Quantitative mapping of trimethyltin injury in the rat brain using magnetic resonance histology.


The growing exposure to chemicals in our environment and the increasing concern over their impact on health have elevated the need for new methods for surveying the detrimental effects of these compounds. Today's gold standard for assessing the effects of toxicants on the brain is based on hematoxylin and eosin (H&E)-stained histology, sometimes accompanied by special stains or immunohistochemistry for neural processes and myelin. This approach is time-consuming and is usually limited to a fraction of the total brain volume. We demonstrate that magnetic resonance histology (MRH) can be used for quantitatively assessing the effects of central nervous system toxicants in rat models. We show that subtle and sparse changes to brain structure can be detected using magnetic resonance histology, and correspond to some of the locations in which lesions are found by traditional pathological examination. We report for the first time diffusion tensor image-based detection of changes in white matter regions, including fimbria and corpus callosum, in the brains of rats exposed to 8 mg/kg and 12 mg/kg trimethyltin. Besides detecting brain-wide changes, magnetic resonance histology provides a quantitative assessment of dose-dependent effects. These effects can be found in different magnetic resonance contrast mechanisms, providing multivariate biomarkers for the same spatial location. In this study, deformation-based morphometry detected areas where previous studies have detected cell loss, while voxel-wise analyses of diffusion tensor parameters revealed microstructural changes due to such things as cellular swelling, apoptosis, and inflammation. Magnetic resonance histology brings a valuable addition to pathology with the ability to generate brain-wide quantitative parametric maps for markers of toxic insults in the rodent brain.





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Publication Info

Johnson, G Allan, Evan Calabrese, Peter B Little, Laurence Hedlund, Yi Qi and Alexandra Badea (2014). Quantitative mapping of trimethyltin injury in the rat brain using magnetic resonance histology. Neurotoxicology, 42. pp. 12–23. 10.1016/j.neuro.2014.02.009 Retrieved from https://hdl.handle.net/10161/10329.

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G. Allan Johnson

Charles E. Putman University Distinguished Professor of Radiology

Dr. Johnson is the Charles E. Putman University Professor of Radiology, Professor of Physics, and Biomedical Engineering, and Director of the Duke Center for In Vivo Microscopy (CIVM). The CIVM is an NIH/NIBIB national Biomedical Technology Resource Center with a mission to develop novel technologies for preclinical imaging (basic sciences) and apply the technologies to critical biomedical questions. Dr. Johnson was one of the first researchers to bring Paul Lauterbur's vision of magnetic resonance (MR) microscopy to practice as described in his paper, "Nuclear magnetic resonance imaging at microscopic resolution" (J Magn Reson 68:129-137, 1986). Dr. Johnson is involved in both the engineering physics required to extend the resolution of MR imaging and in a broad range of applications in the basic sciences.


Evan Calabrese

Assistant Professor of Radiology

Alexandra Badea

Associate Professor in Radiology

I have a joint appointment in Radiology and Neurology and my research focuses on neurological conditions like Alzheimer’s disease. I work on imaging and analysis to provide a comprehensive characterization of the brain. MRI is particularly suitable for brain imaging, and diffusion tensor imaging is an important tool for studying brain microstructure, and the connectivity amongst gray matter regions.  

I am interested in image segmentation, morphometry and shape analysis, as well as in integrating information from MRI with genetics, and behavior. Our approaches  target: 1) phenotyping the neuroanatomy using imaging; 2) uncovering the link between structural and functional changes, the genetic bases, and environmental factors. I am interested in generating methods and tools for comprehensive phenotyping.

We use high-performance cluster computing to accelerate our image analysis. We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel, and graph analysis for connectomics.

At BIAC  my efforts focus on developing multivariate biomarkers and identifying vulnerable networks based on genetic risk for Alzheimer's disease.

My enthusiasm comes from the possibility to extend from single to integrative multivariate and network based analyses to obtain a comprehensive picture of normal development and aging, stages of disease, and the effects of treatments.  I am working on multivariate image analysis and predictive modeling approaches to help better understand early biomarkers for human disease indirectly through mouse models, as well as directly in human studies. 

I am dedicated to supporting an increase in female presence in STEM fields, and love working with students. The Bass Connections teams involve undergraduate students in research, providing them the opportunity to do independent research studies and get involved with the community. These students have for example takes classes such as:

BME 394: Projects in Biomedical Engineering (GE)
BME 493: Projects in Biomedical Engineering (GE)
ECE 899: Special Readings in Electrical Engineering
NEUROSCI 493: Research Independent Study 1

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