Six problems for causal inference from fMRI
Open accessAbstract
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 causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.
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Bayes nets model the full set of covariances or conditional dependencies between all nodes' timeseries and show some promise.
“Bayes nets model the full set of covariances (or conditional dependencies) between all nodes’ timeseries [59] and show some promise.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [59]
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- Authors
- Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C.
- Affiliations
- Carnegie Mellon University (US)
- Rutgers, The State University of New Jersey (US)
- The University of Texas at Austin (US)
- Florida Institute for Human and Machine Cognition (US)
- Corresponding author
- Ramsey, J.D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-01
- Volume
- 49
- Issue
- 2
- Pages
- 1545-1558
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2109198067
- Cited by
- 337
- References
- 50
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 8 · 2023: 16 · 2022: 22 · 2021: 23 · 2020: 24 · 2019: 18 · 2018: 27 · 2017: 29 · 2016: 32 · 2015: 22 · 2014: 15 · 2013: 21 · 2012: 23
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion, cc-by-nc-nd)
- Funders
- James S. McDonnell Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- effective connectivity, causal inference, functional MRI, nonlinear time series, feedback, search strategies
- MeSH terms
- Brain, Computer Simulation, Magnetic Resonance Imaging, Models, Neurological, Oxygen, Time Factors, Nonlinear Dynamics, Databases as Topic, Feedback, Physiological
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