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).

Mild traumatic brain injury (mTBI) is highly prevalent but lacks both research tools with adequate sensitivity to detect cellular alterations that accompany mild injury and pre-clinical models that are able to robustly mimic hallmark features of human TBI. To address these related challenges, high-resolution diffusion tensor MRI (DTI) analysis was performed in a model of mild TBI in the ferret - a species that, unlike rodents, share with humans a gyrencephalic cortex and high white matter (WM) volume. A set of DTI image analysis tools were optimized and implemented to explore key features of DTI alterations in ex vivo adult male ferret brains (n = 26), evaluated 1 day to 16 weeks after mild controlled cortical impact (CCI). Using template-based ROI analysis, lesion overlay mapping and DTI-driven tensor-based morphometry (D-TBM) significant differences in DTI and morphometric values were found and their dependence on time after injury evaluated. These observations were also qualitatively compared with immunohistochemistry staining of neurons, astrocytes, and microglia in the same tissue. Focal DTI abnormalities including reduced cortical diffusivity were apparent in 12/13 injured brains with greatest lesion extent found acutely following CCI by ROI overlay maps and reduced WM FA in the chronic period was observed near to the CCI site (ANOVA for FA in focal WM: time after CCI p = 0.046, brain hemisphere p = 0.0012) often in regions without other prominent MRI abnormalities. Global abnormalities were also detected, especially for WM regions, which demonstrated reduced diffusivity (ANOVA for Trace: time after CCI p = 0.007) and atrophy that appeared to become more extensive and bilateral with longer time after injury (ANOVA for D-TBM Log of the Jacobian values: time after CCI p = 0.007). The findings of this study extend earlier work in rodent models especially by evaluation of focal WM abnormalities that are not influenced by partial volume effects in the ferret. There is also substantial overlap between DTI and morphometric findings in this model and those from human studies of mTBI implying that the combination of DTI tools with a human-similar model system can provide an advantageous and informative approach for mTBI research.

Defining an Analytic Framework to Evaluate Quantitative MRI Markers of Traumatic Axonal Injury: Preliminary Results in a Mouse Closed Head Injury Model.

Diffuse axonal injury (DAI) is a hallmark of traumatic brain injury (TBI) pathology. Recently, the Closed Head Injury Model of Engineered Rotational Acceleration (CHIMERA) was developed to generate an experimental model of DAI in a mouse. The characterization of DAI using diffusion tensor magnetic resonance imaging (MRI; diffusion tensor imaging, DTI) may provide a useful set of outcome measures for preclinical and clinical studies. The objective of this study was to identify the complex neurobiological underpinnings of DTI features following DAI using a comprehensive and quantitative evaluation of DTI and histopathology in the CHIMERA mouse model. A consistent neuroanatomical pattern of pathology in specific white matter tracts was identified across ex vivo DTI maps and photomicrographs of histology. These observations were confirmed by voxelwise and regional analysis of DTI maps, demonstrating reduced fractional anisotropy (FA) in distinct regions such as the optic tract. Similar regions were identified by quantitative histology and exhibited axonal damage as well as robust gliosis. Additional analysis using a machine-learning algorithm was performed to identify regions and metrics important for injury classification in a manner free from potential user bias. This analysis found that diffusion metrics were able to identify injured brains almost with the same degree of accuracy as the histology metrics. Good agreement between regions detected as abnormal by histology and MRI was also found. The findings of this work elucidate the complexity of cellular changes that give rise to imaging abnormalities and provide a comprehensive and quantitative evaluation of the relative importance of DTI and histological measures to detect brain injury.

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study - in-vivo MRI and DTI and ex-vivo MRI and DTI - using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders.

Susceptibility distortion correction for echo planar images with non-uniform B-spline grid sampling: a diffusion tensor image study.

In this paper, we propose a novel method for correcting the geometric distortions in diffusion weighted images (DWI) obtained with echo planar imaging (EPI) protocol. Our EPI distortion correction approach employs a deformable registration framework with the B-splines transformation, where the control point distributions are non-uniform and functions of the expected norm of the spatial distortions. In our framework, the amount of distortions are first computed by estimating the B(0) fieldmap from an initial segmentation of a distortion-free structural image and tissue susceptibility models. Fieldmap estimates are propagated to obtain expected spatial distortion maps, which are used in the sampling of active B-spline control points. This transformation is flexible in locations with large distortion expectations, yet with relatively few degrees-of-freedom and does not suffer from local optima convergence and hence does not distort anatomically salient locations. Results indicate that with the proposed correction scheme, tensor derived scalar maps and fiber tracts of the same subject computed from data acquired with different phase encoding directions provide better coherency and consistency compared traditional registration based approaches.

Automatic deformable diffusion tensor registration for fiber population analysis.

In this work, we propose a novel method for deformable tensor-to-tensor registration of Diffusion Tensor Images. Our registration method models the distances in between the tensors with Geode-sic-Loxodromes and employs a version of Multi-Dimensional Scaling (MDS) algorithm to unfold the manifold described with this metric. Defining the same shape properties as tensors, the vector images obtained through MDS are fed into a multi-step vector-image registration scheme and the resulting deformation fields are used to reorient the tensor fields. Results on brain DTI indicate that the proposed method is very suitable for deformable fiber-to-fiber correspondence and DTI-atlas construction.