Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy - Li, Dong; Wang, Guangyu; Werner, René; Xie, Hong; Guan, Ji-Song; Hilgetag, Claus C. (Frontiers in Neuroinformatics, 2022)
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 towards 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.