Having amassed knowledge on which brain regions relate to specific tasks, functional MRI researchers increasingly wish to identify how spatially disparate brain regions relate across time. “Connectivity mapping” broadly refers to approaches for identifying such brain processes. This course primes researchers to conduct state-of-the-art connectivity mapping analysis tailored for the nuances inherent in fMRI data. The following major topics will be explored : a) Physical principles of MRI; b) Introduction to multivariate time series analysis; c) General linear model; d) Structural equation models; e) Vector autoregression and Granger causality testing; f) Dynamic causal model; g) State space models; h) Reliable group inferences. This course begins at a basic level and culminates in application of cutting-edge methods to simulated and empirical data (provided). Advanced undergraduate and graduate students with an interest in conducting fMRI research are encouraged to enroll.