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A comparison of automated anatomical-behavioural mapping methods in a rodent model of stroke.

Authors
  • Crum, William R1
  • Giampietro, Vincent P
  • Smith, Edward J
  • Gorenkova, Natalia
  • Stroemer, R Paul
  • Modo, Michel
  • 1 King's College London, Institute of Psychiatry, Department of Neuroimaging, London SE5 8AF, UK. [email protected]
Type
Published Article
Journal
Journal of neuroscience methods
Publication Date
Sep 15, 2013
Volume
218
Issue
2
Pages
170–183
Identifiers
DOI: 10.1016/j.jneumeth.2013.05.009
PMID: 23727124
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Neurological damage, due to conditions such as stroke, results in a complex pattern of structural changes and significant behavioural dysfunctions; the automated analysis of magnetic resonance imaging (MRI) and discovery of structural-behavioural correlates associated with these disorders remains challenging. Voxel lesion symptom mapping (VLSM) has been used to associate behaviour with lesion location in MRI, but this analysis requires the definition of lesion masks on each subject and does not exploit the rich structural information in the images. Tensor-based morphometry (TBM) has been used to perform voxel-wise structural analyses over the entire brain; however, a combination of lesion hyper-intensities and subtle structural remodelling away from the lesion might confound the interpretation of TBM. In this study, we compared and contrasted these techniques in a rodent model of stroke (n=58) to assess the efficacy of these techniques in a challenging pre-clinical application. The results from the automated techniques were compared using manually derived region-of-interest measures of the lesion, cortex, striatum, ventricle and hippocampus, and considered against model power calculations. The automated TBM techniques successfully detect both lesion and non-lesion effects, consistent with manual measurements. These techniques do not require manual segmentation to the same extent as VLSM and should be considered part of the toolkit for the unbiased analysis of pre-clinical imaging-based studies. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

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