The hypothesis that certain psychiatric or neurological diseases can be best understood—and therefore treated—at a systems level is an attractive one. For testing such a hypothesis, animal models are useful, and the ability to meaningfully compare multicellular activity between brains is needed. Identifying when population-level functional dynamics are typical or healthy, and when they are aberrant or pathological, is not straightforward, especially when there exists no within-animal baseline state to which to compare. This chapter focuses on practical considerations for applying popular ensemble-identification analyses (such as tSNE, SVM, k-means) to comparisons between groups, such as one might carry out between a wild-type animal and an animal with a genetic mutation affecting a disease-relevant pathway. While the methods are many, the principles are few(er). We focus first and foremost on the choice of metric: precisely what is thought to differ between groups? What aspect of multineuronal activity—stability, number, size, diversity, etc.—might be depleted or augmented in the disease state of interest? And could other behavioral or neural properties, such as arousal, motor output, or baseline neural firing rates, better account for change in your chosen metric, rather than a specific loss or disorganization of neural ensembles? We provide an example analysis pipeline wherein these points are considered.