Our group has been focusing for the past several years on detecting and characterizing ever more subtle and elusive fMRI changes in task-based and resting-state fMRI. Using a strategy involving integration of paradigm design, acquisition, and novel processing approaches, we have had some unique and surprising findings. Specifically, we’ve been able to a) substantially reduce artifactual time series noise - including motion effects using multi-echo EPI in combination with independent component analysis, b) reveal that nearly the entire brain is active - in unanticipated ways - for even simple tasks, using massive averaging and a model - free approach, c) reveal that “brain reading” can be carried out by simply observing the unique connectivity patterns associated with different ongoing tasks. Furthermore, we show that the locations of the nodes of these “informative” connection changes can differ in location from the typically observed magnitude changes in the brain - thus potentially revealing a previously-undetected class of changes (connectivity rather than just magnitude) with brain activation. In addition, this approach appears to be much more sensitive in detecting ongoing tasks than simple assessment of the magnitude changes. Our primary message in these examples that the integrated implementation of paradigm design, acquisition, and relatively simple processing including can open up novel directions in understanding brain organization at an individual level.