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Six problems for causal inference from fMRI

J.D. Ramsey, S.J. Hanson, C. Hanson, Y.O. Halchenko, R.A. Poldrack, C. Glymour
2010 NeuroImage  
causal relations among activity in these regions (known as effective connectivity; Friston, 1994).  ...  Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative  ...  Acknowledgments This research was supported by a grant from the James S. McDonnell Foundation. We thank Linda Palmer, Peter Spirtes and Richard Scheines for valuable discussions.  ... 
doi:10.1016/j.neuroimage.2009.08.065 pmid:19747552 fatcat:26vhgvyzunaw3b6wzuwlz2txtq

Neural Correlates of Causal Inferences in Discourse Understanding and Logical Problem-Solving: A Meta-Analysis Study

Wangshu Feng, Weijuan Wang, Jia Liu, Zhen Wang, Lingyun Tian, Lin Fan
2021 Frontiers in Human Neuroscience  
These findings suggest that causal inferences in discourse understanding recruit distinct neural bases from those in logical problem-solving and rely more on semantic knowledge and social interaction experiences  ...  However, these findings have been divergent, and how these types of inferences are related to causal inferences in logical problem-solving remains unclear.  ...  Jiaxing Jiang for his assistance and suggestions. The authors also thank the two anonymous reviewers for their constructive comments on an earlier version of the manuscript.  ... 
doi:10.3389/fnhum.2021.666179 pmid:34248525 pmcid:PMC8261065 fatcat:7lffcqf3rnhr7efxu7pqiiryq4

fMRI connectivity, meaning and empiricism

Olivier David
2011 NeuroImage  
(this issue) raise fundamental questions regarding the identification of functional networks using fMRI.  ...  Estimating causality from standard fMRI cognitive experiments may then be an ill-posed problem because of lack of sufficient experimental control.  ...  We are facing much more profound difficulties regarding the development of robust inference of causality from neuroimaging data. Roebroeck et al. (this issue) discuss this issue for fMRI.  ... 
doi:10.1016/j.neuroimage.2009.09.073 pmid:19892020 fatcat:yoyhwcxwwveotnkw6ldkvnp5ni

Don't Fall in Love with Your Model: Model Selection for Graphical Model [article]

Joseph Ramsey
2017 Figshare  
Talk given during the "How To Be a Skeptical Neuroimager: Functional Connectivity & Causal Modeling" workshop at the 2011 Organization for Human Brain Mapping (OHBM) conference in in Quebec City, June  ...  Glymour(2010), Six Problems for causal inference from fMRI, NeuroImage.• J.D. Ramsey, S.J. Hanson, C.  ...  Conclusion • Machine Learning methods taken off the shelf from other disciplines aren't going to work on fMRI data. • Even sensible algorithms for analyzing fMRI data in simula8on draw conclusions that  ... 
doi:10.6084/m9.figshare.5484790.v1 fatcat:5w5zzyo7trbvdiskxgmg6qui2a

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data [article]

G. Wu, W.Liao, S. Stramaglia, J. Ding, H. Chen, D. Marinazzo
2012 arXiv   pre-print
For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes  ...  inferred by the parameters of a predictive dynamical model.  ...  In our opinion this joint approach is the most convenient to infer effective connectivity with Granger Causality from resting state fMRI data.  ... 
arXiv:1208.3766v1 fatcat:mol4dnucordtbkzzoem4mondp4

New Concepts in Brain Networks

Robert Turner, Gabriele Lohmann
2012 Frontiers in Systems Neuroscience  
Inference about causality using fMRI data is known to be a challenging problem. Here the authors present a list of six major issues to be addressed when tackling this problem.  ...  They propose a new method called "linear dynamic systems for fMRI" (lDSf) and thoroughly discuss these six issues within the framework of their method.  ... 
doi:10.3389/fnsys.2012.00056 pmid:22907995 pmcid:PMC3415675 fatcat:nij32lmafvca3i56vdjpxqym5a

Causal inference in audiovisual perception

Agoston Mihalik, Uta Noppeney
2020 Journal of Neuroscience  
To dissociate participants' causal inference from the spatial correspondence cues we adjusted the signals' audiovisual disparity individually for each participant to threshold accuracy.Multivariate fMRI  ...  An unresolved question is how the brain solves this binding or causal inference problem and determines the causal structure of the sensory signals.In this functional magnetic resonance imaging (fMRI) study  ...  which brain systems are critical for solving this causal inference problem.  ... 
doi:10.1523/jneurosci.0051-20.2020 pmid:32669354 pmcid:PMC7486655 fatcat:7iif35viircarndlxjcb7l3lxe

