Abstract

Anatomic evaluation is an important aspect of many studies in neuroscience; however, it often lacks information about the three-dimensional structure of the brain. Micro-CT imaging provides an excellent, nondestructive, method for the evaluation of brain structure, but current applications to neurophysiological or lesion studies require removal of the skull as well as hazardous chemicals, dehydration, or embedding, limiting their scalability and utility. Here we present a protocol using eosin in combination with bone decalcification to enhance contrast in the tissue and then employ monochromatic and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure with the preservation of the surrounding skull. Instead of relying on descriptive, time-consuming, or subjective methods, we develop simple quantitative analyses to map the locations of recording electrodes and to characterize the presence and extent of hippocampal brain lesions.

Details

Title
Scalable method for micro-CT analysis enables large scale quantitative characterization of brain lesions and implants
Author
Kastner, David B 1 ; Kharazia Viktor 2 ; Nevers Rhino 2 ; Smyth, Clay 2 ; Astudillo-Maya, Daniela A 2 ; Williams, Greer M 3 ; Yang Zhounan 3 ; Holobetz, Cristofer M 3 ; Santina, Luca Della 4 ; Parkinson, Dilworth Y 5 ; Frank, Loren M 6 

 University of California, Department of Psychiatry and Behavioral Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); University of California, Kavli Institute for Fundamental Neuroscience and Department of Physiology, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811) 
 University of California, Kavli Institute for Fundamental Neuroscience and Department of Physiology, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811) 
 University of California, Department of Psychiatry and Behavioral Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811) 
 University of California, Deparment of Ophthalmology, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); University of California, Bakar Computational Health Science Unit, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811) 
 Lawrence Berkeley National Labs, Advanced Light Source, Berkeley, USA (GRID:grid.184769.5) (ISNI:0000 0001 2231 4551) 
 University of California, Department of Psychiatry and Behavioral Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); University of California, Kavli Institute for Fundamental Neuroscience and Department of Physiology, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); Howard Hughes Medical Institute, Chevy Chase, USA (GRID:grid.413575.1) (ISNI:0000 0001 2167 1581) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2473272225
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.