Functional Ultrasound (fUS) is a relatively new modality to measure brain activity with a high spatio-temporal resolution. In order to collect full-brain information with this 2D imaging technique, fUS data is typically collected for a fixed position of the ultrasound probe for the duration of the experiment, before the probe is moved to the next position. As a result, a 3D functional volume consists of subsequent, time-disjunct 2D datasets. The gold-standard way to analyze fUS datasets is using correlation images or a general linear model. However, these analyses are performed slice by slice; thus, common information across slices is not exploited. We propose the use of two data-driven models, Independent Component Analysis (ICA) and its multiset extension Independent Vector Analysis (IVA), in order to map the mouse visual information processing pathway in 3D. We demonstrate the successful application of ICA and then of IVA, which leverages the dependence across slices in a unique fashion. Furthermore, we provide guidance as to when which approach might be desirable.