[ARTICLE] Diffusion MRI and the detection of alterations following traumatic brain injury – Full Text

Abstract

This article provides a review of brain tissue alterations that may be detectable using diffusion magnetic resonance imaging MRI (dMRI) approaches and an overview and perspective on the modern dMRI toolkits for characterizing alterations that follow traumatic brain injury (TBI). Noninvasive imaging is a cornerstone of clinical treatment of TBI and has become increasingly used for preclinical and basic research studies. In particular, quantitative MRI methods have the potential to distinguish and evaluate the complex collection of neurobiological responses to TBI arising from pathology, neuroprotection, and recovery. dMRI provides unique information about the physical environment in tissue and can be used to probe physiological, architectural, and microstructural features. Although well-established approaches such as diffusion tensor imaging are known to be highly sensitive to changes in the tissue environment, more advanced dMRI techniques have been developed that may offer increased specificity or new information for describing abnormalities. These tools are promising, but incompletely understood in the context of TBI. Furthermore, model dependencies and relative limitations may impact the implementation of these approaches and the interpretation of abnormalities in their metrics. The objective of this paper is to present a basic review and comparison across dMRI methods as they pertain to the detection of the most commonly observed tissue and cellular alterations following TBI.

1 INTRODUCTION

Despite the long history of traumatic brain injury (TBI) as a prevalent cause of death and disability in humans, defining the neurobiological underpinnings of damage and recovery following TBI remains a central challenge. The complex collection of physiological, cellular, and molecular changes that follow TBI can appear to be remarkably heterogeneous, but at the same time they are highly organized into coordinated responses such as neurodegeneration, inflammation, and regeneration. The corpus of histological studies spanning a variety of experimental animal models of TBI have provided crucial insights about the pathomechanisms and cellular alterations that accompany posttraumatic tissue change, but considerable work remains to determine the spatiotemporal evolution of abnormalities, interrelationships among different tissue responses, and their impact on health and behavioral outcomes. Noninvasive imaging in animal models has the potential to build on what is known from histology by providing longitudinal and whole-brain information, but for this approach to be successful it is essential to first improve the understanding of how imaging abnormalities correspond to tissue and cellular changes.

Diffusion magnetic resonance imaging (dMRI) methods are particularly promising for the development of imaging markers of TBI pathology because they are sensitive to microscale water displacement as a proxy for tissue environment geometry and provide a range of quantitative scalar metrics across the whole brain. Furthermore, dMRI may be combined with other conventional or advanced magnetic resonance imaging (MRI) methods such as arterial spin labeling, susceptibility-weighted imaging, or a variety of contrast agent MRI approaches to provide complementary and comprehensive outcome measures. Standard dMRI methods and especially diffusion tensor imaging (DTI) have already demonstrated sensitive detection of abnormalities in a number of experimental models of TBI. In the past decade, multiple advanced dMRI approaches have extended beyond the conventional models with the goals of improving the physical description of water diffusion (e.g., by modeling “non-Gaussian” diffusion) or parameterizing dMRI with respect to the expected biological environment (e.g., by modeling cellular compartments and/or fiber geometry). These new tools will be valuable if they are able to improve the sensitivity or specificity of dMRI following TBI; however, we lack a systematic understanding of how dMRI methods differ from one another for detecting and describing tissue alterations.

A number of excellent reviews exist to describe the current understanding of cellular mechanisms of TBI in general (Bramlett & Dietrich, 2015; Pekna & Pekny, 2012) and within particular areas of neurobiology including neurodegeneration (Johnson, Stewart, & Smith, 2013; Stoica & Faden, 2010), inflammation (Burda, Bernstein, & Sofroniew, 2016; Ziebell & Morganti-Kossmann, 2010), and myelin changes (Armstrong, Mierzwa, Marion, & Sullivan, 2016), among others. As well, several existing reviews have been published regarding MRI and DTI to study human TBI (Brody, Mac Donald, & Shimony, 2015; Duhaime et al., 2010; Hulkower, Poliak, Rosenbaum, Zimmerman, & Lipton, 2013), and recently a pertinent overview and summary of advanced dMRI tools and their relevance to clinical outcomes was published (Douglas et al., 2015). The focus of the present review is to combine what is known from work in experimental models of TBI about tissue and cellular alterations that may affect the physical tissue environment with a comparative description of the major methods for dMRI that may be differentially sensitive to TBI-related tissue change alongside several important caveats for their use and interpretation. The first section provides a categorical summary of cellular response to trauma, emphasizing alterations with microstructural, architectural, or neuroanatomical manifestations that may give rise to detectable dMRI abnormalities, including a review of the existing dMRI studies in experimental TBI models. The second section contains a comparative overview of presently available dMRI methods from standard approaches to advanced techniques. The objective of this article is to provide a reference for the current understanding of these topics as well as a perspective to help guide selection of dMRI tools based on particular aspects of TBI questions.

Continue —> Diffusion MRI and the detection of alterations following traumatic brain injury – Hutchinson – 2017 – Journal of Neuroscience Research – Wiley Online Library

Figure 2. Cross-model comparison of scalar maps in the injured brain. A range of tissue and injury-related contrasts may be visually observed in this collage of 16 representative metrics in the same slice from different dMRI models. This cross-model view of scalar maps demonstrates the potential for nonredundant information about regions of injury that may be gleaned from different models. DTI metrics of fractional anisotropy (FA), trace (TR), axial and radial diffusivity (Dax and Drad), directionally encoded color (DEC) map weighted by lattice index, DEC weighted by Westin linear anisotropy (WL) and DEC weighted by Westin planar anisotropy (WP), DKI metrics of mean kurtosis (MK), axial and radial kurtosis (AK and RK) and kurtosis FA (KFA), MAP-MRI metrics of return to the origin, axis, and plane probabilities (RTOP, RTAP, and RTPP), propagator anisotropy (PA) and non-Gaussianity (NG) and NODDI metrics of compartment volume fractions for isotropic free water (Viso), intracellular water (Vic) and intracellular restricted water (Vir), and orientation dispersion index (ODI). Insets of each map show tissue near the injury site where dMRI values are expected to be abnormal.