"Functional connectivity" or "resting state" MRI has become commonplace in neuroscience over the last decade, and is increasingly used for clinical studies. This talk introduces some of the central concepts and findings in resting state fMRI. Earlier talks will cover fMRI data acquisition, this talk will mainly discuss data analysis and interpretation. This talk will open by introducing a convenient way to visualize fMRI scans, and then will use this approach to visually fractionate resting state fMRI data (via multi-echo analyses) into non-BOLD and BOLD signals. Only BOLD signals are typically thought to be of interest, but both kinds of signals are prevalent in fMRI scans, and both kinds of signals correlate with cognitive and behavioral variables of interest, making it important to recognize the signatures of each kind of signal. We will discuss the spatial and temporal manifestations of these signals and illustrate how these signals influence functional connectivity properties. We will illustrate how individual denoising techniques remove particular kinds of signals, and that no single denoising technique removes all unwanted signals from a dataset. Effective denoising requires multiple simultaneous approaches to best isolate BOLD signals of interest.