traumatic microbleeds. Although these MRI techniques can detect white matter damage, techniques such as DTI are required to detect more subtle markers of axonal injury. Several experimental studies have to date demonstrated the capacity of DTI to identify such white matter damage following TBI (see FitzGerald & Crosson, 2011 or Niogi & Mukherjee, 2010, for a review). This could be especially useful for the detection of mild TBI, as currently some patients who have normal standard imaging results after mild TBI go on to experience persistent cognitive problems. If DTI were to reveal subtle abnormalities in white matter structure, and if these abnormalities were shown to consistently predict functional outcome, then their early detection on clinical neuroimaging would be of prognostic value.
There are a range of other brain imaging modalities, including functional MRI (fMRI), magnetoencephalography and electroencephalography, Xenon-enhanced CT, CT perfusion, single photon emission computed tomography (SPECT), positron emission tomography (PET) and MR spectroscopy (MRS). These have been used experimentally in brain injury research to image neural activation and different aspects of brain physiology (Coles, 2007). Although this diversity of assessment methods has the potential to provide important additional information, it also adds to the complexity within this field of research. Work is under way, however, to identify the techniques and indices which are most useful in assessing, classifying, and evaluating outcomes after TBI (see e.g. Duhaime et al., 2010; Maas et al., 2010; Saatman et al., 2008).
1.6 Diffusion Tensor Imaging (DTI)
1.6.1 Diffusion tensor imaging of brain white matter. Compared with conventional CT and MR imaging, diffusion-weighted MRI (Le Bihan & Breton, 1985) measures a fundamentally different physiological parameter in that the image contrast reflects the rate of diffusion (displacement) of water molecules in brain tissue. Investigation of the 3D process of diffusion-driven displacements of water molecules allows probing the structure of brain tissue at a fine scale, that is, to infer properties of tissue microstructure, beyond the conventional MR image resolution (Le Bihan et al., 2001). Therefore, diffusion MRI can differentiate between regions with reduced and elevated rates of diffusion and may reveal neuropathological changes
in cases where standard brain imaging cannot. Diffusion tensor imaging (Basser et al., 1994) is one application of diffusion MRI and has shown promise in the assessment of neuropathology of TBI, as it can be used to study the structure of white matter tracts that are susceptible to damage by TBI. Specifically, DTI can be used to measure the degree and spatial distribution of anisotropic diffusion within these tracts. The tracts run in three principal directions (superior- inferior, anterior-posterior, and left-right) and are visualised by reconstructing the diffusion- weighted imaging data (Huisman, Sorensen, Hergan, Gonzalez, & Schaefer, 2003).
1.6.2 The diffusion tensor model. The diffusing water molecules probe the local cellular environment of the axonal fibres, reflecting their microstructural characteristics. Diffusion-weighted imaging, by being sensitive to characteristics of the cellular barriers to diffusion, identifies the dispersion patterns of water molecules and, thus, acts as a unique probe of white matter structure (Alexander et al., 2010; Beaulieu, 2002) that can provide useful information about abnormalities following brain injury. Whilst some of this information is apparent from the diffusion-weighted MR images as they are, post-processing of the data is necessary in order to extract more detailed information and carry out statistical analysis (Parker, 2004).
The diffusion signal is often visualised in the form of a 3D vector field, known as the diffusion tensor. The tensor model is applied at each imaging voxel to determine the three mutually perpendicular eigenvalues (λ1, λ2, and λ3) that represent the magnitude of the diffusivity of water molecules in each of the three principal directions. The associated eigenvectors (V1, V2, and V3), one per each of the principal directions, can then be derived, as well as a number of additional DTI metrics that characterise the various properties of water diffusion within the axons (Basser and Pierpaoli, 1998). In this way, the tensor model can be used to infer from the imaging data the kind of tissue microstructure that appears to have given rise to the observed pattern of diffusion.
Chapter 2 has more detail on DTI as a technique and its application in the current research programme.
1.6.3 The biological basis of diffusion. Unlike in pure water where diffusion is based on a random unhindered pattern and the rate of diffusion is similar in all possible directions (i.e.
diffusion is isotropic), in healthy brain tissue cellular structures restrict the displacement of water molecules. The microarchitecture of brain tissue that includes membranes and cell walls thus determines the molecular displacement pattern (Alexander et al., 2010). In brain white matter water molecules diffuse more freely along the principal direction of the white matter tracts than they do perpendicular to the tracts. This preference has been labelled diffusion anisotropy, numerically approximated by a scalar measure called fractional anisotropy (FA), often used to index the degree of structural integrity of white matter. Normally, FA values range between 0 and 1.0, representing the normalised variance between the three diffusivity eigenvalues (as shown in section 2.8.4, p. 80). Greater anisotropy, indicated by a higher FA value is believed to reflect more coherent tissue structure (Arfanakis et al., 2002).
