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

Authors :
Dong Li
Guangyu Wang
René Werner
Hong Xie
Ji-Song Guan
Claus C. Hilgetag
Source :
Frontiers in Neuroinformatics, Vol 15 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

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.

Details

Language :
English
ISSN :
16625196
Volume :
15
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.0355e963c16d4bf7ac1e40217b4f2db2
Document Type :
article
Full Text :
https://doi.org/10.3389/fninf.2021.674439