Month: October 2014

Real-time, motion compensation in Scanbox

Our colleague Tobias Rose was recently asking about Scanbox’s ability to stabilize the motion of images and signals from regions-of-intrest (ROIs) in real-time.  The goal of such processing is to be able to do experiments in closed-loop, and do very quick analyses on the neuronal responses, such as computing tuning curves on the fly.

Below is a simple demonstration of automatic stabilization in Scanbox, where the microscope is tracking the activity on a few ROIs the size of a single cell body. In the first pass of the ROI signals displayed at the bottom motion compensation is off.  The traces are noisy as the cell bodies themselves move with respect to the defined ROI.  The second pass shows the same image sequence once image stabilization is engaged (when the checkbox on the lower left is clicked), the images appear much more stable in space and, correspondingly, the signals for the ROIs are smoother and the resulting SNR is higher.

Incidentally, the demo also shows another feature of Scanbox — the ability to play back already collected data in a loop, so people can  learn the features of the microscope and try things out without the need of a living, animal subject.

Sorting calcium imaging signals

xcorr: comp neuro

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…

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