The identification of causal networks in the brain from fMRI data: possibilities, limitations and subtle aspects

Alard Roebroeck, Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University

Functional Magnetic Resonance Imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data-analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. This talk discusses a few crucial points about fMRI connectivity analysis: i) Model selection procedures for connectivity and the structural graph models that are considered, ii) Temporal precedence in dynamic models of connectivity and causality concepts based on it, such as Wiener/Granger causality iii) The effects of hemodynamics on fMRI connectivity measures and the incorporation of those effects in deconvolution approaches.