1 2 Learning functional structure from fMR images 3

9 We propose a novel method using Bayesian networks to learn the 10 structure of effective connectivity among brain regions involved in a 11 functional MR experiment. The approach is exploratory in the sense 12 that it does not require an a priori model as in the earlier approaches, 13 such as the Structural Equation Modeling or Dynamic Causal 14 Modeling, which can only affirm or refute the connectivity of a 15 previously known anatomical model or a hypothesized model. The 16 conditional probabilities that render the interactions among brain 17 regions in Bayesian networks represent the connectivity in the complete 18 statistical sense. The present method is applicable even when the 19 number of regions involved in the cognitive network is large or 20 unknown. We demonstrate the present approach by using synthetic 21 data and fMRI data collected in silent word reading and counting 22 Stroop tasks. 23 D 2006 Elsevier Inc. All rights reserved. 24 25