michael W. Cole
2017 NeuroImage  
These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.  ...  However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or National Institutes of Health.  ... 
doi:10.1016/j.neuroimage.2016.11.037 pmid:27856312 pmcid:PMC5321749 fatcat:l56jxk6g5fesbb4cczzzw7b3au

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches [article]

Natalia Z. Bielczyk, Sebo Uithol, Tim van Mourik, Paul Anderson, Jeffrey C. Glennon, Jan K. Buitelaar
2019 arXiv   pre-print
We finish with formulating some recommendations for the future directions in this area.  ...  In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling  ...  As indicated in the computational study by Smith et al. [179] , and also in a recent study by Bielczyk et al.  ... 
arXiv:1708.04020v4 fatcat:r4jdjsl4qzdkriawdmuss2s7cq

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

Nan Xu, R. Nathan Spreng, Peter C. Doerschuk
2017 Frontiers in Neuroscience  
This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.  ...  These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions  ...  Smith (University of Oxford) for providing simulation data and his software for applying Patel's conditional dependence measures and network measurements as described in his paper (Smith et al., 2011)  ... 
doi:10.3389/fnins.2017.00271 pmid:28559793 pmcid:PMC5433247 fatcat:escllyqoivcihnjoju6wbfvpy4

Initial validation for the estimation of resting-state fMRI effective connectivity by a generalization of the correlation approach [article]

Nan Xu, R. Nathan Spreng, Peter C. Doerschuk
2017 arXiv   pre-print
This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.  ...  These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions  ...  In addition to the algorithms used in Smith et al. (2011) , which estimate the directional connectivity for single subject data sets, the IMaGES (Ramsey et al., 2010 (Ramsey et al., , 2011) ) and GIMME  ... 
arXiv:1603.03815v3 fatcat:yo3fssvt25hbln4woaroe6d4vm

Network modelling methods for FMRI

Stephen M. Smith, Karla L. Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, Mark W. Woolrich
2011 NeuroImage  
In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity  ...  This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated  ...  ), Anil Seth for providing the Causal Connectivity Analysis toolbox (and for helpful discussions), Shohei Shimizu, Patrik Hoyer and Aapo Hyvärinen for providing LiNGAM/FastICA (and for helpful discussions  ... 
doi:10.1016/j.neuroimage.2010.08.063 pmid:20817103 fatcat:4odksna7gbc7bmxrvuirx3ygzi

Directed functional connectivity using dynamic graphical models

Simon Schwab, Ruth Harbord, Valerio Zerbi, Lloyd Elliott, Soroosh Afyouni, Jim Q. Smith, Mark W. Woolrich, Stephen M. Smith, Thomas E. Nichols
2018 NeuroImage  
In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction.  ...  This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity.  ...  Acknowledgements We thank Karl Friston and Adeel Razi for their expert advice and an in-depth discussion of this work.  ... 
doi:10.1016/j.neuroimage.2018.03.074 pmid:29625233 pmcid:PMC6153304 fatcat:fmebg36anjcwrpfshcdqgs25xa

Empirical validation of directed functional connectivity [article]

Ravi D Mill, Anto Bagic, Walter Schneider, Michael W Cole
2016 bioRxiv   pre-print
These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.  ...  However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via 'ground truth' connectivity patterns embedded in simulated  ...  --Herrero et al., 2008) or DCM (Bonstrup et al., 2016) , rather than the more comprehensive multi--algorithm validations undertaken by the Smith and Wang simulations.  ... 
doi:10.1101/070979 fatcat:2pxqvzyyjzfklg6zsqxlj57jtu

Ten simple rules for dynamic causal modeling

K.E. Stephan, W.D. Penny, R.J. Moran, H.E.M. den Ouden, J. Daunizeau, K.J. Friston
2010 NeuroImage  
DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data.  ...  Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity.  ...  We are very grateful to our fellow DCM developers in the FIL methods group, particularly Lee Harrison for his comments on the manuscript and the shaping discussions on DCM we have had with him over many  ... 
doi:10.1016/j.neuroimage.2009.11.015 pmid:19914382 pmcid:PMC2825373 fatcat:nhftacuv5zfstog4xeqpo6it5m

A systematic framework for functional connectivity measures

Huifang E. Wang, Christian G. Bénar, Pascale P. Quilichini, Karl J. Friston, Viktor K. Jirsa, Christophe Bernard
2014 Frontiers in Neuroscience  
We then evaluated the performance of the methods on data simulated with different types of models.  ...  In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable  ...  This work was supported by grants from the Fondation pour la Recherche Médicale en France FRM: ING20121226265, and the French National Research Agency (ANR MUSIC, MINOS).  ... 
doi:10.3389/fnins.2014.00405 pmid:25538556 pmcid:PMC4260483 fatcat:ehfzt5xymrhrbhcr4qmypkfhhm

Effective connectivity among the working memory regions during preparation for and during performance of the n-back task

Anna Manelis, Lynne M. Reder
2014 Frontiers in Human Neuroscience  
In that study, subjects performed 1-, 2-, and 3-back tasks. Each block of n-back was preceded by a preparation period and followed by a rest period.  ...  The analyses of task-related brain activity identified a network of 18 regions that increased in activation from 1-to 3-back (Increase network) and a network of 17 regions that decreased in activation  ...  ACKNOWLEDGMENTS This work was supported by a National Institute of Mental Health training grant T32MH019983. The authors declare no competing financial interests.  ... 
doi:10.3389/fnhum.2014.00593 pmid:25140143 pmcid:PMC4122182 fatcat:y2z5w7sxzbewlaxk6lpq5kx4mu

A Common Architecture for Human and Artificial Cognition Explains Brain Activity Across Domains [article]

Andrea Stocco, Zoe Steine-Hanson, Natalie Koh, John Laird, Christian Lebiere, Paul Rosenbloom
2019 bioRxiv   pre-print
After the model was implemented and fitted using Dynamic Causal Modeling, its performance was compared against four alternative large-scale brain architectures that had been previously proposed in the  ...  The CMC framework was translated into a model of neural connectivity between brain regions homologous to CMC components.  ...  identifying clusters of functionally connected areas (Cole et al., 2013; Gorgolewski et al., 2014; Huntenburg et al., 2018) .  ... 
doi:10.1101/703777 fatcat:sajhume56ne4vnnrs2otgi6nd4

Studying the effective brain connectivity using multiregression dynamic models

Lilia Costa, Thomas Nichols, Jim Q. Smith
2017 Brazilian Journal of Probability and Statistics  
Note that the MDM correctly identified that connectivities have been simulated to vary over time. Smith, S.  ...  M. et al. (2011) to simulate data, using the DCM method.  ...  The proportion of connections selected correctly (logBF> 0) is around 70% over all comparisons and all replications.  ... 
doi:10.1214/17-bjps375 fatcat:r27kpbyrizh6jaazxaq54kmybu
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