This paper basically does the PCA-ICA breakdown of the imaging signals.
Figure 1. Analytical Stages of Automated Cell Sorting
(A) The goal of cell sorting is to extract cellular signals from imaging data (left) by estimating spatial ﬁlters (middle) and activity traces (right) for each cell. The example depicts typical ﬂuorescence transients in the cerebellar cortex as observed in optical cross-section. Transients in Purkinje cell dendrites arise across elongated areas seen as stripes in the movie data. Transients in Bergmann glial ﬁbers tend to be more localized, appearing ellipsoidal.
(B) Automated cell sorting has four stages that address speciﬁc analysis challenges.
So they also do this ICA in both space and time dimensions, and use both for trying to identify the cell traces. Most of the info comes from the space dimension (like the way I do it), and the best uses a 0.1-0.2 weighted combination of time and space.
In the image segmentation step, they identify spatially separate filters that are caused by different neurons that are highly correlated. Typically ICA handles correlations above 0.8 well, but occassionally it picks out two cells as one component.
Yeah, this is basically the paper that I want to write, but apparently its already been done... Going to check out their toolbox and see if there are any new tricks I can add.