Analysis of calcium imaging signals from the honeybee brain by nonlinear models

Recent Ca(2+)-imaging studies on the antennal lobe of the honeybee (Apis mellifera) have shown that olfactory stimuli evoke complex spatiotemporal changes of the intracellular Ca(2+) concentration, in which stimulus-dependent subsets of glomeruli are highlighted. In this work we use nonlinear models for the quantitative identification of the spatial and temporal properties of the Ca(2+)-dependent fluorescence signal. This technique describes time series of the Ca(2+) signal as a superposition of biophysically motivated model functions for photobleaching and Ca(2+) dynamics and provides optimal estimates of their amplitudes (signal strengths) and time constants together with error measures. Using this method, we can reliably identify two different stimulus-dependent signal components. Their delays and rise times, delta(c1) = (0.4 +/- 0.3) s, tau(c1) = (3.8 +/- 1.2) s for the fast component and delta(c2) = (2.4 +/- 0.6) s, tau(c2) = (10.3 +/- 3.2) s for the slow component, are constant over space and across different odors and animals. In chronological experiments, the amplitude of the fast (slow) component often decreases (increases) with time. The pattern of the Ca(2+) dynamics in space and time can be reliably described as a superposition of only two spatiotemporally separable patterns based on the fast and slow components. However, the distributions of both components over space turn out to differ from each other, and more work has to be done in order to specify their relationship with neuronal activity.