Semi-automated region of interest generation for the analysis of optically recorded neuronal activity

Bath-applied membrane-permeant Ca(2+) indicators offer access to network function with single-cell resolution. A barrier to wider and more efficient use of this technique is the difficulty of extracting fluorescence signals from the active constituents of the network under study. Here we present a method for semi-automatic region of interest (ROI) detection that exploits the spatially compact, slowly time-varying character of the somatic signals that these indicators typically produce. First, the image series is differenced to eliminate static and very slowly varying fluorescence values, and then the differenced image series undergoes low-pass filtering in the spatial domain, to eliminate temporally isolated fluctuations in brightness. This processed image series is then thresholded so that pixel regions of fluctuating brightness are set to white, while all other regions are set to black. Binary images are averaged, and then subjected to iterative thresholding to extract ROIs associated with both dim and bright cells. The original image series is then analyzed using the generated ROIs, after which the end-user rejects spurious signals. These methods are applied to respiratory networks in the neonate rat tilted sagittal slab preparation, and to simulations with signal-to-noise ratios ranging between 1.0-0.2. Simulations established that algorithm performance degraded gracefully with increasing noise. Because signal extraction is the necessary first step in the analysis of time-varying Ca(2+) signals, semi-automated ROI detection frees the researcher to focus on the next step: selecting traces of interest from the relatively complete set generated using these methods.