Brain imaging technology provides a way to sample various aspects of the brain albeit incompletely, providing a rich set of features crossing rest and task conditions, and an ever-growing number of imaging modalities. The conditions being studied with brain imaging data are often extremely complex and it is becoming more common for researchers to employ more than one imaging or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features, modalities or tasks. We propose an intuitive framework based on Markov-style flows for understanding information exchange between features in what we are calling a feature meta-space: that is, a space consisting of an arbitrary number of individual feature spaces, where the features can have any dimension and can be drawn from any data source or modality. We present preliminary work demonstrating the ability of this new framework to identify relationships between disparate features of varying dimensionality.