Six problems for causal inference from fMRI
Section snippets
Six problems for causal inference from fMRI
Functional Magnetic Resonance 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 causal relations among activity in these regions (known as “effective connectivities”; Friston, 1994). Modeling of this kind is typically done either by positing a parameterized causal structure a priori and estimating the parameters from data, or by statistical comparison of a few alternative
Addressing the problems using graphical causal models
We propose that modifications of machine learning techniques for graphical causal modeling available for more than a decade (Meek, 1997) provide a basis for addressing the problems we have sketched above. Appendix A presents a more detailed description of graphical causal models and more detail on the algorithms we use to learn these models from data. The software we used is available as freeware in the TETRAD IV suite of algorithms with a graphical user interface at //www.phil.cmu.edu/projects/tetrad
Simulation studies
In view of the theoretical difficulties with inference to latent, non-linear relations between sets of neurons, some investigation by simulation of the accuracy of the IMaGES procedures is needed. To address this, we simulated causal processes in which a large number of unrecorded latent variables, L (e.g., synaptic activity in each of a set of cortical columns), influence one another non-linearly, with feedback, and combine with a hemodynamic response function to produce a multivariate time
Discussion
The IMaGES algorithm is based on a procedure, GES, that has been proven consistent only for feed-forward, acyclic causal structures, whereas the simulated structures have feedback. Despite this, because of the non-linearity of the systems, IMaGES is able to extract the feed-forward structure with reasonable accuracy on large (for fMRI ROI studies) numbers of variables with multiple subject data, missing variables, and varying time delays. The general behavior of the IMaGES algorithm on the
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.
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