Calcium imaging can record from several dozens of neurons at once. Analyzing this raw data is expensive, so one typically wants to define regions of interest corresponding to cell bodies and work with the average calcium signal within.
Dario has a post on defining polygonal ROIs using the mean fluorescence image. Doing this manually is fairly time-consuming and it can be easy to miss perfectly good cells. Automated sorting methods still require some oversight, which can quickly become as time-consuming as defining the ROIs manually.
I’ve worked on an enhanced method that makes defining an ROI as simple as two clicks. The first enhancement is to use other reference images in adding to the mean fluorescence image: the correlation image, standard deviation over the mean, and kurtosis. The correlation image, discussed earlier on Labrigger, shows how correlated a single pixel is with its neighbour. When adjacent pixels are strongly correlated, that’s a good sign that that pixel belongs to a potential ROI. Similarly, pixels with high standard/mean and high kurtosis tend to correspond to potential ROIs.
With GCamp6f, however, you often have so many cells which are labelled that potential ROIs blend into each other in these alternative reference images. To solve this problem, I introduced x-ray. After I click on the flood fill button, the region surrounding the cursor shows pairwise correlations between the pixel underneath it with all the pixels surrounding it. You can easily tell two cells apart with x-ray by moving your over each of them individually*. Heck, you can even follow processes. We’ve seen some cells where you can follow processes for 200 microns. Ah!
In fact, these x-ray images are so clean that neurons can be identified just by flood filling from a user-defined point. This is what I do above: click, then move the mouse-wheel to adjust the number of pixels in the ROI, then click again to save the ROI. Since most cells are about the same size, you rarely have to adjust the number of pixels, so really two clicks per cell is all that is necessary. That’s still 250 clicks for this particular dataset (see at the end), but hey, quality problems!
The latest interface should be made available soon to all Scanbox users.
*x-ray is not the same as the correlation image. The correlation image is a single image of size h x v. x-ray is a stack of images of size h x v x windowh x windowv. It’s huge, and it contains a ton of information. You can derive the correlation image from the x-ray but not vice-versa.
6 responses to “Sorting calcium imaging signals”
X-ray is the best ray
This local crosscorrelation map (the ‘x-ray’ feature, if I got it right) is indeed something that has been used to study the morphology of neurons based on their activity … e.g. http://dx.doi.org/10.1016/j.bpj.2008.12.3962
I’m not surprised; it seems like an obvious thing to do. Using it in a GUI with live preview makes it really powerful and convenient, though.
Those are rather impressive reconstructions in that paper….
Reblogged this on Scanbox.