Single Image-based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy
Single Image-based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy
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 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.
Li Dong、Werner Ren¨|、Wang Guangyu、Hilgetag Claus C.、Xie Hong、Guan Ji-Song
Institute of Computational Neuroscience, University Medical Center Hamburg-EppendorfInstitute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf||Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-EppendorfSchool of Life Science and Technology, ShanghaiTech UniversityInstitute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf||Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf||Department of Health Sciences, Boston UniversityZhangjiang Laboratory, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Instiute of Brain-Intelligence TechnologySchool of Life Science and Technology, ShanghaiTech University||Institute of Psychology, Chinese Academy of Sciences
生物科学现状、生物科学发展生物科学研究方法、生物科学研究技术生物物理学
vignetting correctionfunctional microscopic imagingneural activityimage analysisimaging artifacts
Li Dong,Werner Ren¨|,Wang Guangyu,Hilgetag Claus C.,Xie Hong,Guan Ji-Song.Single Image-based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy[EB/OL].(2025-03-28)[2025-05-22].https://www.biorxiv.org/content/10.1101/2021.03.01.433412.点此复制
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