Abstract
High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact—the decrease of image intensity toward the edges of an image—is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.
Highlights
Modern microscopic imaging techniques provide unique insights into the structure and functioning of complex biological neural systems
Due to the application-inherent lack of ground truth data to evaluate the effect of the illumination correction, we focus on data plausibility: In the presence of a vignetting artifact, the functional data contain a significant center-periphery bias of neural activity measures that, for the performed experiments, cannot be accounted for by the underlying biology
Considering the reduction of the center-periphery bias in neural activity as well as consistency of absolute and relative activity changes over time as proxies of success, we demonstrate that the vignetting correction substantially improves data plausibility
Summary
Modern microscopic imaging techniques provide unique insights into the structure and functioning of complex biological neural systems. A common and major illumination artifact in microscopic images is the vignetting artifact, which reflects a (usually radial) decrease of the image brightness, its contrast, or the saturation from the image center toward the periphery (Leong et al, 2003). Reference and multiple image-based methods are considered most reliable (Smith et al, 2015), but are not always applicable due to application-specific constraints (e.g., data availability). At this point, single image correction strategies come into play (Leong et al, 2003; Zheng et al, 2008)
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