Six problems for causal inference from fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 49; no. 2; pp. 1545 - 1558
• Main Authors: Ramsey, J.D., Hanson, S.J., Hanson, C., Halchenko, Y.O., Poldrack, R.A., Glymour, C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2010; Elsevier Limited
• Subjects: Algorithms; Automation; Brain - physiology; Computer Simulation; Databases as Topic; Economic models; Feedback, Physiological; Graphs; Magnetic Resonance Imaging - methods; Models, Neurological; Nonlinear Dynamics; Oxygen - blood; Probability distribution; Random variables; Time Factors; Time series
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.08.065

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.