Regional variability in white matter structure and FA is likely to be based on differences in fibre myelination, fibre diameter, and fibre directionality (Bigler & Bazarian, 2010). In damaged white matter, where diffusion is more isotropic, diffusivity is increased perpendicular to the principal direction of the axons. This tendency can be approximated by mean diffusivity (MD), the average diffusivity in all directions, based on the three eigenvalues. Two further DTI metrics that are increasingly used in research are axial diffusivity (Dax; diffusivity parallel to the main axis) and radial diffusivity (Drad; diffusivity perpendicular to the main axis). Previous studies have suggested that anisotropy of diffusion in white matter is likely to primarily depend on intact axonal membranes, whilst changes in radial diffusivity potentially index structural changes in the myelin layer of axons that may act to modulate diffusion anisotropy (see Beaulieu, 2002, 2010). Mouse models of axonal damage and demyelination in particular have implicated axial and radial diffusivity as potential biomarkers of axonal and myelin loss, respectively (Budde et al., 2008; Budde, Xie, Cross, & Song, 2009; Song et al., 2002; Song et al., 2003; Song et al., 2005).
1.6.4 Limits of the tensor model. The diffusion tensor is a helpful way to describe the Gaussian distribution of water molecule displacements in each imaging voxel. Metrics derived from DTI, particularly FA and MD, have become popular as indices of white matter ‘integrity’ and damage, respectively, and have to date been shown in several studies to have relevance in terms of behaviour and outcome following neuronal injury or illness. However, because changes in these markers do not relate directly to abnormalities in specific features of tissue
microstructure and can be influenced by a variety of properties of the cellular environment, their biological determinants cannot be inferred directly from the diffusion tensor (Alexander et al., 2010).
Moreover, the size of DTI imaging voxels is at a scale of cubic millimetres (e.g. 1.75 x 1.75 x 2 mm), whilst the average size of an axon of the central nervous system is approximately 1µm in diameter. It follows that each voxel contains multiple axonal fibres, which in some cases may have distinct principal orientations. This could, through affecting the DTI metrics extracted from a given voxel, distort the FA value so that it appears excessively low (Tuch, Reese, Wiegell, & Wedeen, 2003). This issue is more relevant in those white matter regions that are characterised by fibre crossings than in voxels contained within white matter tracts that have a clear principal orientation, such as those containing the interhemispheric fibres of the corpus callosum (Jbabdi, Behrens, & Smith, 2010; see Chapter 4 for further description and discussion). The signal decay in diffusion MRI is also more complicated than can be acquired and analysed using DTI. Although restricted diffusion is the most apparent type of diffusion in neuronal tissue, mostly due to the directionally restricted water molecule displacement within axons, the entire signal decay also includes free and hindered diffusion. Alternative diffusion- weighted image acquisition and analysis frameworks include q-ball imaging (Tuch et al., 2003; Tuch, 2004) and the composite hindered and restricted model of diffusion (CHARMED; Assaf, Freidlin, Rohde, & Basser, 2004). Depending on the particular research question, these techniques can be applied, for example, to extract information specific to intra- and extra-axonal compartments and to model complex patterns of fibre orientation within an imaging voxel, or to estimate axonal diameter (Assaf & Cohen, 2009).
It is also important to recognise that the relationships between DTI metrics and underlying injury mechanisms can be complex and vary with time after injury. For example, an inflammatory response such as axonal swelling or cytotoxic oedema can lead to elevated FA in the early stages following a TBI (Bazarian et al., 2007; Mayer et al., 2010). Thus, highly anisotropic white matter does not necessarily imply the absence of neuropathology. Decreased FA, likely to reflect axonal damage, has been observed following both mild and moderate/severe TBI in various white matter tracts in a number of recent studies (e.g. Kraus et al., 2007; Sidaros et al., 2008; see Niogi & Mukherjee, 2010, for a review). There is a need for more longitudinal research to elucidate how changes in DTI indices of white matter structure
may reflect different neural responses to TBI at different stages post-injury, as well as cross- sectional studies to investigate the relationships between DTI findings and specific clinical/cognitive sequelae. Here, the main interest is in exploring how DTI-identified white matter abnormalities may relate to cognitive outcome in the post-acute/chronic phase following TBI.