@article {843, title = {Cloak and DAG: a response to the comments on our comment.}, journal = {Neuroimage}, volume = {76}, year = {2013}, month = {2013 Aug 1}, pages = {446-9}, abstract = {

Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes{\textquoteright} notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour{\textquoteright}s comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.11.027}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {842, title = {Dynamic connectivity regression: determining state-related changes in brain connectivity.}, journal = {Neuroimage}, volume = {61}, year = {2012}, month = {2012 Jul 16}, pages = {907-20}, abstract = {

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method{\textquoteright}s ability to observe how the networks between different brain regions changed with subjects{\textquoteright} emotional state.

}, keywords = {Algorithms, Brain, brain mapping, Computer Simulation, Emotions, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2012.03.070}, author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A} } @article {845, title = {Graphical models, potential outcomes and causal inference: comment on Ramsey, Spirtes and Glymour.}, journal = {Neuroimage}, volume = {57}, year = {2011}, month = {2011 Jul 15}, pages = {334-6}, abstract = {

Ramsey, Spirtes and Glymour (RSG) critique a method proposed by Neumann et al. (2010) for the discovery of functional networks from fMRI meta-analysis data. We concur with this critique, but are unconvinced that directed graphical models (DGMs) are generally useful for estimating causal effects. We express our reservations using the "potential outcomes" framework for causal inference widely used in statistics.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.020}, author = {Lindquist, Martin A and Sobel, Michael E} }