Literature DB >> 30474629

A Micro-CT-based Method for Characterizing Lesions and Locating Electrodes in Small Animal Brains.

Javier Masis1, David Mankus2, Steffen B E Wolff3, Grigori Guitchounts4, Maximilian Joesch5, David D Cox4.   

Abstract

Lesion and electrode location verification are traditionally done via histological examination of stained brain slices, a time-consuming procedure that requires manual estimation. Here, we describe a simple, straightforward method for quantifying lesions and locating electrodes in the brain that is less laborious and yields more detailed results. Whole brains are stained with osmium tetroxide, embedded in resin, and imaged with a micro-CT scanner. The scans result in 3D digital volumes of the brains with resolutions and virtual section thicknesses dependent on the sample size (12-15 and 5-6 µm per voxel for rat and zebra finch brains, respectively). Surface and deep lesions can be characterized, and single tetrodes, tetrode arrays, electrolytic lesions, and silicon probes can also be localized. Free and proprietary software allows experimenters to examine the sample volume from any plane and segment the volume manually or automatically. Because this method generates whole brain volume, lesions and electrodes can be quantified to a much higher degree than in current methods, which will help standardize comparisons within and across studies.

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Year:  2018        PMID: 30474629     DOI: 10.3791/58585

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  2 in total

1.  High-resolution micro-CT for 3D infarct characterization and segmentation in mice stroke models.

Authors:  Raquel Pinto; Jan Matula; Andrea Lobo; Maria Gomez-Lazaro; Mafalda Sousa; Tomas Zikmund; Jozef Kaiser; João R Gomes
Journal:  Sci Rep       Date:  2022-10-19       Impact factor: 4.996

2.  Imaging peripheral nerve micro-anatomy with MUSE, 2D and 3D approaches.

Authors:  Chaitanya Kolluru; Austin Todd; Aniruddha R Upadhye; Yehe Liu; Mikhail Y Berezin; Farzad Fereidouni; Richard M Levenson; Yanming Wang; Andrew J Shoffstall; Michael W Jenkins; David L Wilson
Journal:  Sci Rep       Date:  2022-06-17       Impact factor: 4.996

  2 in total

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