Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

• Published in: Frontiers in neuroinformatics Vol. 15; p. 674439
• Main Authors: Li, Dong, Wang, Guangyu, Werner, René, Xie, Hong, Guan, Ji-Song, Hilgetag, Claus C.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 05.01.2022; Frontiers Media S.A
• Subjects: Activity patterns; functional microscopic imaging; image analysis; Image processing; imaging artifacts; Microscopy; Nervous system; neural activity; Neuroinformatics; Pipelines; Protein expression; vignetting correction; vignetting correction; imaging artifacts; neural activity; image analysis; functional microscopic imaging
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2021.674439

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.