A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect

Rik J.C. van Esch, Shengling Shi, Antoine Bernas, Svitlana Zinger, Albert P. Aldenkamp, Paul M.J. Van den Hof
2020 Computers in Biology and Medicine  
Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data  ...  Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after the exposure to Mozart's sonata K448 at least 2 hours a day for 7 days.  ...  Acknowledgements Shengling Shi would like to thank Maarten Schoukens for his input into this work.  ... 
doi:10.1016/j.compbiomed.2020.104055 pmid:33157484 fatcat:fmp5rp6zbzeljlrkn43ym3gqf4

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

Guo-Rong Wu, Wei Liao, Sebastiano Stramaglia, Ju-Rong Ding, Huafu Chen, Daniele Marinazzo
2013 Medical Image Analysis  
For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes  ...  inferred by the parameters of a predictive dynamical model.  ...  Wu gratefully acknowledges the financial support from China Scholarship Council (2011607033).  ... 
doi:10.1016/ pmid:23422254 fatcat:g6euzkyjmzbani62x5hhao2tfm

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

Tim Rohe, Uta Noppeney, Christoph Kayser
2015 PLoS Biology  
Thus, perception inherently relies on solving the "causal inference problem."  ...  It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition.  ...  the fMRI sequence.  ... 
doi:10.1371/journal.pbio.1002073 pmid:25710328 pmcid:PMC4339735 fatcat:afxy4j3a2fhfjlmeevnkkupyba

Constructing Brain Connectivity Model Using Causal Network Reconstruction Approach

Supat Saetia, Natsue Yoshimura, Yasuharu Koike
2021 Frontiers in Neuroinformatics  
There are several mathematical frameworks that can be used to infer the connectivity model from brain activity signals.  ...  In the past, we could only study brain anatomical structures post-mortem, or infer brain functions from clinical data of patients with a brain injury.  ...  SS wrote the manuscript with support from YK. NY and YK helped supervise the project. SS, NY, and YK conceived the presented idea.  ... 
doi:10.3389/fninf.2021.619557 pmid:33679363 pmcid:PMC7930222 fatcat:jknimmwjoneyjkgwysjjkacs3q

Variational Bayesian causal connectivity analysis for fMRI

Martin Luessi, S. Derin Babacan, Rafael Molina, James R. Booth, Aggelos K. Katsaggelos
2014 Frontiers in Neuroinformatics  
We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data.  ...  Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm.  ...  Unfortunately, this problem has received little attention in recent work on causality estimation from fMRI data, where severe downsampling is common.  ... 
doi:10.3389/fninf.2014.00045 pmid:24847244 pmcid:PMC4017144 fatcat:y6sccwobsnfmvekvpawguqhyla

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Anupon Pruttiakaravanich, Jitkomut Songsiri
2016 Engineering Journal  
Exploring relationships between brain regions inside human brains from fMRI data is an active and challenging research topic.  ...  Interpretation and relations of these measures as well as relevant estimation techniques that are widely used in the problems of fMRI modeling are summarized in this paper.  ...  Valdés-Sosa and Esin Karahan at Joint China-Cuba Laboratory for Frontier Research in Translational Nuerotechnology, for pointing out many interesting references that make the list of measures for effective  ... 
doi:10.4186/ej.2016.20.3.207 fatcat:k4gftuygcjbizmnebfj2tvwtky

Neural activity of inferences during story comprehension

Sandra Virtue, Jason Haberman, Zoe Clancy, Todd Parrish, Mark Jung Beeman
2006 Brain Research  
To find the most compelling evidence of neural activity during inference generation, we examined fMRI signal at these two critical points separately for people with high working memory capacity (i.e.,  ...  We observed distinct patterns of increased fMRI signal for implied over explicit events at two critical points during the stories: (1) within the right superior temporal gyrus when a verb in the text implied  ...  Wong for their assistance.  ... 
doi:10.1016/j.brainres.2006.02.053 pmid:16574079 fatcat:ugolsdpgk5dydlki6kdmrws5jm

Frequency domain connectivity identification: An application of partial directed coherence in fMRI

João R. Sato, Daniel Y. Takahashi, Silvia M. Arcuri, Koichi Sameshima, Pedro A. Morettin, Luiz A. Baccalá
2007 Human Brain Mapping  
Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 00:000-000, 2007. V V C 2007 Wiley-Liss, Inc. Figure 5.  ...  In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap.  ...  In fMRI experiments, our experience with typical signal durations, suggests selecting a maximum of five or six areas for inclusion in the analysis.  ... 
doi:10.1002/hbm.20513 pmid:18064582 fatcat:johtfjk4arfi7egqfsnsprns7u
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