Detection and Distinction of Mild Brain Injury Effects in a Ferret Model Using Diffusion Tensor MRI (DTI) and DTI-Driven Tensor-Based Morphometry (D-TBM)

Hutchinson, Elizabeth; Schwerin, Susan C.; Radomski, Kryslaine L.; Sadeghi, Neda; Komlosh, Michal E.; İrfanoğlu, M. Okan; Juliano, Sharon L.; Pierpaoli, Carlo

doi:10.3389/fnins.2018.00573

Cited by 16 publications

(15 citation statements)

References 47 publications

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“…This is not the focus of our research and does not directly impact on the observations we have made about the relationships between biomechanical forces and glial response and axonal injury. However, further work with gyrencephalic animals such as ferrets or pigs would allow the impact of sulcal anatomy on the relationship between biomechanical forces and brain injury to be studied directly ( Schwerin et al , 2017 , 2018 ; Hutchinson et al , 2018 ).…”

Section: Discussionmentioning

confidence: 99%

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From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury

Donat

Lopez

Sastre

et al. 2021

Brain

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The relationship between biomechanical forces and neuropathology is key to understanding traumatic brain injury. White matter tracts are damaged by high shear forces during impact, resulting in axonal injury, a key determinant of long-term clinical outcomes. However, the relationship between biomechanical forces and patterns of white matter injuries, associated with persistent diffusion MRI abnormalities, is poorly understood. This limits the ability to predict the severity of head injuries and the design of appropriate protection. Our previously developed human finite element model of head injury predicted the location of post-traumatic neurodegeneration. A similar rat model now allows us to experimentally test whether strain patterns calculated by the model predicts in vivo MRI and histology changes. Using a controlled cortical impact, mild and moderate injuries (1 and 2 mm) were performed. Focal and axonal injuries were quantified with volumetric and diffusion 9.4 T MRI at 2 weeks post injury. Detailed analysis of the corpus callosum was conducted using multi-shell diffusion MRI and histopathology. Microglia and astrocyte density, including process parameters, along with white matter structural integrity and neurofilament expression were determined by quantitative immunohistochemistry. Linear mixed effects regression analyses for strain and strain rate with the employed outcome measures were used to ascertain how well immediate biomechanics could explain MRI and histology changes. The spatial pattern of mechanical strain and strain rate in the injured cortex shows good agreement with the probability maps of focal lesions derived from volumetric MRI. Diffusion metrics showed abnormalities in the corpus callosum, indicating white matter changes in the segments subjected to high strain, as predicted by the model. The same segments also exhibited a severity-dependent increase in glia cell density, white matter thinning and reduced neurofilament expression. Linear mixed effects regression analyses showed that mechanical strain and strain rate were significant predictors of in vivo MRI and histology changes. Specifically, strain and strain rate respectively explained 33% and 28% of the reduction in fractional anisotropy, 51% and 29% of the change in neurofilament expression and 51% and 30% of microglia density changes. The work provides evidence that strain and strain rate in the first milliseconds after injury are important factors in determining patterns of glial and axonal injury and serve as experimental validators of our computational model of traumatic brain injury. Our results provide support for the use of this model in understanding the relationship of biomechanics and neuropathology and can guide the development of head protection systems, such as airbags and helmets.

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Section: Discussionmentioning

confidence: 99%

“…However, further work with gyrencephalic animals such as ferrets or pigs would allow the impact of sulcal anatomy on the relationship between biomechanical forces and brain injury to be studied directly (Schwerin et al, 2017;Hutchinson et al, 2018;Schwerin et al, 2018).…”

Section: Gfap-positive Cellsmentioning

confidence: 99%

From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury

Donat

Lopez

Sastre

et al. 2021

Brain

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“…In a recent ferret model of mTBI, FA reductions in the white matter regions not proximate to the site of impact were most pronounced at later time points (ie, 4-16 weeks). 21…”

Section: Discussionmentioning

confidence: 99%

“…14 Only few DTI studies have proven to reliably detect TBI and approximate time of injury 15 in various rodent models of axonal injury following single or repetitive head impact. [15][16][17][18][19][20][21] Moreover, few studies have assessed the evolution of axonal injury over time following mTBI in rodents. It has been suggested that longitudinal studies are needed to determine how changes in DTI indices are related to recovery in response to concussive injury.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Tensor Imaging of the Evolving Response to Mild Traumatic Brain Injury in Rats

Hoogenboom

Rubin

et al. 2019

J Exp Neurosci

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Mild traumatic brain injury (mTBI), also known as concussion, is a serious public health challenge. Although most patients recover, a substantial minority suffers chronic disability. The mechanisms underlying mTBI-related detrimental effects remain poorly understood. Although animal models contribute valuable preclinical information and improve our understanding of the underlying mechanisms following mTBI, only few studies have used diffusion tensor imaging (DTI) to study the evolution of axonal injury following mTBI in rodents. It is known that DTI shows changes after human concussion and the role of delineating imaging findings in animals is therefore to facilitate understanding of related mechanisms. In this work, we used a rodent model of mTBI to investigate longitudinal indices of axonal injury. We present the results of 45 animals that received magnetic resonance imaging (MRI) at multiple time points over a 2-week period following concussive or sham injury yielding 109 serial observations. Overall, the evolution of DTI metrics following concussive or sham injury differed by group. Diffusion tensor imaging changes within the white matter were most noticeable 1 week following injury and returned to baseline values after 2 weeks. More specifically, we observed increased fractional anisotropy in combination with decreased radial diffusivity and mean diffusivity, in the absence of changes in axial diffusivity, within the white matter of the genu corpus callosum at 1 week post-injury. Our study shows that DTI can detect microstructural white matter changes in the absence of gross abnormalities as indicated by visual screening of anatomical MRI and hematoxylin and eosin (H&E)-stained sections in a clinically relevant animal model of mTBI. Whereas additional histopathologic characterization is required to better understand the neurobiological correlates of DTI measures, our findings highlight the evolving nature of the brain’s response to injury following concussion.

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“…Xtract contains tractography libraries for humans and macaques derived from the same protocols (Warrington et al, 2020) and is currently being extended to include more species, including the chimpanzee (Bryant et al, 2020). AFNI (Cox, 1996) and TORTOISE (Pierpaoli, et al, 2010) have several diffusion processing tools and integrated features for distortion correction (Irfanoglu et al, 2015), deterministic and probabilistic tractography (Taylor and Saad, 2013), tensor-based morphometry (Hutchinson et al, 2018;Irfanoglu et al, 2016), and network-based structural analyses (Taylor et al, 2016b). Additionally, Dipy is a Python-based software toolbox for diffusion processing (Garyfallidis et al, 2014), and DSI-Studio contains tools for tracking and statistics with a particular focus on high angular resolution diffusion imaging (HARDI) techniques (http://dsistudio.labsolver.org).…”

Section: Diffusion-weighted Mri Toolsmentioning

confidence: 99%

A collaborative resource platform for non-human primate neuroimaging

Messinger¹,

Sirmpilatze

Heuer

et al. 2020

Preprint

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Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.

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Detection and Distinction of Mild Brain Injury Effects in a Ferret Model Using Diffusion Tensor MRI (DTI) and DTI-Driven Tensor-Based Morphometry (D-TBM)

Cited by 16 publications

References 47 publications

From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury

From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury

Diffusion Tensor Imaging of the Evolving Response to Mild Traumatic Brain Injury in Rats

A collaborative resource platform for non-human primate neuroimaging

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